Ultimate Google Analytics 4 (GA4) course + 50 practical tips

1. Introduction: Hi, guys. My name is problematic, and I'm a Web analyst in Public Analytics Lecter. The goal of this course is to teach you how to use Google Analytics as a tool. But what is more important, how to leverage the data to drive your business at first place we won't cover topics is how to create an account or set up a basic measurement, because you can find thousands of tutorials about it. And it's not the place where the money and value is generated. After completing the course, he should be able to identify what should be quickly improved on the Web site from a user experience point of view. Very fire agencies, online media spends you decide, sir, data check and evaluate campaigns. And in general, you should have very decent dated really ability. The course is designed to help you answer three questions. Why, how and when something happened. And it's for everyone who wants to make the right data decisions. We won't cover all of Google Analytics report because there's just too many of them, and plenty of them have very small potential impact. I personally use probably only 25 to 30% of reports. Instead of that, we'll focus on analytics principles, and the reports that can make the huge impact when we will interpret the data correctly during the course will go through 50 practical tips. They are based on my experience from both working in agency and now as an in house Web analytics lead. Some of them come from mistakes I made when I was starting with analytics, so we don't have to repeat them. I'm sure many of tips will be applicable to your data on business, too. The first couple of lessons are slightly more theoretical, explaining how the data is collected and describing the basic terms you need to know. But then we'll spend most of the time in the interface. And one more thing. Always check the lessons, description and resources because most of them will contain an extra material for studying or short please. So what's master? This powerful too 2. Basic data description: Google Analytics is nothing but one huge table. I'm sure everyone has seen at least one in your life. GA itself is basically a visual layer upon that huge table, and every time we change something in report configuration, it just plots us a different dataset. Just because it's table, the very basic data differentiation is on two types. Dimensions and metrics. For easier understanding, dimensions are te data characteristics and metrics are numeric data characteristics. Or to simplify to atoms, dimensions are letters, and metrics are numbers. Let's have the first look into interface and see the dimension metric relation. Okay. Okay, so here we are for the first time in the Google Analytics interface. Don't worry right now about not having the access to the tool itself because we will show it later during the course. The only thing we're about to show right now is the relation between dimension and metric. So I will open a random report, in this case, the one with which we will also operate quite often during the course, and it's called the Acquisition O. So I'm clicking on the tab of reports I'm opening one from the life cycle series of the reports and particularly about the traffic acquisition. I'm clicking on this one. Let me minimize this left tab to see a bit larger screen so we can see it properly. Another thing I will show you right now is that I'm about to change the dimension to the source medium. This is something we will be more familiar with probably, and here we are. As set, Dimensions are letters and metrics are numbers. This is exactly what we see. It's nothing else like any other table you probably see in your life before, for example, also in the Excel file, and here we are. In this particular case, our dimension is session source medium, and its values are, for example, Google CPC, direct non, Google Play organic, and so on and so forth. And the metrics or the numbers mapped to that is, for example, sessions, engage sessions, engagement rate, and so on and so forth. We'll, of course, the details and the explanation of what these metrics actually mean in reality. But for now, we're only showing the very basic data characteristics, which is on the dimensions, in this case, session source medium, and metrics, which are in this report, sessions, engage sessions, and so on and so forth. Yeah, this is it. Now we know how it works between the dimension and metric. 3. How the measurement is done: Google Analytics measurement is based on cookies. And don't worry, not on the ones you buy in the store. In tach terminology, cookies are small files stored in your browser. I stress the word browser. They have limited size and they don't store any information about the pages you viewed before or how many times you've already visited a website. For purposes of Google Analytics, cookie is just identifier of user or to be more precise browser. So yes, the number of users we all see in our accounts is in fact the number of browsers that visited our website. Let's have a close look on how the GA cookie looks like. This is how Google Analytics cookie looks like. Pretty simple. We have a couple of numbers in here separated by dots. Let's see in more detail what it actually means. GA dot one dot two is every time the same and it's the version of Google Analytics measurement code. It's every time the same, so we don't have to worry about it that much. Then we have two more number sequences. The first one is actually nothing but the random number. It's generated by Jala Script measurement code, which you all have on your website. Then the second one is the first time stamp. This is actually the time when you for the first time visited a particular website. And these two numbers together make something we call client ID, which is user unique identification, and as we know, the user is nothing but a browser. This is actually the client ID that measurement code takes every time you visit a website and is able to recognize you as the same user who has already been on the website. We already know the GA is one huge table. To make it work correctly, it's of course necessary to have the JavaScript snippet on every page of your website. Once it's loaded, together with the rest of your websites code. In very simplified version, it only sends information that some users, who we know is a browser has view certain page at certain time. Time is represented by timestamp, which is nothing but time format. This is it. There's of course some more information signed, but for understanding of how it works, it's okay to remember just this. Try to imagine a simple scenario. You typed a URL address into your browser and your browser is now downloading and parsing an HTML code. Part of this code is also a Google Analytics code, which ensures a measurement and once it's loaded, it asks your browser a question. Is there a Google Analytics cookie? If yes, It takes a client ID from it and knows it's a returning visitor. If not, it's a signal for a GA code that it has to create a cookie and take a client ID from it. Either way, we now have a Google Analytics cookie, so we can send all the information on Google Analytics servers. This is exactly how Google Analytics server call looks like. We are calling a GA server. We are sending there an information that we're reviewing a page. We want to send it into this particular Google Analytics account, UA Hyphen one, two, 3456 Hyphen one. We're sending their information that this is our client ID, which we took from GA Cookie, and we're sending there an information that we just view this particular URL called Contact page. Once all this information hit Google Analytics server, they are parsed there and stored in a database, which we know is a huge table. After that, we are able to see all the beatiferal charts and tables in the interface. 4. User identification: All right. Let's spend some time with proper understanding of the very basic metrics we will be operating within the rest of the course. Plus their understanding is crucial for any interpretation of the data. So let me start with something that starts with the real world, and let's start calling something as users, which, let's say, we will try to resemble something very close to the human. This is something where I would start. In the world of digital analytics, the highest entity we are measuring and we will operate with is called users. So this is something that is something that closes to something like devices. We'll explain it later what that means. But user is the highest entity metric we will be operating with. Then one user can have multiple sessions, of course, during his interaction with your website. And during every session, there are multiple views or events occurring. So this is the very basic metrics we'll be operating with. And it's important to understand it really in this way. So we're not measuring users as a human, but something that is defined as more closer to device. And now to further understand what that is, we need to understand how Google Analytics is identifying the metric user. Let me go to next slide, and there are four identification methods that GA four is using to identify one particular user. Historically, it used to be much more simpler than it's now, but due to various legislative changes and more and more strict content method collecting. It's getting more and more complicated also when it comes to identifying the user. So historically, it used to be only something that we have here called as line number three or item number three, which is device ID. But let's start from the very beginning. So the first identification method that Google is using, and we can, and I stress we can use it is called user ID. What that is? It's the ability that we can say to Google Analytics measurement code, Hey, this is one user ID. Please collect it and use it as the identifier. In reality, it means that we need to do the implementation in a way that with every hit we sent to Google Anytix, we also sent that user ID, and we need to let Google Analytics know that, hey, this is the identification of the user. So this is one identification method. Then there's a second one, which is called Google signals. The way it works, it's the optional feature in GA four, and it works in a way that if you as somebody who owns the Google account, give the permission to Go to Google to use this data, Google can then create its own user ID, which then also can be used for identification of user in the GA four. So this is the second method. The third one is something called device ID. In majority of the cases, this will be the identifier stored in the co okie, which we already know what it is and how it works. This is where the identification of the user can also come from. Then there's the fourth one, which is called modeling. This is, I would say most advanced one. This works for the users who are non consented, which means they didn't give the permission or the consent to be measured, but Google still can anonymously track this user. These are the four ways how Google can and is identifying the user. Pretty complicated. And now, how it works in the interface. As there are four methods of identification, we have also the ability within the GA Admin section, what kind of the identification method we want to use. So let me show you where it is. I will go to Google Anytix. Right now, I am in my Google Eytics account, and to show you how it works, then in the interface, I need to go to the Admin section, scroll down a bit and go to something that is called reporting identity. If I click there now, I have there two options, how I can measure the data. Maybe even more. Yes, it's also the third one here, which is called the device base. Based on the identification methods available, which are four of them, we can select, how do we want to identify our users. Based on what we select here, it will affect the volume of users, we will then measure in the Google Analytics. The largest possible option is so called blended, which combines all of the fementioned identification method we chose. Both like user ID, device ID, and then the model data. This will collect by far the most users or something that is the closest number to the real number of users. If for any reason, you only want to collect the the data which you really collected in a way that you have the proper device ID or the user ID. So so to say, either data from the cookies or the one or the IDs that you are sending to Google Ayt, this is the option for you. So you only want to see the data for the users that you that truly gave you the content and nothing is modeled. Or you can select the third option, which is devised base, and then you are only collecting the data which are coming from the user's cookies from the devices, or in case you're measuring the app, the alternative for the information that is stored in the cookies. So these are then the options that we can have. I'm using the blended one because I want to see the largest possible number of users I'm measuring. But it's really up to you. I do recommend to use this one, since this is the closest what we can have in terms of having the largest volume of users we measure. What is important to know that if you decide to change, for example, from blended to observed or to devised base, you're not changing the underlying data, meaning that you're not changing anything in that large table, it only affects the reporting. If you decide to change it from blend it to observed and then vice versa, feel free to do that. Just keep that in mind that the calculation will every time change for the total volume of users that you are measuring. So yeah, this is, these are the identification method, and then the way we can select how many users are we going to measure based on the selected method. So, a little bit of theory, but to me, important and necessary one for the further lessons. 5. Session definition: Okay. Another of the very basic metrics we will be operating with is called sessions. Even though Google has mentioned that multiple times that with Google Analytics four, it's switching more towards the relationship between user and events, whereas Universal Analytics was much more based and gravitated around the sessions. It doesn't mean that session itself disappeared from Google Analytics. That's not true. There are still exist and still used and will be used. And what I want to explain you right now, what is the currently relationship between the sessions and engagement? So this is something that also Google is constantly repeating that it's about how to understand the user engagement on our website and within the mobile apps. So first of all, what is important to understand that the basic differentiation that Google Analytics four works with is between the sessions and engaged sessions. What is the session, how it expires, we'll say in a couple of moments, but right now we want to understand what is engaged session or how Google defines that. It's actually pretty simple. The way Google defines the engaged session is based on the three conditions. If any of them is met, then Google is saying, hey, this session was engaged one, or this is something where I think the user interacted enough to mark the session as engaged one. So first of all, the session must last more than 10 seconds. If that happens, it automatically marks the session as engaged one. The second one, even though it didn't last for 10 seconds. But it includes conversion, means that you are in a business where conversion can occur pretty quickly. For example, I don't know, it's enough that the user just starts to play the video or click on the button, which of course can happen within 10 seconds and you mark this event as conversion, then automatically even the session lasts less than 10 seconds, it's marked as engaged session. And the third option is that the session doesn't need to last 10 seconds to be marked as engaged. I user views at least two pages on your website or sees two screens within the app. So this is the definition of the engaged session. We will work with this pretty often. And for those of you who are familiar with what was bound straight, this has also a very strong connection to that. And right now, I want to show you one thing, which is the possibility within the GA interface to change that time window of 10 seconds to prolong it if for any reason, it makes sense to you. That you want to say, 10 seconds is just not enough for me. I want to prolong it to let's say like 30 or even longer, or in other case, you want to shorten this to let's say 5 seconds. Depends on you. I don't want to provide you any guidance since I don't know what your websites are about, but there is an option. So let me show you where to find it now. 6. Session adjustment: In it now. Let's jump into the GA interface, and we need to go to the admin section. Here we are. Then going into property settings into the data streams where I already am. I need to click here on that particular data stream which I'm using for the purposes of this course. Here I am. I need to scroll down a bit to modify that, and now I need to click on the configured tax settings, which is here. Again, wait for a second. Now, scroll down. I I show more, I will scroll down, scroll down, scroll down. I I right now see here adjust session timeout. This is the place where we can adjust that. I'm clicking on this one. And here we are. We have here two options. Let me first go to the second one, which is exactly about what we said, and it's the adjusting the timer after which we want Google to count the session as the engaged one. Here we are. We have the options there up until 60 seconds. It's entirely up to us, which one we select. This is about saying which session was engaged. Then there's another session, and let's stay here for a second to explain what it means. Historically, in Universal analytics, there were a couple of condition when a session could expire. Meaning, for example, when a traffic source changed or the session was restarted just after midnight if a user was browsing a website around like a midnight, the first hit after the midnight automatically started a new session. There's no such a case within the GA four and the only way how a session can expire, and I stress, the only way how a session can expire if the user is inactive for more than 30 minutes. This is the default set up here. So no matter the traffic, the traffic source change or like the midnight happening, it still continues as a new session. So 30 minutes is something that is default. And similarly, as for adjusting the time for engaged session, we can as well adjust the whole session time out from 30 minutes to 55. I would say, this is something that should be like reasonably enough. For us to consider to have the session expired and the new one started. Feel free to switch it to anything that fits more to your business and to your needs. I'm just showing you here the options. The reason why we were speaking right now about the session and engage sessions and skipping the detailed explanation of what user and active user is is purely to understand what the engage session is. Right now we know it. We are going to explain the difference between engagement and bound rate, and then we go back to the explanation of what user versus active user is. 7. Bounce rate and engagement rate: Another aspect and the metric, which is fairly often used when it comes to evaluating any digital activities as now it's called engagement, as we already explained in the previous videos. And I want to explain you, what is the connection between the engagement rate, which is newly used and formally used bound rate. Actually, it's not that much of a difference, even though understanding some of the basics, it's crucial. So let's first understand how something that is called engagement rate is calculated. It's something that is fairly used in many of the reports. So let's just briefly look on the calculation. It's actually fairly simple. So let's assume the following scenario. We have in total 100 sessions, out of which 45 were marked as engaged session. We already know what's the definition of engaged session. So if we will now calculate the engagement rate, it's simple. It's engaged session divided by all sessions, which in this case is 45 divided 100, which is 45%. These numbers basically tells me how many of my sessions or what the volume of the sessions which really engaged with my website or mobile app. Of course, depends on how we adjust them the criteria for the sessions to be marked as engaged. But this is what it tells us. Historically, for many years, there was a different metric used for explaining pretty similar information. It was called bounce rate. Or in other words, it was the volume of the sessions which bounced without performing any other interaction. What is important to know that the criteria weren't exactly the same, because for the session to be counted as bounced, the criteria, there was only one criterion, which meant that the user viewed on one page viewed only one page, yes. So the fundamental difference is that the criteria is now changed in a way that user, if he or she spends at least 10 seconds, it's automatically engaged session. So this is the difference from the former calculation. Anyhow, the metric bound rate still exists in Google and Lytex, we will show it that it's still there. And what is important to know that it counts with the exactly same numbers. So engagement rate is actually the inverted version of the bound rate, which is one minus bound rate. So in our specific case, which we showed here, if the engagement rate is 45%, then the bound rate is one -45%, which would be 55%. So this is pretty much how the calculation works. And now let me switch to the GA interface to show you that actually still both metrics exists there. I prepared here a custom report. No worries about right now not seeing that or not knowing how I got there. This is something that we're covering during the course. But I'm going to show you that the formula that I showed you actually actually works. So I created a simple report of the traffic sources here, which is the dimension here. Direct non, Google Organic and Google CPC, and so on, are the values of it, and I edit their three metrics. Sessions, bound rate and engagement rate. So as you can see, if we sum up the bound rate and engagement rate, we'll get exactly 100% for every single line. So it's just a matter of interpretation of this metric, whereas historically, we tried to have as low as possible bound rate. And now we're trying to have as high as possible engagement rate. So it's more like switching towards positive, which which normally works in the world. It works in a way that we want to increase the values of the metrics, not to decrease them. But anyway, it's very much the same information that it provides us. One is just the inverted value of another, but still both of them exist. Keep in mind that the criteria for marking something as either bounced session or now engaged session has changed, and the fundamental difference is that it's just enough to spend 10 seconds on the website to be marked as engage session, whereas historically the time didn't matter. So this is important to know. 8. Active users and users: B. So we already know how Google Analytics four identifies user, what is session definition, and what is engagement and engage session. We're getting to one of the last fundamental metrics we will use during the course, which is the difference between active users and users. So this is what Google is right now using in quite a couple of standard reports. So it's important to know the difference. In majority of the cases where possible, Google is trying to use the active users, which, for example, you can see if you just open the basic acquisition report and go to the overview. And for example, if I will click to user acquisition and scroll down a bit, I see here a metric total users. What is important to know, what is the difference? There's always more total users than the active users. And the way that where the difference is coming is that the active users are the one who had at least one engaged session. So and I will repeat it again. Active users are the ones who had at least one engaged session. We already know what engaged session is. So this is then also implication to the difference of the active users and users. And just to show you that there's always more total users than the active users, I created a simple Custom report. No worries again right now, if you don't know how I got there because this is something we'll explain later in the course. So I have here a simple dimension, which is month, and I have two metrics, total users and active users. As you can see, in total, that there was about 22,000 total users, whereas about 18,000 of them were active ones. So now we know what's the difference between those two. Active users are the one who had at least one engaged session during selected period of time, whereas total users are all of the users that Google Analytics F was capable to identify. Yeah, this is the difference between active users and total users. 9. Time measurement: But So as we're proceeding in understanding the fundamentals of how Google Analytics four measures and collects the data, we're getting to another thing important to understand, and it sticks to engagement or something that Google is right now revolving the way it presents the data, and it's about the time measurement, or then in Google Analytics, which is found as like the average engagement time spent either on page or during the visit or per user. So a little bit of history for better understanding how it used to be measured, I mean, the time and how it's measured now. Let's have a look on how it used to work on this particular example when we have one user who viewed four pages during one session. And here it is. At exactly 9:00, he viewed page number one, 5 minutes after that page number two, 9:10 page number three, 20 minutes past nine, page number four, then he spent there another 5 minutes and left the page. And the session ended. The way the measurement used to work was that it was sending the time stamp, which is the representation of the time only with every page view. So that was the fundamental of the measurement. What would that do in reality is something like this? It would measure the time spent on the page one as a 5 minutes because it would subtract the page the timestamp of viewing the page number two, which was 9:05, and subtract the timestamp collected when viewing page one. So this is so it's 9:05 minus exactly nine, which is 5 minutes. Similarly, for the page number two. Page number three was loaded at 9:10 minus 9:05, so again, 5 minutes. And then for the page three, which is the last one with measured time. I'm stressing this out, where the time spent on the page number three would be 9:20 minus 9:10, which is exactly 10 minutes. As I said, the only moment when the timestamp was sent was when the page was loaded, which means that effectively, even the user spent 5 minutes on the last page, since there's no other timestamp, the time measured on the page four in Google Analytic would be 0 minutes. So this is how the reality used to work. The whole time of the session would be five plus five plus five, which is 10 minutes. So you already probably see how much flawed or incorrect the metric of measuring the time was and how far from reality it was. So let me illustrate the problem of that. Let's assume that the first scenario we're showing assumed that the user spends the whole time on the one domain, right? Which is not reality. We know how we all behave. We have multiple tabs open, we have notifications, so we constantly changing between one tab to another. So let's assume that the scenario that happened in reality was something like this. So it wasn't like straightforward flow through one domain. But the user was switching from one to another. Let's assume it was like this. At 9:00, opening the page from my domain, then just 3 minutes after that going to Google, searching something, doing something else, and then coming back actually to the page number two, five plus nine. Then again, after 2 minutes going to read some blog post and coming back at 9:10. And again, just a couple of minutes after that going, for example, to Amazon, doing something there and then coming to the back to the original domain. So this is probably something that is happening quite often and is reality. But the way we measure time is very far from that, right? Because it was considering only actually viewing or the moment of viewing and loading the page and measuring the time. So that was far from reality, right? So this is why time as a metric was basically it was distracting the people from understanding what actually is happening. And as you said, Google is turning much more towards the engagement. Also, the way the time measurement is currently done was completely revamped and redone from the very beginning, and it's much much closer to reality. And this is something we're going to show on this slide. So, let's assume exactly the same scenario. Also already I'm describing that with that flow that user is very likely going from one domain to another, switching in multiple times until the very last leaving the page. What is important to remember right now is that what has changed with Google Analytics four is something that we will repeat a couple of times during the course that it's currently event based measurement. What it means also for the time measurement is That whereas in the universal analytics, the only moment where the timestamp was sent was at the moment of viewing a page, whereas now it's sent with every event, meaning like every time you measure something and there's like a bunch of events that Google is sending automatically right now to your Google Analytics account such as scrolling on the page, filling in, for example, the form, downloading the PDF, and so on and so forth. There's much more events with which Google Dan can calculate time spent on the website. And what is important to also know that there is one specific event, which is a game changer when it comes to time measurement, and it's so called unload event, which is sent to your Google Ayt account every time the tab is inactive. So it's either inactive, and user is no longer there or the page was closed by pressing the x button on the tab. So this is something that is much closer to reality. So if I would go then to explain how much time would be actually measured right now in Google and Lytic four, it would be something like this. I viewed or user viewed the page number one at 9:00. And then 3 minutes after that, he left to completely different domain, which in this case is Google Com. The moment of user is leaving the active tab or meaning that this tab is inactive in the browser, Google is automatically also sending the event, Hey, this page is no no more active. So at 9:03, there's event sent to Google Analytics that with the particular time that this page is no longer active. So for the page one or the time spent on the page one or so called engaged time would be measured as 3 minutes. Then similarly for the rest of the pages, Every time user is leaving the original domain and going somewhere else, the time is sent to Google Analytic servers and then using for processing the total engagement time. So as you can see, the current measurement is much closer to reality plus, as we said, historically, the time spent on the last page was always zero because of not having another page view. But right now, as I said, as the unload event, meaning closing the tab is also sent, is also very precise measurement of how much time user spent on the last page. If we had previous or the same example, but the different way of time measurement. In the previous Google Analytics version, the whole time spent would be collected as 20 minutes, which is not reality, whereas here, it would be like three plus two, which is five plus five, ten plus another five, 15 minutes, but this is really effective time when user engaged with the content of your website. So yeah, this is it. Hopefully, it's pretty clear. From now on, having the time as a metric is quite a valuable thing in comparison how it used to be. What is important to remember that the time stamp, meaning effectively sending what time is actually now, is being not only being sent with the moment of viewing a page, but we like every event that is happening on your website, including the events which Google sends automatically such as scrolling or like unload event, meaning closing the tap, which represents the tap of not being active. Yeah, this is how the time is measured right now. 10. Basic interface elements: B. Thanks. That was a theory. It's time to finally get to the interface and see what everything is in there. So I'm not going to prolong it, and let's go straight into it. All right. Let's do the Google Analytics for Interface walk through. I'm going to show you how to use the interface on the Google Analytics Mandie demo account, to which all of you have linked also in the resources of this lesson, and as well, you can find it the same way as I do. So all you just need to do is to Google for GA merchandise store demo account. And you should see this link. As I said, you have it as a resources of this lesson. If you can scroll down a bit on this website, just click here, and you should be automatically redirected to the GA four demo account. Here we are. This is how it looks like. The first thing I want to stress before we'll go to the report itself is that it's not that far in time ago that something that was used to be called for 20 something years as conversion in the digital analytics industry was recently renamed to key events. So please keep that in mind. Since in the plenty of the following videos, I'm using the term conversion and conversion rate and from now on, in all of the GA four parts where it used to be So, something that was called conversion is right now called key event. Please bear in mind, basically try to swept the word conversion for the key event and you will know what I'm trying to show you. Here we are. I will right now dismiss that part just to show you how it looks. On the first side, it looks pretty similar as what we were used to do from the universal analytics, but as well slightly different. This is what we have as the first thing. You may see that if you just hover over the left pan, it will appear and show you a bit more line. This is something that we will be interacting quite frequently with. And this is how it is. Anytime you click on the home, you will see sort of like the managerial dashboard in front of you showing you by what Google thinks, the most important metric about your customers and users' behavior on your website. So this is how it looks like. It's sort of like the set of the reports that are in more detail available in the following tabs available here. But this is how it is. It shows you that during the past period, which in this case is last seven days, it can be changed to anything here. You had or Google had 17 k users who did 20 k events and so on and so forth, a lot of the other metrics. Please bear in mind that this is just showing the walk through of the interface itself and the very detailed usage of the rest of the interface is in the upcoming videos. So this is what is here. It's not much we can do here on this first home screen because majority of the things or the interaction clicks, which are here, so for example, here, view, e commerce purchases are just redirecting us to one of the reports available further on. So this is what's here. Let me go and show you the other parts of the interface. If I will right now click on the reports, I will see the first cut of the more detailed data. Let me show you the first thing I would like to explain to you, which I believe you will be using very much and very often, which is normal, and it's how to use the date range or how to change the date range since analytics is mostly about comparing. So the element is on the very right side. It's pretty intuitive. If you just click on it, and you can easily select the period on which you would like to look at. Let's say from the August 4, till 24 August. If I just then click apply, I will wait for a second or two and all of the data. Well change showing me just the numbers for the particularly selected period of time. This is how to use the date range. Pretty simple one. Important thing, of course, just like looking on the selected period of data doesn't tell us much when thinking of data. This applies to any kind of the data we will be looking at and examining. What is important to do is to always compare it with something. How to do that. It's pretty hidden, so to say, on the first side, because if you want to compare the originally selected date, you need to scroll down a bit and here we have the check box where you need to first check the compare, and then exactly as in the previous case, you can select the compared period. So there are quite a few of them which are automatically comparing the original period with the original period. As you can see is like the preceding period, same period last year, preceding period, or the custom one, which I highly recommend to do. I will explain you in a second why, but just to show you that the time time range comparison works. If I will click right now on the apply and wait for a second, now we can see that there's also dotted line. It appeared here under the original one. So hopefully, this is pretty clear. Also, there's a change in the metrics where we can see whether particular metric, for example, in this case, active users has changed by plus 6.3%. Right now it tells the way or this way of showing the data, starts us to telling the story, what has changed in positive or a negative way. So this is how to use the date comparison. There's one thing I want to stress out. Let me first minimize this left tab, so we see a bit more of the data, and it's the way how we compare the data. It might seem like a super straightforward thing, but I still see it quite often that people just mismatch and the interpretation because they just don't compare the data the right way. What I mean? What is quite often happening is that people choose pretty short period of time. Let's say, I would like to compare the data from Monday till Thursday. This is my sorry, I need to cancel the comparison. So Let's assume I would like to examine the data from Monday till Thursday, let's say. I will click apply, and I will see just like four data 0.0, which is okay and normal. What people often do is that if they start to compare the data, they select the preceding period, which does that preceding period, this one. They select apply. Of course, the data will appear, but the typical fail here is that we're not comparing the same days. Please bear in mind, anytime you do the comparison, try to compare it in a way that you compare as it was available here like preceding period, which is comparing the same days within the week. Because I can imagine in a lot of cases, there can be a seasonality even during the weekend, especially if you're in the e commerce area. I'm 100% sure that Monday and Thursday are from the business perspective, much stronger dates. Then, for example, if I would compare it like this with Friday, ATL till Sunday, right? So just like using the common sense trying to compare apples with Apples. So please bear in mind to always compare Apples to Apples and not then to mismatch any kind of the data that will be okay in terms of like, I'm comparing four days with four days, but these four days are not the same in both cases. So that was the first element of the GA for interface, which is about knowing what everything is here and how to work with the date ranges. 11. Real time report: The second part of the basic interface features, I want to show you is something that is quite often used, even though there's not that much of the analytics we can do. It's called the real time report. So I'm going again, switching from the home tab to the reports. I will wait for a second. And as I see here, if I scroll up to the very top of that, I see there the real time report. So let me click on that. Now I will minimize it, so we see the larger part of the report. As you probably know and it's hopefully self explanatory enough, we're watching on what is happening on our website in the real time, which means currently right now. Hopefully, all of you are familiar with the world map. We can see which part of the world is currently the largest volume of the traffic coming. You can hang out with it, just drag and draw and play with the map as much as you want to. As we can see that probably majority of the traffic is currently coming from the India, and we speak particularly about the GA four account for the Google merchandise store, then probably from the United States, something from Dubai and Istanbul. In total, it means that there's 50 people in the past 30 minutes, sorry users, people. We are not that close when it comes to measurement to the people and nine active users in the last 5 minutes. You also see the timeline starting from the current moment and going 30 minutes in past, and you can zoom in and zoom out as much as you want on the map. How and when to use this report. It actually makes sense or there are only a few scenarios where it actually makes sense. Let me give you two examples. First of them, let's assume that you launch your website in the new country and you want to see whether the measurement works just fine. So this is exactly the place just after the launch where you can see if you're observing the new traffic coming from the new country. This is one of the scenarios I sometimes use when we launch something new, and then there's a second one. Which is more detailed. And when I scroll down a bit, there's again, the set of the reports showing the data in the real time and the one that has the like, I would say business wise reasonable use case is the one called here, which is about the key events or the events in general. What it means actually that the As we said, GA four is mainly event based measurement. And for example, if you start to measure something new, let's assume some new event which is very specific and you just set it up, and you want to test whether the data is flowing to the Google Analytic servers, and whether the action you want to measure is actually being collected. This is exactly the place where to look for it. So if something new is appeared or new event, you can just briefly check whether the new event appeared, for example here, among the events which are already being measured. So If you click on the specific one, you can see more details about the events. We will cover the event parameters later in the course. But I'm sharing with you the example where to check if something new, what you want to measure is actually measured. So this is the real time report as set. You can't do much of the analytics. It's more like the debugging or checking whether if you launch something new, it's already being measured. So this is the real time overview. 12. Time range granularity: All right. Another basic feature we're going to show is how to use the time series granularity. Let me explain to you what that is. I need to go to the reports which is showing the data in a way of a time series. So one of the great example is when I will go to the life cycle reports and let's say to traffic equisition. So here we are, again, minimizing the left step, and what I mean is that simple drop down menu, where we can choose the time series granularity. It sets to day, week, or month. I believe it's also quite self explanatory, but yet many people forget about having it there. What it actually does or what it actually means. Once you plot a certain period of time, let's assume I here have just four days, it makes sense to have the daily granularity. But what happens if I, for example, extend that to let's say last 12 months, and I will plot the data. If I will leave there the daily granularity, you see it's quite difficult to read the data, it makes sense to change the granularity. If I will do change to the week, Of course, it's becoming a bit more readable, but still quite a bit of up and down, not that easy to read. Actually, like the monthly granularity when looking on the whole year data is something that is that is, of course reasonable. This is the small feature. It was launched just recently, even though GA four has been for almost four years on the market. But this is that is pretty new. This is one of the features I want to stress you to use. Use it wisely, of course. But I believe this is pretty straightforward what it does. The second thing I wanted to show, which was also launched just recently is the ability to see the total data. As you can see the way the GA four reports works by default, It means that if you open any report, which, for example, as I have now, this is the traffic acquisition showing us the most popular traffic sources. It automatically pre selects the first five lines and plot them in a chart. To me, that's not that good when it comes to analyzing the data because the first thing I want to see is, what are the totals? Thank to Google Almighty Engineers, we finally have this feature. All we just need to do is we just uncheck the breakdown of the particular lines of the traffic, and we click on the plot rows. If we wait for a second oil, we only see the totals so we can like much easier digest the data we have. In case we just want to compare some some traffic sources next to each other. Of course, we can select only, let's say, two of them, the direct and end organic search, which I'm going to do. If I scroll down, I see both totals, and I as well see the detailed breakdown. Of course, there's possibility to hide the totals and see only the selected two of them, so you can compare if it makes sense, just the two selected lines for the particular metric, which in this case, are sessions. Yeah. That was another element of the basic ones when it comes to how to use the interface. 13. Primary and secondary dimension: And let's continue with showing another element of how to use the GA four interface. So we are still staying in the traffic acquisition report, and we're going to show how to work with primary and secondary dimension. So from now on, let's by design, use the wording of primary and secondary dimension. What that is? If you enter any report in the GA four interface, and I mean the default ones, which are here, you have pre selected always 11 dimension which Google considers as the most important one. It's not always the case, and it definitely won't be the case for all of us since every one of us is trying to solve a slightly different business, slightly different website and slightly different user scenarios we're trying to understand. The point is that we have the ability to select a different dimension, not only the pre selected one. So what we have right now here is channel grouping of the traffic channels that Google is using and somehow grouping into the larger buckets. So, for example, if you want to change it, we just click here on the dropdown menu. You see there like two, four, eight, eight or nine other dimensions we can use. So my most favorite one of this is called session source medium, which provides me a bit more detailed breakdown. So This is something that is called changing the dimension and all of the metrics are accordingly recalculated and changed. What I want to show you also is that not every time, having only one dimension is enough because we want to see a bit more detailed breakdown. How to do that. It's again, fairly easy. All we need to do is just to click on that plus button, and then we have the ability to use a secondary dimension. As you can see, there's plenty of them if we just do by clicking here or also the text filtering works here. So we have here session source medium, just for the sake of showing you how the secondary dimension works. I'm going to use something that is fairly easy probably to understand as a dimension, which is country. I typed country, I can see it here. And when I click here, what happens is that if we wait for a second, right now, we see every channel, every traffic channel, which is in this case used by the Dimension session source medium is right now also broken down by a country. As we can see now, the report has much more lines. Previously, there was something like 300 something. Now it's over 2000 I hope you all understand why it happened because we used the secondary dimension which helped us to break down the data to the more detailed level. This technique can be used in majority of the default reports available in the rest of the interface. This was about how to use the primary and secondary dimension. 14. What to remember when doing analytics: first things first Analytics is nothing but way of thinking. Every analysis is only as good as their hypothesis or question. You asked. Before you dive into data, the very good characteristics off analyst is being sceptic, which means they do very fine numbers from multiple point of views. Secondly, you have to perceive the data in context in under the second lessons, with the explanation of high bounce rate and contact or information page. That's exactly what I mean. Context is something I will stress couple more times your next lessons. Another one is that we don't measure the reality because we measure the browsers and we can't measure everything to G A. By this, I mean we can't measure P II Information such as name, surname, email, phone and the national identification number. This happens will receive a warning message from Google with some period of time to fix it . 15. GA4 - basic setup: Okay, let's start with the first step with Google Analytics for which is creating the account. How to do that, we first have to login to Google, to any Google Analytics account you have. And then we have to click to admin section. Right now I'm in my Google Analytics account which is calibrating that sees it. And the place where we can set up a Google Analytics property is exactly here. So we're looking on this button, creates property. So when clicking here, it's fairly easy to do. So first of all, we have to name the property, so feel free to use something reasonable. So it's then easy for you to recognize which account it is. So I will name it like this. Then the second thing we have to select as our time zone, which in my case is check. Yeah. And if your website or business is also having the e-commerce part, which means you're selling something actually on the website. Please also select a currency, which in my case would be checked crown. I don't sell anything, but just for the purposes of setup, I will select it. And then we'll have here advanced options. There's actually only one option which is creating the Universal Analytics property. But since it will be sunset it on the July 1, 2023, which is also written here. It doesn't make much sense to do so. So let's hide it back in. Click on the next step. The next step once so lower when creating GA for account is having this, this check boxes to select, actually it does not anything for you. So if you want to feel free to select what's your business size or what's your intent to use Google Analytics. But if you will skip that also nothing will happen. So I will skip that. And right now we have the last thing we have to select one creating GA for data stream, which is a platform. We have three options, web, Android app and iOS app. And since we are going to primarily focus on the website measurement, I'm also selecting a web platform. The next step is typing in the website URL address, which in my case would be www dot Bible, but it's sick. That sees that. And then the stream name. Assume I call it this course data stream. Please bear in mind that once you create a stream, it's not possible to rename it. So try to set to something reasonable so we don't have to then start over again. The last thing when creating a data stream is about enhanced measurement. This is something new. What GAL4 is bringing, which is automatically tracking a little bit more than just the Phaedrus, which was the case of Universal Analytics. As we can see here. We can select whether we want to automatically drag a couple more and more things which are scrolls, outbound links, sorry, I'll work. Outbound clicks automatically track Site Search, Video Engagement, filed file downloads. Feel free to turn it on. At least you can see what the GAL4 can automatically collect for you. And when we click to a configuration of that enhance measurement, there is a possibility to turn off. Every enhanced measurement here is, as I said, feel free to leave it there. At least you can see our G54. Collect for you automatically. A little bit more information regarding every align scrolls. Now what it does by default is that it sends the event every time user scrolls to 90% of every page. It's not the best, but at least it collects something. And don't worry, we will show in the further setup how to enhance that. Then there is an outbound, outbound click which sends the event to Google Analytics every time a user clicks on the link that redirect him outside your domain. Then there is a site search, which automatically collects the data from site search. If you have such an engine on your website, what is meant by site search? Let me show you. I will go to e.g. amazon.com, and this is what I mean by site search engine. So let me type e.g. iPhone here. And the way it's collected on the 95, maybe 99% of websites is that every time you type in some query, it's then automatically also shown in the URL address after some parameter, which in this case is shown here. As you can see, I typed iPhone. And in the URL address is after the parameter k, which means k is equal, k equals iPhone. So this is the way you, Google Analytics can collect that. So in hypothetical case, if Amazon was also creating GA for property and they wanted to have their sites are data collected. All they need to do is to check whether their perimeter is also selected here, which we can see it's not, there's a set of five parameters. Which parameters which are most used across the website. But the Amazon want is not there. So if this is also your case so that you don't see that search parameter already here. Just feel free to type in, just put comma. Okay, and that's it. Or we can also remove it, lived or just k and it would also work. So, so this is it. Then there's a video engagement where every time a user interacts with your, with your embedded YouTube video, it also sends the data about how users consume the video content. And the last thing available as advanced measurement or enhanced measurement, if you wish, is about the file download. So again, every time a user clicks on the known files such as the PDF file or some video format. It automatically sends data to Google Analytics about that. This is a little bit more information about the enhanced measurement. So I will click now, save here and create a stream and a second or two. And here we are. Right now we basically created an empty table or database, which so far does not have any data, which also is something that is telling us here. But now we are ready to start sending them there, their data. There are two options for that, and we will show that in the upcoming video. 16. GA4 - hardcoded measurement: So there are two options how to then start to measure the data. We will show both of them. So let's start with it. As we created now the data stream, which can now start collecting the data, we have to scroll down to view tech instructions. We click there and wait for a second. And there are two ways how to do that. First of them is if your website is built with one of these website builders such as Drupal, do that our monster inside. Just feel free to click there and follow the instructions. Or the second way how to do that manually. And I stress the word manually is by clicking here. And what will appear is, is that global Google tag, which is the measurement code. All you have to do right now is just to select this code, copy that, and paste it into your website on all of the pages, ideally as high as possible in the HTML code so you measure as much data as possible. So if people just copy that and paste to your website as I did for the purposes of this, of this video, I will now go to my website, to the page where I pasted that, which is my bubble bread thing that sees it's less EN. And if I will write now, show you the source code and scroll down a bit. We can see that this is the ID of that a measurement called, which is G hyphen b, d, g, B13 and so on. So going back to my source code, you can see that I copied that tracking code, which you can see is here, G hyphen BD, G23 and so on. So this is basically the moment from which you are starting to collecting the data. So this was one of the, one of the ways how to implement it. Just to show you that it works exactly from the moment. You copy that to your website. I will now go to real-time reports going here and then going to report, waiting for a second. And going to, as you can see, this user in last 30 min. And if I will, right now, just go here and refresh the website. Okay, it now should send the data to GA. So if I will go to real-time report, as you can see, I can see myself right now sending the data to Google Analytics. So you can see it's being collected exactly from the moment we based it, the code to HTML. So this was one of the ways also showing you how to check whether the measurement works. Now let's have a look to another way, which is through Google Tag Manager. 17. GA4 - GTM setup: The second option of starting measurement is using a tool called Google Tag Manager. For those of you who already are in the measurement industry for some time, you are definitely familiar with that. For those of you who are here at Google Tag Manager for the first time, It's a great tool which allows all measurement specialists to populate any measurement goals basically without any further developers needed. So this was the history and the past where we needed to send all the CO2, want it to have installed to developers and then pray that in the upcoming three months they might have been installed. This times are long gone, thanks to God. And right now we can use only Google Tag Manager. What it is. The interface looks like this, which is the tool with a lot of options what to fire, where there are basically three main instance this, it works with which our tax triggers and variables, how to translate it into the human language. Texts are basically mirror mint coat triggers are the conditions when we want to fire measurement codes. And then there are variables which are basically any detail we can collect from the website or pushed from data layer and then collect to the tax. So this is a very brief introduction to Google Tag Manager. If this is really the first time you're seeing Google Tag Manager or feel free to go through at least a bit of basic tutorials to understand it. Anyway, even if you see it for the first time, it should be fairly easy for you to install the code also through, through it. It's much more convenient for the future and for enhancing the measurement. And we will show it also on the example of the scroll tracking. So how it works very similarly as, as the case of installing the tracking code directly from Gia interface. Instead of that, we first have to have installed the Google Tag Manager code on our website. How to do that? Once it's created, the container it's created, we go to admin section and then there is a install Google Tag Manager. You can see it's very similar code to the one from the Google Analytics. So all we have to do is to copy that and paste it into all sides of your website. Ideally, as it recommended here into hat section. Just to show you that it's also the case of my website. So when I'm here, I will again show you the page source. And if I will scroll down a bit, you can see that here is the Google Tag Manager code. It's easily to recognize, but by these id, which is GTM hyphen N6 GP, z x j. So if I will go here, you can see it's exactly the same GTM and sex and GP that six a. So this is the installation of Google Tag Manager. It's already there. It's not doing anything right now. It is there and it's ready to start sending the data anywhere. I'm stressing not just due to Google Analytics, it can be used to multiple other measurements. So going back to workspace and showing how to install the measurement of Google Analytics through Google Tag Manager. All we have to do right now is to create a new tag, which we have to name. Let's use some something reasonable. It's always a good case when naming something. So I will name it as GA for configuration tag. Now I have to configure that. And as you can see, there are tons of options of what we can measure by default. As we said, that this is just the basic setup. So we will stick to that. So first of all, I want to send the basic GA for data, which is this Google Analytics GA for configuration. So by clicking on that, all I have to do right now is to then go back to Google Analytics interface here and copy that measurement ID, which I can click here. I will copy that. Then I'm going to Google Tag Manager. I will paste the year, that measurement ID. Sorry. This is the basic setup. We'll show a little bit more advanced thing with scroll depth, as we, as we said in the previous videos. So right now I have the basic setup done. I have to now select the trigger, which means the rule when I want to fire that stack, or in other words, when I want to fire the measurement. Now, there are a couple of predefined triggers or all the rules. And as I want to measure all the websites, I want to use that All Pages trigger. So I will click on that right now. I will click, Save. Wait for a second. And all I have to do right now is to submit that Google Tag Manager version. So I will do that. We'll click on the Submit. I will add some nice name, which is GAL4 course version. I will publish debt. Wait for a couple of seconds, and right now it's published so exactly as in the case of copy and pasting the code directly from the interface which we did in the previous video by going here and copying this code. We did that through Google Tag Manager. So from that moment on, we should also start collecting the data to that property. Let me just show you. I will again go to the reports, the real-time report to check if it's working. Going here. And let me just now go through a couple of websites. I'm sorry, a couple of pages I have. So I just refreshed the homepage. I will click on e.g. couple of blog posts I have here. I will do that a little bit more quickly just to show that the measurement is supposed to work. So going back to real-time report, I am seeing myself. You can see that there are four pages I just made. So again, just the example that it's working and also a way for you to check that, that the data is being collected. You can see another event game just 0 min ago, which is right now. So example that it's working as expected. You can see that there are, right now the pages I just viewed the two blog posts of mine. So there was, this was the second example of how to start a measurement this time with Google Tag Manager. And in the next video we're going to show a little bit more advanced implementation of Scroll Tracking. 18. GA4 - scroll tracking: Let's show another great thing which is possible through Google Tag Manager and is an oval. Do that only for the purposes of this, of this video, which is as promised, showing you how to implement custom or a little bit more advanced scroll tracking or measurement of Scroll Tracking. How to do that, we are begging the Google Tag Manager, and as we said, doing any measurement is much more convenient in that tool. So highly recommend this one if you don't already use it. So how to do that? As we said, there are three main instances, texts, triggers, and brambles. For this case, for implementing the advanced scroll tracking, we will first create a trigger, or in other words, rule when we want to send data to Google Analytics. So we will go there clicking on the triggers I will create on creating a new trigger or rule. I will first name it S Custom scroll tracking. I will click here exactly as in the case of tags, there are a lot of predefined triggers or rules when to fire or something. One of them is also a scroll depth. This is automatic. In history, we had to do that manually by creating a JavaScript code, but not anymore. So all we have to do right now is to clicking here by creating a specific trigger type, which is scroll depth. And we have to define whether we want to track vertical or horizontal scroll depth. In our case, it will be vertical scroll depth. And we can either choose percentages or pixels. So let's take two percentages. All we have to do is just type the values 0-100 separated by a comma. So let's try something like this by 25%, 50%, 75% and 100. We want to fire this trigger for the purposes of this showcase on all pages. So I will now click right now on the Save. So we have the trigger ready. And now we have to create a new tag which is the measurement called we want to be triggered. So now we click on the new tag. Again. Good. Naming convention. Convention is good. So G4, custom scroll tracking. My case, I, in this case I want to send their event. Now the configuration of n because everything that we measure to Google Analytics for is considered to be event. So I'm clicking here. All I have to do is to select configuration tag, which is basically taking all the values from the basic configuration of Google Analytics measurement, which in this case is the Tech we created in the previous video, which is g configuration. Now we have to name the event, which is basically the value we will then see in Google Analytics. So let's e.g. use this one as a scroll tracking and we have to select the event parameters. So additional values we want to see in Google Analytics how to do that. It's fairly easy. We'll click on Add row. We have to name this parameter and as we want to track scrolled up that this is exactly the recommended name of the parameter. So scroll depth, and we want to send there some values. And this is the part where the variables comes to place. I will click here. And here are just a few variables or parameters available in, in Google Tag Manager which are being collected automatically. I will click to see more of them, which is here on the built-ins. And as you can tell just like quickly scroll, there's quite a lot of them. We won't go through all of them, but feel free to go through, through documentation. What do we are interested in about scroll depth threshold? So this is what I will do. Now. I will add there, scroll that threshold. And all it does is every time that tag is triggered by the trigger we created, it automatically fills in the scroll depth threshold because this is automatically generated value. So this is, this is the tech configuration. And the last thing we have to do is to select a trigger when we want to fire that deck. So clicking here, and I'm going to select that Custom scroll tracking trigger we created just a minute ago. This is when I want to fire that. Now the custom scroll tracking event is ready. So we will click on the save. Everything is ready. And again, we have to publish that version of Google Tag Manager. So I will find some nice name. Gal4, custom scroll tracking. Well, publish death, again, waiting for a couple of seconds. This is it. So that should be already live. And again, we will show the real example that it's working. I will again go to my website, pressing Enter, and again, I'm scrolling right now to at least 25, 50, 75 per cent. So as you can see here right now, by this red bar, I should be somewhere over the 50%. Let's e.g. click on one of my blog post. Again, I will scroll down a bit. At least 25075 or let's go to the very bottom to the 100 per cent of scroll depth. And let's go now to the Google Analytics, to the real-time report I'm in. I will refresh it to see whether the data is already being collected. So let me scroll down a bit and we should be able to see already Custom scroll events, right? I just scroll down and we can see that under event name, I'm already seeing the data being collected. This is the custom scroll. When I click on that, you can see there are a lot of parameters being sent automatically. But also one of them is that scroll depth, which we created in Google Tag Manager. So by clicking there, I will just check, okay, it's sending the exactly the values of the scroll depth we set in Google Tag Manager. So as you can see, it's working. It was pretty straightforward. If you wish to enhance your measurement much more. Go into all that Google Tag Manager topic. This was just the example, how to set it up. And from now on we are collecting all the basic data to Google Analytics. And we can jump directly into interface and describe it in much more detail. 19. GA4 - additional setup: Okay, let's dive a little bit more into additional setup on a GA site, which I highly recommend you to do. But even if you want, you will still measure quite a lot of data. So the first thing I highly recommend is to go to data settings and then data retention setup. What there is by default is that these Evan data retention is set to two months. Please go there and switch it to 14 months. You will have additional capability of analyzing a little bit more aggregated data. What it does, it doesn't mean that if you will leave it to two months, you will lose the data. But since the GA is pre aggregating a lot of data for the further analysis. If you will leave it to 14 months, we will have a lot more data that G can then aggregate. So this is the first thing I recommend you to do. Switch that 2-14 months. Just click Save, and that's it. The couple more things I recommend to change a little bit is going into data stream setup. We just create it. And we need to go to Configure Tag settings here. Here we are. We will wait a little bit more. And what do we need to do here in settings is to click to show up. There are two things I'd like to show you. First of them is to modify the list of unwanted referrals. This one is specifically very useful for those of you who have e-commerce sites. Why? The way it works is every time there is a change of a terrific source, all of the following actions are attributed to that traffic source. The nice example is when someone is purchasing something on your website, then customer is going to a paywall. He pays and going back to your website. If such a scenario happens, all of the revenue and transactions are there and then attributed to that paywall, which I don t think it's the case you want to have in your data because it tells you, Okay, We have great traffic source which brings **** of a lot of revenue and transaction and it's a paywall. That's probably not the case. And exactly for that reason, there is possibility to exclude such a traffic sources. All you have to do is just to click here. Select one of the conditions here. It's completely fine to leave it as it is on the referral domain contains. And then just type in the name of the paywall e.g. people, or any other that is in your, in your country. So if I would just leave it like this, click Save from that moment on, the paper towel would be ignored as a traffic source, and the previous one would be still attributed for all of those upcoming transactions and revenue. Second thing I recommend you to do is to adjust session timeout or at least click there. We have two things we can set up here. First of them is to adjust a session timeout. As we know by default, the session or one of the conditions one session is expiring is after 30 min of inactivity. This is something that was with us during all of the history of Universal Analytics and we are all used to that. So I highly recommend you to leave it as it is. Or if you had a different session timeout in your Universal Analytics account, please adjust it according to it. So this is the first thing and the second one, which I think is more important in this, in this setup is adjusting timer for engaged sessions. What it is. Previously in Universal Analytics, we were familiar with the concept of bounce rate and balances. What it is, it will, It was one of the metrics which was used in a lot of reports, in a lot of analysis. And it was telling us how many customers or viewers or users viewed only one page and then left. So this was considered as a bounce okay, user came view just one page and left that has bound. Gal4 is using a different concept but still the same methodology. It's switching from bounds to engage sessions. The one of the definitions when the session is considered as the engaged? Yes. When a user review at least one page, which is at least two pages, which is exactly the same as in the Universal Analytics. But there's one more condition, which is also including the time spent on your website. So you can set here after what time window, you can consider the session as engaged one. So by default it's 10 s, which can be, I would say a little, little small number. So I would recommend to adjust it to at least know 30 or 40 s to consider the session as engaged about I leave it up to you. Every website and business is different. Anyway, definitely 10 s is very small number to me. So I will set it up to 30 s and click on the Save. So this is about the, about the session timeout. And the last thing I'd like to show you is there's one more option which is Modify event. As we said a couple of times, Universal Analytics was more session oriented. And G4 is mostly like Evan based analytics. And one thing which is available here, which is very nice thing, is called modify events. It's something very similar to the filters in Universal Analytics when you are trying to replace some values in your reports. So let me show you what is possible to do there. I will write now switch to my another GA for account where I already have some data, which is here. And I will just simply go to the report of traffic sources. Don't worry if you're not familiar with the report itself, we will go through that. But for the sake of this video, I will go to acquisition overview and look on the little bit longer period of time, I will click apply. And what I want to show you is to see, sorry, we'll go to traffic acquisition. What I wanted to show you. If I will write now switch my default dimension, which is default channel grouping to source. You can see there are the various traffic sources coming to my website. And the one example I'd like to show is line number four, where there is a traffic from udemy.com coming to your website and also from the sub-domain EY learning udemy.com. As soon as scenario, I'd like to join those two sources under the one. So I'd like to see only one line when there is only udemy.com. This is possible to do in that, in that setup. So if I will go now back to admin, to data streams setting. Here we are, and I will go to Modify event. I can create such a condition when G will automatically rewrite some parameters according to the rules we set up. So let me click here and I will create a new modification rule. So call it, which can be Udemy source unification, something like this. And if I will click here as a parameter, which is source, I want to everything that contains Udemy to then be shown in the report as udemy.com. So if I'm if I will now save it from that moment on everything, every traffic source that will continue Udemy will be shown in GA interface as udemy.com. So this is something that can help you clean your data. Keep in mind that it's not good. It's not working retroactively, but only from the moment of setup to the future. This is important, important to remember. So yeah, that was a little bit more regarding the general GA for setup. Recommend you to do that. 20. GA4 - goal setup: The next thing we're about to show regarding the GA setup before we jump into the explanation of the interface, our goals. I'm using the old naming goals from Google Analytics three or Universal Analytics because right now they are called conversions in GA for, no matter the name, it's still the same. It's some predefined action towards which we then evaluate the website performance. So it definitely worth having set at least one goal per every account. Or let's use the new terminology, which is conversion. There are three ways how to set it up. The first of them is using so-called automatic events, which are automatically marked as conversions in GAL4. There is a set of events. Let me show you when I go to Configure which are by default created as and marked as conversions in Google Analytics. Depending whether your GA for an account is set for app or mobile, that are automatically created, events which are matched or sorry, marked as convergent. One great example is purchase event, which you, if you start sending to Google Analytics for it, it is automatically marked as convergent. As you can see. There's nothing I can do with it too on market. So this is one way of setting up something as convergent. Every event which is from the predefined set of events can be marked as convergent or will be automatically marked as conver. Second way how to set it up is to go to events in the configure sections where we are. There, you can see the list of events I'm collecting into my GA account. And from that events, I can mark any of them as calmer. So e.g. we have here File Download, if I will mark it from now S conversion from that moment on to the future. Every time such an event will occur, it will automatically be counted as conversion. Please keep in mind, it doesn't work retroactively. All of the events that were recorded previously will be taken a standard events only from the moment of set S, S conversion. It'll be marked as conversion. So just a quick example that it works. If I will then go e.g. to the report of Terrific Going to traffic acquisition. And scroll down a bit. If I will scroll, scroll to the right, I can select this file download as a conversion. Obviously, I will see here zeros because I just set it up as conversion. But just to show you that it works instantly and you can see that pre-selected event S conversion. So that was the second case of already collected event. Then there's a third one. And I will go back to a convergence, which is by modifying existing event. And the Bernays example of that is if you have some particular page, which if it's viewed, it automatically means that there was a conversion. Very nice example are thank you pages. Let me show you one example. Let's assume that every time someone viewed a URL, which is thank-you page e.g. on my, on my domain bulb rhetoric that sees it. I want to mark it as conversion. How to do that? We have to click on Create Event. And we have to create a new one. First of all, we have to create a dedicated specific name for such an event. So in my case, let's assume it would be. Thank you. Page sorry. Now this one this one. Now I have to create the conditions based on which such an event will be Asara heavier a typo, which is supposed to be like this, based on which conditions such an event will be automatically also created in Google Analytics. So my conditions are amend name is page view because as we know, GAL4 is event-based analytics. So everything which ascender is event. So every time there's a page view and a page location which is the perimeter for URL address e.g. will contain. Thank you that HTML, this is hypothetical case, but just to show you how to set it up. So let's assume I am having such a URL address on my website, which which to me means that someone e.g. has successfully sent a contact form. And I'm thinking for that. So if I will create such an event from now on every time both this conditions are met so that the the event Pedro occurred, which is every time the page is loaded. And also the page location, or we can say URL address contains think HTML. The event thank you. Page will automatically appear also under the events I'm collecting, so I will copy it and let me give me give me a second then you will know why. So if I will now create such an event from that moment on if such a condition will occur. You can see here the thank-you page event will be automatically sent to Google Analytics. And if phi right now, well, want to create a new conversion based on that event, I have to go here to Conversions, create new conversion event. And this is why I decided to do copy that the name of the event which was thank you page. So if I will click right now, save from that moment on also, every time such an event will be sent to Google Analytics will be also automatically marked S conversion. So this was the third way how to set up a goal. This will just an example how it so far works in Google Analytics. So there are three types, just to recap, that we either can collect or use any event as conversion if we use some of the predefined Events, which is e.g. purchase for, for mobile app, for web website measurement. The second case is that if we go to the a list of already collected events and we just mark it as conversion. And the third one is the one we showed right now, which is creating the event based on currently existing event. So it was e.g. a. Thank you page. So these are these are three examples of how to set it up. What is important to remember is that if we compare the way the goals or conversions were counted in Google Analytics, three, or so-called universal analytics and GA for defers the rule in GA, three wars that every conversion could be counted, counted maximum of one time per session. So even though e.g. someone would download five files, which is file download event in Universal Analytics, it'll be counted only once per session has conversion, whereas in GAL4, the conversion is counted every time the event occurs. So if this is the same case and someone will download five files. We will also have counted five conversions in GA for, so this is worth remembering. So don't try to compare the total volume of convergence in G3 and G4 because it can differ, and it can differ quite a lot depending on what type of conversion it is. So that was it. 21. _00006Filtering and sorting: Another basic technique, how to work with data in GA four is the ability to filter them and then order or sort the data in the table among any metric you want to. So the way how to filter the data is by using this search tab where we can type pretty much anything and then press enter and wait what happens. So the way it works is that if we, for example, type here GO O and then press enter, it'll pre filter us the table by all the lines containing, and I stress the word containing the search term input there. This is the way it works. Pretty straightforward. So far, we don't have the ability to filter in a way that we would filter only the lines that begins or ends on a certain search term. For now, it works only in a way that we can filter with the condition that contains. Similarly, it works if we would add a secondary dimension. If I will use the same case as in the previous tip, and I will add here a secondary dimension country, and when I then use the search, it works that it will leave us or return the lines that contains in at least one of these lines, or sorry in one of the dimensions, the search term. So let me write here, for example, GUU, as we had there. We will have all the lines containing Google, either organic or CPC, or if I will right now type here united, You can see that what I input here is searching among both of these dimensions. Right now as I tape type there United, we can see that prefilter as the lines containing United, which would be probably only United States and United Arab Emirates. But I think you understand the logic. This is how the filtering works. We don't have the ability to filter only the primary or only the secondary dimension, neither the ability to filter by any other condition than the condition contains. This is how the filtering works. If I will go now to the second feature I want to show you, which is about sorting. Let me cancel the filter here, so we have only the one dimension to better show how it works. The way it works in the standard report is that every report is sorted by the first metric, which is here, in this case, sessions. And the way it sorted is signed by this small arrow, which is showing, the report is sorted by the volume of sessions. If we want to sort the report by any other metric, we can just simply click or hover, On the metric, we want to sort, and then just click on that. If we wait for a second or two, we can now see, so the whole report is now sorted by the average engagement rate. Similarly, I can click by engage session. If I want to sorted descending way, I need to click again on this arrow. And you can see it's swept right now, so it's showing, so now the report is sorted by the engaged sessions and the channels are ordered by the one with the lowest volume of the sessions to the highest one. So this is another pretty simple thing, but very nice to do. And another one of the basic features of the GA interfaces, and thanks Google for that. This is something I'd really like. If you just hover over any metric and wait for a couple of seconds, there's a great tooltip explaining you what actually the metric means. So for example, if I go to the engaged session, I can see, there's a nice explanation, and sometimes there's even like the additional information source where we can just click. And then you have pretty extensive analytics help. You can just read the very details about the examined metric. So this is again, something pretty nice to use. And the last thing I will show you in this video is the ability to also filter the report specifically when we want to examine some events. What are events is something that we already described just briefly, and we will also cover that in the rest of the course. But why is that important is that in the majority of the cases, when we will examine some traffic source, we want to examine it towards some key event or formally something that we called conversion. And how it works here is that we finally can see also the conversion rate, which is the former name, and now it's called like event rate. So this is something that was that took years, again, to be to be launched, but it's finally here. So how to work with that. If we use particular event as a conversion, meaning something that this is something as expected action from our users, we want to see a conversion rate. How it is calculated, is something that is explained further in the course. But I want to show you that that basic thing, how to filter only for a particular key event and see the conversion rate or sorry, I'm still not used to that to use it like the key event rate, but it's fairly simple. I just need to click on this drop down menu, and I need to select for which event or sometimes can be called like the action. I want to calculate the conversion rates. Let's assume for this case as the Google merchandise store is really an E Comer store. I want to see how particular traffic sources are performing when it comes to the purchase. I will click here on the purchase. If I will wait for a second or two, I can see that this column, this metric, session key event rate was calculated for only particularly one selected event, which in this case is purchase. Now I can compare the traffic sources by the succession key event rate, and we can see it differs quite a lot. Again, right now we're only showing how to use the interface. Yeah, that was it. 22. GA4 - UA vs. GA4: Okay, let me describe you the main differences between Universal Analytics and brand new GA4GH, Google Analytics for if there was one sentence I was supposed to explain it, it would be something like this. Everything has changed. For those of you who already tried to explore the interface, you definitely noted that it's looking completely differently. It's probably not that easy to get to the reports you are used to. Let me explain you. Why is it? So we have to start with something which is called Data Model. It's the set of rules based on which the data are being collected into database and then the way the interface is being organized. So when looking on the Universal Analytics, there have been quite a lot of different types we were collecting. So there was something we used to call page view. Then there could be a van den e.g. social media interaction transaction. Some events which were sent automatically to Google Analytics such as user timing or exception, or in case of measuring a mobile app, an app or screen view. Whereas looking on GAL4, everything there is event which can have specific parameters. So this is the main change in the data model. If we will show it in a little bit more detail on the next slide, this is how it works or used to work in Universal Analytics. We had something we called user. And user was making different interactions, as we said, e.g. pageview, which was hit, then it could be social media interaction which was also a hit. Then e.g. it's to cart event. Based on certain rules. Those hits or events or any other hits types were sending were then encapsulating to something called session. There were three rules after which a session could expire, which was e.g. after 30 min of inactivity when the traffic source has changed it or if you if the date changed. So if you were exploring the website around around midnight, the first hit after midnight automatically started a new session. Then there could be another session for a particular user which started with checkout event, then e.g. another page view which was hit, and then transaction which was also it. So this is then how also the interface was organized, if you remember it, all of it is around the sessions, right? Even some of the metrics have something called session as a name, like a session with advanced session with transaction and so on and so forth. So this is the way it was organized in Universal Analytics. Whereas if we look on the Google Analytics for, as we said, everything is event there. So if I will take the same timeline off of interactions, right now, I even have something as it's called, a session start, which is automatically sent to GAL4 and it is also event. Then we have a bedroom which is advanced social media, which is a two car, which is event, Checkout, which is event, and so on and so forth to be exactly same precise comparing those two views, Universal Analytics and j for i was supposed to have here one more session start event, which was supposed to be exactly here between Add to Cart and checkout event, which in this case is here. So this is the way it looks from the data model perspective. And this is why also the interface looks completely different. Whereas in Universal Analytics was everything organized around the sessions. Here, it's much more organized around the user and the events. So it's more user-centric analytics. It doesn't mean that the sessions itself disappeared from GAL4. They are still there, but they are not as much promoted as they were in the Universal Analytics. So hopefully, this gives a little bit more perspective. Why is it so? And we will describe the interface in more details in the upcoming videos. 23. GA4 - Free form: So we already know how it works with the default report in Google Analytics for interface. And right now we're going to, in my opinion, the more interesting part, which is about custom reports, what do we have to do is to go to that explore part. So let me click there. And what we can see, there are a couple of options how to create the first new exploration report. We can see we can start completely from scratch. Or we can use one of those predefined type of reports or techniques as they are called its free form, final explanation, exploration than segment overlap, cohort exploration and user left a lifetime. We will cover them one by one. So let me start with the first one. We can use free form. You don't have to start with a blank one because the first step you have to do there is to select what kind of technique you want to use. So let me start with a free form. As you can see, it looks quite complicated or, and that's complicated, but with a lot of options we can do here. For those of you who are familiar with custom reports in Google Analytics. This is something that works very similarly with couple of improvements. If you select any of those predefined options, google automatically shows you how a report can look like. So in this case, we see device category breakdown with city breakdown and then metric active users. We are going first to remove all dimensions and metrics we have here. And we're about to show how these free-form technique looks like and how to build it from scratch. So let me first remove everything we have there, all the columns, all the values. So we have blank report how it works. We have to start on the very left. First of all, we can name reports somehow. So I will name this test report here. We select the date. I think that's pretty straightforward. Then, uh, we have here segments. This is something that it's basically named exactly as it was in the Universal Analytics, or we can call it comparison here. So this is something we can select here. And then we have dimensions and metrics. There are some by default selected dimensions, metrics if we want to add any other dimension, except the ones available already here, We have to click on plus. And we can either select them by scrolling or we can use this search engine here. So let me e.g. they're like operating system. Just to show you that it works, I will click on import and then the operating system will appear here. This is the way it works. We have to first edit here, and then we can start using them in reports. Exactly the same way it did works with metrics. So if I click here, I see all the available metrics. So I can choose any of it. So e.g. let me Google for total users just to show the very simple example. And let me add actually one more dimension, which is Session source medium. Sorry, I will search for it like this. We can see that we hear a source medium and then we definitely have here sessions source medium. It's sorry, sorry, sorry, sorry. Here it is. So anytime you'd like to see the numbers comparable to Universal Analytics or the logic that is used to work as the Universal Analytics regarding the traffic sources, I always recommend you to use this perfect session. So far it works a little bit differently in GAL4 with the default source medium. So again, stress it that use sessions source medium, session, default grouping and so on and so forth. So let me add also this one. So here we are. Now how it works. We have pre-selected the technique free form. It's possible to change it to any other of it. So but for, for the purposes of this video, leave it free form. Then we can select the various regionalization types, such as table which we will show and the line chart. And there are a couple more of them, but not very much useful. So the table and the line chart, it's something that can be already used. How it works right now, we have segment comparisons if you want to already add a segment, but we can skip this step for now, we are about to build a simple report and how to start with it. We can select rows and columns and the report will start to be created. So let me start with a simple report where in the rows I'd like to see a device category we can edit thereby either double-clicking on it or by manually drag and drop. So let me just double-click it automatically. Understand that it's row. If we wait for a second, we should see, start seeing there. So now we have to wait for a metric. Let's add. The first method which is total users. Again, I can either drag and drop. I will show it that it works also this way and add their metric. And I can see that it already started to appear. So e.g. let's also add the transactions since we're in GA merchandise store where transactions are being collected as an event. So let me add there also transactions and now I'm starting to see more and more columns. If I e.g. also would like to add another dimension, I can do that. Again. Let's use double-click right now. If I do that, it automatically expense the report. The report from the London engine to actually two-dimensions. I will cancel this dimension now to show what is also possible to do there. So let me minimize that. And what do we have here? So far, it's a simple table. You can see that the volume of total users transactions you can sorted by ascending or descending or whatever you wish, also by the volume of transactions. Now what do we see right now is a simple table, but also working as a bar chart showing this, this bar here. It's not very much useful since I guess the number itself works much, much better here. So this is the simple table we can create. We can play with basically many dimensions and metrics available there, but you can also change here is that cell type view. What it is right now we have bar chart, as I said, which is representing the, visually the values we see here. We can change it just plain text if we want, and we only see the numbers itself. Or we can also change to a heatmap, which we'll color it, Show us the highest, higher than the lowest, the lowest earlier. So I will leave it here just to plain text for the sake of this, of this report. So this is the very simple thing we can do. Another option available here is about filters. What is there? We can pre filter the report we're looking at. So let me show you what I mean, e.g. right now we have all users coming to a website and all transactions. What is possible here to do is e.g. to pre filter that e.g. that the sessions source medium. Now I can select conditions, can contain e.g. and now select expression. Let me just click the Google Organic. And if I will click Apply, we wait for a second and we definitely will see lower number of both users and transactions. So this is already pre filtered only for particular conditions. So this is what we can build. We can also definitely add more metrics and more dimensions if we want to, if it makes sense. And the second thing I wanted to show you regarding that free form report before we dive deep is about the types of visualization. As I said, there are more of them just to show you how the doughnut chart works. It's not very much useful, right? Because it's not showing us neither the absolute values, neither the trend line. But what I use from time to time is a line chart which is showing me how the numbers are evolving in time. So if I switch to that, I had there my dimension which was Device Categories. And now we can see the total users per every dimension, which is device category here. So this is something we can do. We can also, as in the cases of standard reports, use date comparison or adding segments if we wanted to. So e.g. if I were to add there beats traffic there, the chart would change a little bit. We just have to give it a second or two until it's loaded. And right now it's showing only the data for the paid traffic. So it's basically automatically, automatically free filtered only for four beats. Terrific. I can cancel it right now and getting back to the numbers of thousands instead of, instead of units. So this is it. I will get back to the table and just do a quick recap of this report. We'll get into more details in the upcoming examples. So this is how we can control it by adding dimensions which we first have to add manually here. Also do with the metrics. We can select the type of visualization we want to. And then we either by double-clicking or drag and dropping, can create a custom report with the data we wish to see. We can also pre filter them based on the condition which in this case is source medium containing Google Organic. And we can change the type of the cell from the bar chart to plain text or to heatmap. So this is the example of very basic report and the controls available here. 24. GA4 - conversion rate: Okay, let's do a little bit of theory about the conversion rate in Google Analytics for there is a change comparing to Universal Analytics, which means that we, instead of one conversion rate or one calculation of conversion rate, to be more precise, we have two of them. It's session conversion rate and user conversion rate. Let me illustrate on to examples was the difference and how the exact calculation works. So let's assume we have user a who does do sessions. In the first one makes two conversion events. In the second one, there is no conversion event. Then we have user B, who has different set of sessions. In the first one, there's no conversion event as we can see. And then the second one there is one conversion event. So we have two users for sessions, three, convergence. And let's have a look on how the calculation works here. It looks actually like this. It's the volume of sessions with conversion and I repeat it's the volume of sessions with conversion. So we have first session with the conversion. Actually there are two, but it's basically a binary counting. So if decision had conversion, it's marked as the one with conversion. And then we have the second one also with the conversion. So if we do the calculation right now, the volume of sessions with conversion is two divided by the total volume of sessions, which is four. And we have 50% right now. So it's quite obvious that if those users had more sessions without convergence, then the conversion rate would decrease. So this is the first calculation. Then there is a second conversion rate, which is called user conversion rate. And this works differently. We have the exact same group of interactions and we have also just to users. But the calculation works as the volume of users who made conversion. I repeat the volume of users who made conversion. So in this case, both of them have made at least one conversion. So this is why we have two in the nominator and also two in denominator. So we divide to as volume of users who had at least one conversion divided by the total volume of users. So in this case, no matter how many more sessions will those two users made, the user conversion rate would be still 100 per cent because every single one of them have made at least one conversion. So these are the differences. It works differently than in Google Analytics then in Universal Analytics, this is why explain it at that specifically. And now let's have a look where to find conversion rate in GAL4 interface. 25. GA4 - conversion rate in the interface: Alright, as we now know the precise definition of both user and session converting light, Let's have a look where to find it in Google Analytics for. So far it's not available in any default report. And the only way how to see it and access it is in the custom one. So we need to go here to the Explore part where we can build a custom report. And for the sake of this video, let's choose free form. So this is the one we already know how to do that. Let me clear this one so we start from the scratch and here we are. We have clean report. How to do that? First of all, we need some dimension, so I will use my favorite one which is device category. By double-clicking on it. We sit here and as we know how to use this report, we are adding here the new metrics, so we'll add both of them here. So let me see it and hear this session conversion rate, end-user conversion rate. I have to import it first and I add it actually two more metrics here. First of all, I'd like to see the transactions volume than adding here session conversion rate, user conversion rate. And I will also add here one more metric which is total users. Just to have a little bit more extensive reports. So total users, I will add this one here too. So here it is. And now we, for the first time can see both session and use our conversion rate. Let me minimize type settings and variables. So we see the bigger report here. What do we see here? It's session conversion rate and user O'Connor, but there are a couple of things worth knowing. First of all, so far, it works in a way that we are not able to filter only particular conversion for which you want to see conversion rate. So what did it mean that if we now have e.g. marked as conversion e.g. first visit or any file download or multiple events. The conversion rate is calculated as a sum of all conversions which occurred. So this is why we see that high conversion rate, 50, almost 60% for the session convergent rate, and almost 90% for user conversion rate. So there's not possible to do any breakdown when it comes to see particularly conversion we're interested in. So it's not very much useful so far, but this is the way it works right now. And the only way how to access it is as we just showed in a custom reports. So please bear in mind, it's not we can't do much with this right now, but hopefully in the future, it'll be more flexible. So we'll be able to select only the conversion we are interested in and see it in a report. Because as you can see, like 60% conversion rate is something we might wish for, but it's definitely not reality and we now know the reason. Why is it so 26. GA4 - funnel exploration: Okay, Another exploration report available in Google Analytics for is called funnel exploration. So again, we're in the Explore section in J4 and this is the one we're going to look at, as it's hopefully clear from the name, it's about funnels. For those of you who have been playing with funnels in Universal Analytics, you know, it was a bit rigid. It means that we only see any funnel if we created a specific goal called funnel. And then we were able to see what was the abandonment rate between any other steps we just created in the funnel? Here in Google Analytics for its upgraded, it means that we are capable to create a funnel retroactively. Which means that we can select any set of conditions through which are the steps through which we expect users to flow and then see what was the abandonment between every other step and as well to see what happened with those users. Exactly as in the case of free form. If you click on the funnel, exploration as I did, just, you will see already prepared report from Google. I want to start from the scratch. So let me clear all the steps here by clicking on the axis there, as well as a breakdown. So we have a blank sheet here. The way it works, we already know anything we want to add to tap Settings has to be added to the segment dimensions and metrics, which are called variables here. So let's start with the very beginning. What we have here. The most important thing here is to create a steps through which we want to see what was the flow in a positive way and also abandonment rate in negative way. So let me show you how to use this type of report as well as create the first final report. So by clicking here, we have to first define the step name and then set the conditions by which we basically define who or how many users will be counted into every step. So let me do the first one, which is very simple. I would like to see how many users made it to category page. I'm naming it as category page or let me rename it maybe like a view category page. Now I'm asserting a condition and as we know, everything in GE four is right now event. So I'm selecting one of the events here. And as you can see, there's plenty of them because they're a bit advanced implementation of Google Analytics for the event I'm looking for is view item list. Just keep in mind that this is not something that is tracked by default in Google Analytics. And it requires additional setup either in Google Tag Manager or it requires setup from your developer. But for the sake of this video, I'm showing you what is available already in google Analytics for merchandise account. So we're item names list. This is the first condition. If we want to somehow specify step more, we can add some parameter here like e.g. only users from United States or only users coming on the mobile devices. So this is what we can do here. Or if this is the step where multiple conditions can be met, we can add here also another condition. So e.g. I. Would like to see abusers who viewed category page or product detail page. I could add here another condition. I don't want to do that by just showing you. Or we can add another condition here, which would mean that both of the conditions we select here are combination of the total step. So this is, this is what is possible to do. I will leave it blank because we are going to show how to interact with the report. So this is the first step. Another step could be users who viewed product detail page. I'm just trying to show you normal flow of users through the e-commerce site, which again in this case is event, which is called view item. It's this one. So this is, this is the second step. And the third one I like to add there is users who added products to card, so I will name it, it's edited to cart and the event name in this case is at two card, so I didn't have to filter that. So this is, this is the basic setup. There are a couple of more options. If we want to somehow add there also a time dimension which means that we want to next step occur within some time frame. We can do that by clicking here and select it. And select After how long it has to happen. If it makes sense to you, feel free to use that. Then we have another option here which is either indirectly or directly followed by. So this is again, another specification we can do. Indirectly means that there can be any other interaction between those two steps. And it doesn't have to happen exactly in that sequence. So this is what it's doing. For those of you who have been playing with Universal Analytics, it's pretty similar setup as in the sequence segments we were creating. There. So this is it also can copy step or removes them by clicking on these three vertical buttons, as well as we can add step above or below if we decide to enrich it. So this is, this is the very basic setup. So let me now click on Apply to see what will happen here. And wait for a second or two. Here we are. So right now we can see how the funnel looks like, how to orient in that. Let me minimize the tap Settings and variables. So we have a Morris Bezier, how it works. Every time we hover on the particular step, it shows us how many users have made it to particular step. So in this case, we can see that 42,000 users viewed category page and 21,000 of them have made it to product detail page. So as we can see, all those percentages here are showing us one was success rate of the previous step. So this is it. And if we then look e.g. on the add to cart or the volume of users who edit at least one productive card. We can see it was 8.3 thousand, which is 40 per cent out of those 21,000 who made it there. So just keep in mind that the percentages we see here is completion rate or success rate of previous step. The numbers below charts are, is the abandonment rate. It's telling, it tells us how many users didn't make it to the next step. So this is how it works. It's also in the percentages here, so we can see 50 per cent didn't make it to the product detail page. And 13,000 out of those, 21 didn't make it to Add to Cart. So this is how it works or this is the very basic setup. We can tell us if it's good or bad. What is important. Such a setup itself doesn't tell us much, right? Because we want to see some comparison. So if I will go back now to the app settings, Let's add there and also variables. Let's add some breakdown. So in this case, let me use my favorite 11 of my favorite dimensions, which is device category. So I will drag and drop it here to see the breakdown there. And here we are in right now the total numbers hasn't changed. But what do we see here in the table? Minimize at least one of them, is to see what was the flow in a breakdown by device categories. So we can see what's the completion rate for every major device category, which is three of them, I would say that's the mobile and tablet smart TV, as you can see, there's only two users. But anyway, we can see what the completion rate, and we can see that it differs a lot. So what it, what it can tell us is basically there might be some issue comparing completion rate on desktop and mobile. It of course requires further analysis, but this is just a first view to spot where the potential issue can be in the flow of users on a mobile devices. So this is, this is something we can easily do here. Another thing available here is filtering. This was just recently introduced and it's a great, let me show you what it does. Again, if we want to filter by any dimension, we have to first have it here. So for the sake of the report, I will add a device category here. And I will filter some conditions which I would like to then be applied on the report. So let me use one of those operators here, which I would say Contains. And I only would like to see the data and the chart for mobile devices, which is by doing this, I will click Apply. And now both the report and the chart will filter only and only for mobile devices. So it's easier to explore it then and see the chart for a particular filter we just do. So this is another thing we can do here. If for some reason makes sense to either zoom in or zoom out, you can do it by this, but just keep in mind that this wave is telling you that the size of the chart is not recommended, is not responding to the numbers we see here. So keep that in mind. You can zoom out and then back and see the, the, the right, I hate of the chart. So this is, this was another thing which is filtering. And then another great thing there. Let me clear the filter here is the next action, availability here, what it does is let me just show you exactly on the example. So then it's much easier to understand. First of all, I have to add their dimension, which I would like to see here, which in my case would be page path. So again, I have to go there. Let's use e.g. this one, beach bath and query string, and it will import it here and then use it here. Snack section. On the first side, nothing will happened with, with a funnel we have here. But if we hover here, we can then see what was exactly that next action. So in this case, we can see that out of 42,000 users and the top five next actions was number one was no next action. It means that the user has just abandon the website and the session finished. There we can see that another five URL addresses to which users have gone. It can tell us if if we are such a report, if configured properly, can tell us if there isn't some loop in which users are constantly looping, not going to the next step. So this is, this is another great thing available here calling next action. So for now we can only use event and some of the Page Path dimensions. Hopefully it'll change in the future, but this is the way it works and this is what we can see here. Again, I will cancel that. And there's another thing we can see here, which is a segment comparison. We can just simply compare segments over that report. Let me show you how it works. One of the variables here are segments, which is something I hope we are all familiar with. It's just a subset of the data we're looking at. Since so far, we're looking on all of the data. So if we only would like e.g. to see direct traffic, I will take it here. And also compared e.g. with a mobile traffic, your M. We will see the funnel will change a bit. It means that we will see now two lines, okay? Hopefully it's pretty clear. So now we can by hovering over any of those colors which are, which are telling us the blue one is direct traffic, the purple one is mobile. Well, we can by hovering here, see what was the flow and compare those segments to each other as well as see what one was also abandonment rate for every segment we have here. So another availability here if I will cancel it. So we don't have much of it here. But what we also see here is two types of visualization. This one is called standard funnel, which is the static one, telling us what was the flow between every other step. Or there is a possibility to also use a trended funnel. Important to know is that it's not that much useful yet because it's showing you only the total numbers of how many users have made it to every step in time. What will be more useful is to see what was the completion rate instead of the absolute numbers. So there are a couple of scenarios where this also can be useful. But, but for now, it also require some further development from Google so you just know what it will do in the future. So that was it. Hopefully, it's pretty clear how to use this report. It's a major upgrade comparing to Universal Analytics because we can create funnels retroactively for the combination of any dimension and event we'd like to. So that was it. 27. GA4 - path exploration: Another new exploration added to Google Analytics is path exploration, you might say, okay, but this was also available in Universal Analytics. And you are right by the report, has a lot of limitations and wasn't much useful. That one has much more features right now and is much more useful. Let's start from the very basic and let me show you how it works. So by clicking here, the path exploration. Again, as in the previous case, Google will already show us some data here by viewing some path exploration. But as we want to learn it, we want to start, start over, which is by clicking here. So let me do it. And already here we can see what we can either select starting point or ending point. What's the difference? First of all, we select where we want to start, and then by clicking we can see what happened with the users. What did, where did they go? So let me show you how it works. First of all, let me show you a simple example. I will take a page title and screen here. And I will select e.g. only the homepage. What will happen right now? I will see what happened with users who made it to homepage and where did they go following link. So first of all, I can select either if I want to look on the event count, which is how many such an event occurred. Or I want to change it for any other metrics. So for, for a single report, I will switch from Event Count to total users. So let me just drag and drop it there. So the number should be a little bit lower. Yes, it is not a little bit significantly lower, but what is important is to show how the report works. So we just can easily click on every other step and dig deeper and deeper and deeper. And Sebald was exactly happening with the users going through particular path. That is something that was completely unavailable in Universal Analytics are very difficult available. And we can go, Let me minimize this for a second, up to ten steps. So very, very granular level. We can, we can go through that and see whether the users, e.g. aren't in some kind of the loop where they got stuck. We can also do some filtering here or changing the one of the three available dimensions to e.g. event name. So it can be for every starting point, it can be different. So I can see okay, from whole page then the user has made a page view. If I click here, then what was happening in terms of event, which is view promotion, first visit and so on and so forth. Right now we're only showing how the report works. So bear with me for more concrete examples. And I can also do a filter in here. So by clicking e.g. here, I would like to only see the one who made it to view promotion. So I will do it by unclicking and other ones available. And clicking here on Apply. If I will wait for a second. You can see only this one is available of the other ones are grouped under, under the More so it's easier to orient them in report. So by clicking e.g. here I can again see what happened with this particular page path. So that was the pot exploration. If we choose starting point, what I think is more interesting, if I will start over and starting from the ending point. This is something that a lot of people have been interested in previously and didn't have the availability of such a report. So if I will click here, I want to start e.g. from events this time. Let me, let me select purchase as ending point. So if I will search for it, purchase, here it is. I can now, by clicking, I can see okay, what was happening here. Before the purchase. There was a patriot. Before the page you there was a scroll, and right now we have events, so there's a lot of events. It's not that easy to orient there. But if I will switch now from event to page diethyl, I can basically see, if I will wait for a second, basically see a funnel we were creating the in one of the previous videos. So we can see that out of those 2.2 thousand users, the previous page view they viewed with Checkout Confirmation, the rest of them made it to payment method. Okay. What was happening with us from the checkout information they made it to payment method. There's some of users, not very much of them who made it directly to that step. What will happening here? Going to check out the inflammation, then going there to the shopping cart and so on and so forth. So I think you are already right now we can see how easy right now it is to also create kind of a backward report or backwards funnel to see what exactly did user made it. How sorry, now, in other words, But how exactly user has made it to a purchase by retroactively seeing what were the previous steps. So this is one of the great features in Google Analytics, no doubt about it. One of the, so far, very few I really like. So feel free to play with it and set, tried to look for loops if you want find something that is constantly happening in a way you wouldn't expect it to happen in flow of the user through the website. And I said Google Analytics, forest of land-based. So feel free to filter it based, based on the conditions which are useful for your websites. So this is how the path exploration works. Hope you liked this report. 28. GA4 - segment overlap: Another new exploration technique available in Google Analytics for a scald segments overlap. So we find it under Explore tab. And here is. So let me go straight to it and explain it to hopefully as it's probably clear from the name, it will visually show us what is the overlap between up to three segments, exactly as in the case of previous exploration techniques. Also here is by default available some set of data. But we want to start from the scratch. So let me clear this one. Now it's clear and we are going on that. What is segment? Basic definition of it is that it's just a subset of the data. By default, most of the reports are showing aggregated data. And the segment is some simple set of rules by which you only set smaller part of it. So during this video, I will also show you how to build a simple segment and let's start directly with it. The interface of exploration technique is pretty much the same. We're familiar with it. So let's start building segment. It's doing by clicking here on that plus button. And the first segment I'd like to build is my favorite one, which is based on device category. And I want to segment of desktop traffic, how to do that? It's very simple by clicking here, searching for the dimension which is device category. You can do it either by selecting in front that long list or using search, which is exactly what I will do. And here it is, Here's my dimension. I will add filter. And I only want to see the desktop perfect, as I said. So here it is. If I will click right now, save and apply, the first bubble will appear here, which will show me how many active users, Sorry, I didn't name it. So let me go just here and edit here. So we'll also show good thing that it's every segment who clearly is already saved in your GA. So this is desktop. Terrific. We are, and we can see that during the last month or the past 30 days, there was about 50,000 of active users. So this is, this is the first one so far, but we're not comparing anything to anything because there isn't just one terrific one segment, sorry. And the second segment I'd like to also create from scratch in a segment of users who have purchased at least do things. How to do that? Again, we have to start from the scratch. It's not pre-built Sigma. And here you can feel free to click here and see what is already pre-built there. But as we want to deeply analyze the data and understand it from the scratch, I will again build it from the very beginning. So this is user-based segment. And how to do that. This time it'll be kind of a event-based segment. Everything is event in Google Analytics for we have to get familiar with it. And so is the kind of the segment I'd like to build. So the event I'm looking for is called purchase. And here it is. And as a parameter here, I have to add something called Event Count. So here it is. It's how many times a particular event has occurred, which in this case, I'd like to see all users, as I said, we'll have purchased at least to do purchases. So the condition it means that the oven count is more than one, if I will. Now, there's also an option of time periods, so I can select that we are looking at. We can also look only for the users who have purchased at least two times during the past, I don't know, 2030, 50 days or we can just disable that, which for the sake of this report is just completely fine. If I will click right now, apply. Wait for a second. We will see that it's not that many of them. Okay. 161. And I will name it S e.g. one plus purchasers. Click, save and apply. Now we'll start see already two segments, right here, it's comparing. And what we also see here is that all of the combinations of possible segments, if I would add one more. So let me do that. I will also add here as we have desktop traffic, I will also add mobile traffic to comparison. To have a little wider table. Let me now minimize these variables. What do we see here? We can see that there's almost no overlap between mobile and desktop traffic. Actually, we can see some of them by scrolling down a bit. There are some users who are counted also for desktop and mobile traffic, which is here 167. But what is more important is that we are looking for the top purchasers, which is that purple one. And we'd like to e.g. see if there are more desktop or mobile traffic users. So comparing to how many how many of total and Wars, which was 160, if I remember correctly. Yes, that was it. Here we are at 160. So comparing to desktop versus mobile, we can see these two lines are in total 160, but we can see that there's 151, which basically means that all of the 1plus purchasers are coming from desktop traffic. This is something that this report easily allows me to see. So this was one of the things we can do here and also break down is possible here. So let me show you e.g. if I will add here first user medium, this will also add another line to the report. Let's just wait for it. As you can expect. I will see what all the top acquisition channel for every combination of the segments here. So a pretty decent kind of report already giving inside. So as I saw from the previous report that most of the first OnePlus British sailors were on the desktop. I can see what was the top channel through which they came for the first time to the Google merchandise store. As we can see, out of 160, 6,103, cake game through the direct channel. And I don't know 30 from organic, just one from a CPC and other seven from referral and so on and so forth. So this is this is also possible to do in that report. There are countless of opportunities of how to slice the data, including the segment overlaps and then, and then demand and bring down. It's kind of a multidimensional table in which we can easily spot the top values. And what is also possible here, if you for some reason find something that is interested for you and you want to either use the segment in the further analysis or e.g. in Google Ads as a segment, you can just simply click right here. And you can create another segment from a selection. So it automatically creates a segment from that conditions you are in. So in this case, it's all of the users from desktop which have purchased at least two times, and also their traffic source is direct. So if for any reason this is important for you, just right now, just named a segment, you can either leave it as it is, that kind of a strange name, or you can name it as you wish. So if we will save it from now on, you can use it in any other exploration technique you want to. Or if you want to start with targeting the segment, you can just clicking here, create the member, members duration. You'd have to create a new trigger here somehow name it S, Let's assume something like heavy purchasers. From desktop. Here I am. If I were to click right now Save and now click Save and Publish, if, if, if this would be my personal account, I will be able to immediately start seeing that segment in Google AdWords and start running campaigns on that. So that's pretty, pretty cool feature available here. So we now know how to build segment overlaps. Report also how to build a segment from the scratch and easily spot something that might be interesting for us. So this is how segment overlap report works. 29. GA4 - reports customisation: Alright, as you probably noticed that Google Analytics for is a lot about customizations, let's dive a little bit in to show what everything is possible to customize. I have to admit that comparing to Universal Analytics, they are much more things we can actually customize. So let me show you how to do that. We have to go to any of the report and in order to find out whether you can customize the report or not, you have to see that icon on the top right side. Also with the name customized report. You have to have sufficient permissions in order to do that. So don't even try in Google merchandise store GFR account because you, It's very unlikely we will have sufficient permissions there. So how to do that? Let me go e.g. to the report of engagement and then into pages and screened. As you can see, I have this icon here. So let me click there and show you a lot available there. So first of all, we can customize those two charts we see there. So if for some reason we don't want to see that page title and screen clause, that bar chart we can just clicking here and you can see it disappeared or salt if we don't want to see line chart and leave it entirely just to blank table. We can do it by here. So this is the first thing we can do. The second one is we can also change the dimensions and metrics we see in the default report. So if I will go to dimensions here, e.g. I. Don't want to see the page title and scream class as primary dimension. I can either select one of those and set it as default, or I can add they're completely different ones. So e.g. let's scroll a bit to see what's available there. And e.g. I. Am interested in the one which is called page path query string, which is something that was or is still available in Universal Analytics as primary dimension. So let me put it there. I will click here and set it as default. I will click Apply. And also e.g. let's change. The metrics will see there because I don't find very interesting to see views per user, e.g. I. Also don't want to see average engage them in time. Let's assume we will leave event count, but since I don't have any e-commerce modules on my website, I also don't want to see this, but e.g. something we all we were used to since it has a lot of limitations and requires deep understanding, which is bounce rate. So e.g. if I want to add it just here and now clicking Apply, I am seeing it here. So if I will right now click on the Save and save it to current report into the life cycle. Yes, that's exactly what I want to do. We will show that in a minute what this pop-up window means. I'm clicking right now on the Save. Waiting for a second until it's published. Alright? Alright, they will say so. If I will run now go back and going to the reports of pages in screens right now, let me just show you the DoubleClick first two conversions. And then going back to pages and screen, the report should already be modified entirely to the way we just created it, right? So we don't see any charge there. We only see the different dimension we select it and also the metric is we select it. So in this way we can pretty much completely customize the report. We see. There's one more thing I'd like to show you in this video. And it's when I go to Report and the weight for a second. For some there is, sorry, I minimize that. There is a library. What it does, as we can see, we still have quite a lot. As you can see, you have two life cycles here, which is probably a box. So if I will refresh it, it shouldn't appear there. Sometimes happens with Google Analytics. So yeah, now it's only ones there. We can see kind of a bigger group of reports, which is lifecycle, then there is a user, and then there are e.g. a. Report snapshots and real-time. If we go Then tool library, there's another level of customizations we can do. We can either completely reorder those main tabs or delete them or add their specific report we want to. So that's something that wasn't available in Google Analytics or in Universal Analytics. So e.g. if I will scroll here, and for some reason I wouldn't want to see the tag or this technology tab over there. I can click here on Edit collection under the user. And if I want to completely remove it from here or any, or just let's let's remove only the tech overview to show that it works. If I will remove that and click on the Save. Again, waiting for a second, and it was set in the collection. So if I will go back right now, that tech overview shouldn't appear there anymore. So c, we basically changed the interface for all of the users. So this is a great thing. If you want to just customize the interface in a way that we only want to see the data which we want users to actually consume and not being confused. On the other hand, we can create basically any report we want to and edit also there. So this is, this is also something I would like to show you right now. How to do that. Again, I will go to library and I will create one custom report, which I will add there. And I want to add it under the engagement. I need to create a new report, which is by clicking here. And let's create a detailed report. First of all, I have to select based on which on based of which report I can I want to create it. I can either start blank, but for the purposes of this video, I will start e.g. for four pages and screens. So let's wait for a second until it's available here. Basically modifying one of the existing reports. So it's a duplicate. What I want to show you how we can add a new tab into already existing bigger Tap of reports. So let's assume I want to leave there a line chart. I want to see e.g. I don't want to see this one here. Neither this one, neither this one. And I only want to add there. The one that I'm very familiar with, which is e.g. can be a landing page. So we're basically recreating the landing pages report. So I will remove this one. I will leave only that. I will click Apply. So let's wait for a second. Okay, clicking here, apply. And I want to add a different, okay, you can see it's already showing in real time how the report is changing. So again, for the sake of the videos, I will remove all of those metrics I'm not interested in. Then I will add their sessions, which will which will tell me how many sessions have started with that particular landing page. So I will move this one as the very top. And as we are used to, Let's also add a bounce rate just to see that it works. So here it is. A simple table showing me, showing me the top landing pages by the sessions that started there. So I only want to see line chart. As I said, this is my report template. If I wanted to, I can add another card, but that's not the purpose of this video. So here we are. I will right now click on the Save. I want to name it as landing pages report. Here it is, saving it. And again, it takes a second or two, OR gates saved right now. So I have to go back to library. We are, I want to edit this collection, as I said, and I want to add this report into that engagement part. So what I have to do right now is to scroll, scroll, scroll, scroll, scroll until I see the report, the custom report I just created. And right now I will only drag and drop it where I want to see it. So by clicking Save here into the current collection of reports. Okay, it's there. So by going back to the standard reporting and clicking on engagement, I can already see that landing pages report I was talking, I just created right now and can see it the way we built it. So this was the example of what everything is possible to customize. So I recommend you to start playing with that, since it's possible to create quite a lot of reports from what we were used to, from Universal Analytics. And this is exactly the technique, so hope you like it. And this is, this is the way Google Analytics for is built. It's a lot about customization. 30. GA4 - Tips - Browser's language: So a set, Google Analytics four S and probably will be for quite some time about the customisation, which means that we will have to create the reports that will give us insights by ourselves. Let's start with the upcoming Tips and the real examples I'm going to show you because most of the ****** will be based on the custom reports. How to do that ledger? Simply try and see the first one. We're going to explore section. And we're going to build one from the very scratch. So I'm clicking here on the blank report. And as we know how it works, we first have to define a couple of dimensions and metrics we'd like to see and use them in the report. So I'm starting here and I will use just a few to show the first step, which will be while the Browser's language. So let's assume I want to give her browser as a dimension. I won't have. Language is the second one. You can just pre-select a few of them and then click on the important. You don't have to import every single one. So let's assume that those two are just fine for the first step. And now I'm going to pre-select a few metrics. I'd like to see, which definitely. First is users, I'd like to SI sessions than I'd like to see bounce rate. Here it is. And I'd like to see, for example, engagement rates just to see that it works. And here I am importing that and now I'm going to build that customer reports. So first of all, and this is actually the first tip, tip number one, which is about the browser language. The dimension name is language. So I'm either double-clicking here, which is what's happening right now. And I'm also putting here the values which is total users. I want to see also sessions and for example, bounce rate. So let me know, prolong the time window to let say one year. So I will do it from the May 2022. I will apply. After I see the data, I will a little bit change to the cell type because the bar chart doesn't tell me much, but I want to see only the plain text here. And what I want to show you, this is the very simple report actually, which is showing me the Browser's language. And it's important to remember the word browser. This is actually the setup that every user does in their browser. And all it tells us is probably their native language. So even that simple report which tells me that, okay, about two-thirds, yeah, 2000, 3,000 of my users have their Browser's language setup as English. And the rest of them have something like this. In my case, it's quite August and most of the people who are reading my block will have their, their language setup in English. But what is importantly are the, are the following lines which Italian is quite interesting for me and something that I wouldn't expect that I have. The line number two in my case will be will be telling than German russian check from a check. So, so yeah, that's expected. And so on and so forth. We can even see more lines if we want to. But what is important? Why is this tip number one? Try to imagine that you are running a multi-country or multinational business. This reports simply tells you from where your customers are. So feel free to use this report when you're thinking, for example, where you should expand your business or potentially to which languages you should try to translate your website. So it serves natively to the customers and their own native language. So actually, very simple thing, very simple report but providing the first business inside you can use for your daily operation. So that all steam tip number one, which is about understanding what your users browser language 31. GA4 - Tips - Location data: Another set of tips will be based on the Location data, which in case of GA4 are dimensions called country and city. So we still have here the previous example. So to speed it up, I will leave all the metrics we have there. And I will add two more dimensions, which I just mentioned. So clicking on the blast, searching for the City. Here it is, and for the country. And there is also the country. I will import that. And I will immediately exchange the language ahead for the country which already is here. But just to show it works, I'm drag and drop it here. And here we are. Instead of language, which is something that is being extracted from the user's browser. The country is something that Google is taking from the IP address location. So it's something slightly different. And it helps you to simply recognize from where your users or if you're a business business-wise, website customers are simple thing, but worth knowing if you are especially multi-country from where're customers are. How to, how to actually imply that based on the location, simply think of where to run the campaigns both online and offline because you want to be where our customers are. So I'm not inventing the wheel here, but very simple technique telling you where customers are or users if you're non-profit. And this is exactly the brace place where you want to target them both online and offline. So this is the reason, or this is a technique that should help you to target your budgets. The most effective, in most effective way. So this is, this is the tip number two, do to targeting based on where you're customers are. If you are based only in one country than it's definitely worth to replace that dimension country for the city. So we can either do it by filtering here particular country and then adding there also second, second dimension into the row as a city. Or we can just simply to show the technique, replace the country for the city. So this is probably more useful when you're single country business or site. So instead of seeing in the more global way, which is, which is worldwide, we are right now looking on the particular cities. And again, exactly as in the case of the countries you want to be where your, where your customers are. So for example, on the council level, you might think, and this is actually tip number three, where to run your online versus offline campaigns within the single country. Do you want to be in a big cities or in small cities? This is definitely something worth knowing because also the people are probably living in the big cities have slightly different behavior, or I would expect to have slightly different behavior than people from the smaller cities, from the towns are from the villages. So if you're based in one country, you will probably know just by looking here on the report, which of them are larger city, which of them are, are smaller towns? So again, very important information. And all these series of these first steps is the goal of those tips is to help you understand who your persona or customers or users or whatever the fancy name we use for. The point is to understand who they are, where are they from? Where do they live, and so on, so forth. So actually, there were the two Tips and I'm getting one more, which I would say, okay, let's let's call it as a tip number three. But if your business, which is also having the offline part of that, let's assume we deliver something or you own something in an offline. You might do the analysis, for example, where to open your pickup points network because you want to be where your customers are. So for example, if Prague is number one city, I definitely want to be thinking about opening the pickup points. That will be probably place number one where I would like for to have to have my APM network or pickup points or the place where I physically want to be present, where my customers are. So simple, geographical adolescents, but I would say very, very precise data to help them when you're when you're making a business decision. So that was tip number 2.3. Number three 32. GA4 - Tips - Browser conversion rate: Alright, for another set of tips, I will switch to another Google Analytics account, which is Google merchandise store. And the reason for that is that I would like to show you a couple of real examples based on the Ecommerce data. So I just switch there. And exactly as in the previous case, we are going to build a couple of reports from the scratch. So I'm going to to explore report and I'm starting fresh on the blank one. The next few of them will be based on the Browser's on the Browser's named data. So let's start again with clicking the first couple of dimensions we want to use. So the very first one will be Browser, which is completely enough for this particular tip. And again, let me select a few metrics. So I want to see total users. I want to SI, sessions. I definitely want to see bounce rate. And now we're going to a few e-commerce metrics. So first of all, as Google merchandise store has implemented also e-commerce and measurement, measurement. I will see there also Ecommerce data, so purchases, this is something guy you definitely want to see. I want to see conversion rate, which is finally there and I'm using the session conversion rate. And I would like to see the e-commerce revenue. So here we are, and here it is. Let's say, Yeah, we can use this one is it's exactly the same to purchase and the e-commerce revenue or should be the same. So what importing this, this metrics. So first of all, what I would like to see, I'm building the report. So again, double-click on the roles and I'm adding the metrics, total users. Now, sessions, definitely bounce rate, purchases, session conversion and e-commerce revenue. So let's wait a couple of seconds until all of them are visible. Yet here we are, again switching from the bar chart to plain text. As we can see right now, it's pretty simple report. So far. Let's ignore the, the conversion rate here. I'd like to show you something different first, this is another tip, tip number five, which is just looking on what are the most popular browsers were between your users. In the case of Google merchandise store, it's definitely not a surprise that number one is Chrome. But then there is a safari at Samsung Internet, Android review and so on, so forth. Do exactly the same thing in your case and try to understand what are the popular Browser's. Just the browser itself is a characteristic of the user. If your users are using modern and new browsers such as Chrome and Safari, they are probably a bit more advanced. The technical users that then for example, someone who's using Edge, which is pre-installed on the Windows devices. So again, very simple, very simple data, but definitely worth checking. And also try to check, which is another tip, tip number six, whether you're speaking the same language as your users are. And by language, I don't mean English or German or Spanish, but the tone of the language you speak. So if you find out that you have a geeky users, so I'd know a lot of Safari users try to also speak a bit more maybe geeky language to them. Something modern and fresh. So something that might be a little more familiar to them. So this is purely about, about the interpretation of the total volume of users. Right now we're going to have a look or have a closer look on the conversion rate. Right now, as you can see, there's very high conversion rate, something like 90%. Because Google merchandise store have, have marked quite a few events as conversion. So for example, even viewing a product detail page can be a conversion. So this is why so many. This is why so high conversion rates. So we want to focus only on the one that is based on purchases. And now a simple technique how to get that. It doesn't only have to be purchased. So if in your case, for example, your main conversion is something like a file download or sending a contact form. So you know which particular event it is, the exact same data, same, same filtering because I will show right now we'll apply on your case. So what do we have to do here is to add there one more dimension, which is called event. As we already explained couple more times. Google Analytics is event-based Analytics. So right now we have here also event. And if I want to calculate the conversion rate only for particular event, I have to go to filters here And I need to filter the events. So in this case, I want to filter using regular expressions. And I want to filter all the, all the events are only events which are called session start. Here it is, which will count basically all of the total users and sessions, which is exactly what I want. But right now when it comes to the conversion rate, I only want to have it calculate it based on the purchases. So this is the second event I'm going to filter, which means that this, this sign means logical OR so I want to filter all of the, all of the events which are called either a session start or purchase. So if I will click Apply right now, try to focus here on the right side you will see how the conversion rate will be lower. So clicking Apply here, waiting for a second-order do. And we should see the data, okay, here we are. Right now, we can see that the conversion rate is something like a half percent right now. Maybe let me prolonged the window a bit more, something like here because I'm not sure if they if they measured it properly in the recent days. Again, secondary to Okay, Anyway, the point is to understand how to, how to filter the data in a way that you can look on the conversion rate just for the particular event, which was exactly the case. And now how to connect it with browser data. We see the first loop I would have is definitely to see what is the conversion rate average, which is 0.9. And now looking on the top lines here, so we can see that the conversion rate is slightly above the average for the Chrome 1.1. But we're looking in line number two, number three, which is Safari an edge, we can see that the conversion rate is not even half comparing to what isn't on the Chrome. So this is something definitely that's spots my intention. And it says this is another tip. So do the same exercise in your case. And if you will see such a difference between, between the conversion rate and the Browser's. The first thing I would do is right now to open the Safari browser and try to play in the Safari for a couple of minutes to see if I won't be able to spot it. Something that something fundamentally doesn't wrong there. And either, either do the same exercise on the Edge browser because it shouldn't be like less than half, less than half conversion rate or in this case, of the edge, almost all just one-quarter of the commerce is right here. So it's quite likely that something is broken. It can be that I don't know. The the Add to cart is not working properly or you can go through checkout or some validation doesn't work there. Sometimes it's just like a small thing that can block the users in order to complete the purchase. So it's definitely worth checking and spending there a couple of minutes, you might find quite a lot of money in the table just by fixing maybe a small issues. So this is another tape. Feel free to do this exercise, super simple but super, super brushes data. You have. So that was a couple of tips regarding the browser data and the combination with the conversion rate 33. GA4 - Tips - Device category: Let's go to tip number nine, which will be again based on the Google merchandise store data. And this one we're going to do based on one of my very favorite dimension, which is called Device category. So we still have the same reports we have in the previous tip. And right now we're getting to add one more dimension which I just mentioned, which just Device category. So let me find it here. Device category, here it is. It only has three lines, but super-important to me. So let me exchange the Browser for Device category. We are again, super simple report, but one of the most business, possibly changing one. So let's ordered based on the total users. We are still using the same filtering for that event name called session start and the purchase. So we also can evaluate it, but actually to do tips here, first of them, tip number eight is just checking what's the ratio between your desktop and mobile traffic. As I told you, this dimension has only three values, desktop, mobile and tablet. And it tells you on those devices are user browsing your website. So as we can see in this case, it's almost 5050 between mobile and desktop. So the first information in the first tip number eight is actually to check what's that ratio and what's the, what's the implications your business is, whether you pay enough attention when you're designing something. It means any feature, any innovation you do when it comes to user experience should be at least equally designed for mobile and desktop. And my recommendation is to design it only for mobile. Make it first working on the Mobile good, and then do the desktop version of it. Because actually 50, 50 nowadays is something that I would expect to be even more on the mobile. From the region I am from which is Central Europe, the penetration of the mobile devices and the ratio between mobile and desktop is something like 72, 30 in favor of Mobile. So actually 50, 50 is still something that quite surprises me that that is still a bit more of desktop users than the mobile one. Yes. You might argue with me that. Okay. But they are looking probably on the on the merchandise store at work or, or, or they might have some other reason. But still, even the 50% of mobile should tell us the very direct information that guys, we should design everything we do, particularly for the mobiles. So this is this is tip number eight. Tip number nine is again just looking to the right side of that report and looking on the conversion rate. And I probably don't even have to comment this one because we're looking on the conversion rate difference between the desktop and mobile, which is 1.6 and 0.3, which means that the conversion rate than the Mobile is not even one-fifth of what is on the desktop. There's definitely something fundamentally wrong when it comes to user experience on mobile devices. So this is something that should there should we, we should really put spot on and try to dig in deep. This will be the part of another tip. But first of all, literally looking on what's the difference. My expectation for a good working mobile experience is that the conversion rate comparing to the desktop sun should be something like 80 per cent of the desktop conversion rate, which, which in this case I would expect to be when the desktop is 1.6, I would expect here to be something like 1.3, at least, not 0.3. So there are definitely something wrong. And looking on the tablet 0.1, that's probably also something it now the very, very well working when it comes to the responsive part of, of the GA4 of the Google merchandise store. So we'll dig in a bit more deeply to understand it in the next day. But this one was super-simple about the Device category and looking on the share of it. So we can design everything based on the share of dogs of those Device Categories. And then looking on the conversion rate, whether we shouldn't immediately focus only on the Mobile, which in this case we probably should. So another two Tips and we're going to dig in a bit more into the mobile experience. 34. GA4 - Tips - Mobile device and screen resolution: We continue to play with device category data, uh, said we're going to dig in a bit more with mobile device performance because as we can see, we're continuing with the same report configuration. And it tells us that the conversion rate on mobile devices is 0.3, whereas in the desktop is 1.6, which is five times more. So there's definitely something wrong. We just need to figure out what. So what I did, I already added a few more dimensions here which I will use, which is screen resolution device model device, and mobile Moodle. The rest of the report is still the same. What I want to do as a first thing here is to keep only the mobile line, which is easy to do if you just click with the right and you include on this one in selection. So, so it's pretty much the same thing as you would, as we would filter it, for example here, it's automatically filtering that. And what I want to do right now is to add there another dimension which will be screened resolution. What I'm trying to do is to find out whether there are some of the screen resolutions which are performing on the mobile device is better than the other ones. So what do we have right now? The report right now is broken down by the, by the various screen resolutions that we have. And what I am interested in is the conversion rate. So we see that the average 0.4 we can see, okay, slightly above, slightly below. This is very below, which is 362 times 800. Very unusual resolution. Going down. If I won't put something interesting, which is here, for example, line number 11, which is 393 times 852. So this obviously performs high above the average. And I can scroll down a bit more to see whether there's something that gets my attention. Again, there are a few very low, some of them very high. The point here is, as we can see, that the demon and there are quite a huge differences between the screen resolutions on the mobile devices or their recommend you to do. Here is to try to simulate that screen resolution and try to play with your website to see if you won't be able to spot as the buy if you want, maybe a sorry, if you won't be able to spot something wrong just by playing with your website. How to do that, it's actually fairly easy. I would just go here to the Google merchandise store. I'm here. You can easily get there by either Googling that or you can see the URL here. And for simulating the mobile device on the large display, I'm using the add-on, which is called a mobile simulator. I already have it installed. So when I click on it, it automatically assimilate me. What's the mobile experience so I can play with it. You can actually do, it's quite cool. You can select from quite a few devices available here, both Android and Apple, even a few tablets or even the specials like Apple Watch and galaxy fall to my MacBook Air and so on. So the ideal way is to, is to try to simulate the exact resolution or try to try to understand what a particular resolution can be. So as we saw, the first thing was that the general conversion rate on the mobile devices was very low. So the first thing I would do is to just in general play with the mobile experience and try to see if I won't spot something wrong. I'm just just by first side. I can see that it looks quite strange. I can't say that it looks super cool or super, super great on desktop. But even here on the mobile, just the alignment of the basket on the very right. Looks that looks like a broken website. It's like not not done properly. When I'm trying to scroll down, I was playing with it previously. So I'm showing you some of the things that instantly popped up to me when I'm trying to click on on any picture here, which is kind of a promotion of bike collection or drink where it doesn't do anything, which is something I would expect to do. And the only thing how to get further is by clicking on that Shop Now title. It took me a while. It took me awhile until I understood that did I have to click only here to get further. So when I, when I did that, There's another thing I spot it. Which was that the website was on the mobile device is definitely slower than, than on the desktop one. I will show that in another set of tips, but this is tip number ten to try to simulate that mobile experience. And it's probably that it will find something, something that doesn't properly work. So just by playing here again, I would expect the Add to Cart button to be, to be constantly visible, which is kind of a mobile best practice. Which is not the case even though I'm using, using the iPhone for which it should be optimized. This is another, another thing I would probably try to fix when I would be UX designer. But the point is to see from the data that there's probably something wrong. We won't be able to see that exactly. This is the thing we need to fix, but we know that there's something wrong with their mobile devices. Just so just put his information to your UX designer so he or she can play with the website and try to find out what the issue can be. And the second thing I wanted to share with you from that report is try to look for particular resolution if you will see that your conversion rate will be quite good. But then if you dig in a bit, you might see that some resolution is performing either far better or far worse than, than, than the other one. And how to do that? There's also another way how to simulate it. And this is what I'm going to show you. Right now, cancel this, this browser add-on. And you can basically create them manually. The exact resolution you'd like to see how through death. I'm using Google Chrome. And if you just do the right-click and you click on the inspect, there is a small icon on the top left corner, this one toggle device toolbar. If you click on that, it'll become blue. And from now on, if you go back then to the browser, it will start a loop like this. So this is actually the simulation for iPhone SE. So if I click here, I can either select from one of those predefined devices here. I can even simulate some, some slower connection. For example, like low-end mobile know throttling, which is something that I have right now. You can also play with the different standard resolutions and see how the website looks like on a small mobile, on a larger mobile while. So that's actually actually quite good to see. Let's now go back to, to iPhone SE. Or you can, as this is what I wanted to show you in a part of this step. Simulate particular resolution if you will find out something interesting for you, how to do that, it's not possible just to click here and edit this number. What do you need to do is click here and click here on the Edit. Again. The console will open and you can either occur at one of the devices which is available here, or you can add custom device. So if you all right, now put here something like, let me just show you that the 415 times 850, just like using the random number, just to show you that it works. And I will name it as a bobbles device. I will click on the Add. Here is already pretty clicked. Right now. Go, go back, which can be done by this Glick. And going back to, sorry, going back to what we're showing, I can right now. So as you can see that between the devices that is already here, there's also problems device, which is the one we just created, 415 by 150. And right now I can play with that. So do the same thing if you find something interesting between the specific screen resolutions that will help you do to see if there's either not something super grade or super wrong. So we can simulate the exact experience that your customers are having. And you might spot that there's something wrong and it's potentially and hopefully easy to fix. So that was that was tip number ten, tried to simulate the same thing 35. GA4 - Tips - Page speed insights: Okay. And we're going to tip number 12th. And this one will the probably the only case where we will show most of its usage outside the GA interface. And it's because the data that you historically used to be within the GA interface, particularly in the Universal Analytics version, are not normally available there. And I'm speaking about Page speed. So historically used to be very useful within, within the interface, but it's not the case anymore. So Google decided to provide this data outside GA in a separated product, which is called Page speed insights. So all we have to do is just to type into the search Page speed insights. And here it is, here is the name of that product. It's Page speed, dark, VAP dot, deaf. And how it works, It's super simple actually. So all we have to do is just to enter a webpage URL, which we want to analyze by Google algorithms. So what I'm going to do here is just exactly what to expect. I'm just copying the URL, I'm pasting it here, and I'm leaving Google to analyze actually its own website. And here we are. The, the report is divided into mobile and desktop version of it. And we can see that even which is quite funny. At the same time that even though Google own E-sharp is very poor when it comes to the Page speed insights, the largest contents will paint. Is it 4.2 second, which is which is too slow. So it wasn't just my personal observation that it's slow, but it really is the case. There are, there's also a set of other, other metrics which are important, but S4 now, the google is claiming that that LCP largest content for paint is the one that also goes into the search ranking algorithms as the input. So try to optimize for that metric. If we scroll down a bit, we can see even more detail here. We can see that the largest comfortable paying takes 15 s to load. So that's a very, very pool. So Google, thanks for the tool that we have it, but also try to optimize your website according debt that the recommendations. And it'll be much, much smoother experience that it is right now. So feel free to also scroll through that. There's quite a few insights that the tool is providing you. And they are very concrete, which means that if we see here properly sized images, if we scroll down, it's actually telling what is, what is so slow and what takes so long to load for the users. So it's quite extensive report, but definitely worth checking in trying to optimize your your website according to that, because it'll help you in getting the organic traffic because the speed as his Google claiming on probably every conference they are attending, that the speed is one of the key factors. When it comes to the search engine results page. When I, we'll compare that with the desktop experience because what we have here is that largest content for pain and the first content pull paint. And as we can see it, it was 12.3 s, which is literally terrible. Comparing to the desktop, it was 2.9 and 3.5. So as in the previous, previous step, in the previous tape, when we were showing the simulation of the mobile experience at a desktop, I was claiming that to me personally, it seems slow. Here is the data that is confirming that fact that it wasn't just my personal observation, but also that the, the fact, so again, the same set of opportunities and recommendations. What should Google actually do with the Google merchandise store? But do the same thing with you for your website, the tool is for free. It's super easy to use. And you can actually get quite a lot of improvements if you speed up your website. So that was, that was step number 12. Use Page speed insights, which is the data that are right now in the separated product. Historically they used to be within the Google Analytics interface. So that was it. 36. GA4 - Tips - Mobile operating system: We continue with another another tip, tip number 13, which is about adding some additional data to the mobile devices. This are the ones that are not only the future, but actually all already a present experiencing websites. So we're going to dig in a bit more deeper and I will explain you why. First of all, I added two more dimensions here to our report, which is devised brand and operating system. And I added one more metric, which is the average purchase revenue. So first thing I will do is that I will add the average purchase revenue as a metric by double-clicking here. And we can see it appeared on the very right. And what I will do right now is that I will break down the device category, first of all, on the level of operating system. So double-clicking there, you can see there are only two lines, of course, Android and iOS. And what do we see here? Again? This is the report that will that will or the tip number 13 that will help us to understand who is our target group. Which in this case means, are they more of Android or iOS users? Yes, this is the good thing. And also one of the, one of the beginner's mistake that there was a yeah, yeah. Android. These are the people who should focus on I wouldn't be so sure just because there's more of them, I will try to see more contexts of the data. So for example, when then looking on the conversion rate, I can see that Android people are actually having just 0.3 conversion rate another mobile devices, whereas iOS have 0.5. So I would say that maybe this is the target group we should maybe focus on or we should try to get more of them because their conversion rate is naturally higher than the dendrites. So this is again like seeing the two lines report telling us much more about who our users are. So if i would be the one who is supposed to run the campaigns for new users acquisition. This is where I would focus definitely more on iOS users then an Android because I can see that they are converting more. What is even more important? They are willing to spend more when they, when they purchase something, right? So the average purchase revenue or average order value, as we were used to from the Universal Analytics is $50 for Android, whereas it's 70 for iOS. So this is definitely something I'm much more interested than on the Android. Of course, there can be many reasons for that. But as looking on the quiet large group of users, I would say that this is just the natural behavior of iOS users on the Google merchandise store. They converting with a higher conversion rate. And if they convert, they are willing to spend more money. So again, very simple tip, but definitely with the business insight. So just by comparing this number, we can see that the ratio between Android and iOS users is one-third to two-thirds. But the revenue shares 5050. So this does quite a lot. And if we want to dig more deeper, if we want to, we can just exclude the operating system dimension and add their device brand. So definitely more lines than in the previous case, but basically helping us understand it even, even more. So. We can see that most of the users on the device bread brand dimension breakdown is Apple with the highest conversion rate and the highest average purchase revenue. I can see here that this is 98 from, from Huawei, but this is only only one purchase from that. So I wouldn't consider that as irrelevant and always do the same double-check if you see some, some outlier in the data. So also feel free to do the same thing on the device brand level. But the important information is that actually the group of users we should focus on are the mobile devices on the Apple, because they have the highest conversion rate and they spent the most money when they purchase something. So again, just to repeat, what was the point of this step is to understand who our target group of users is. Who is, who is willing to convert the best, utmost level and is willing to spend the most. So it's definitely the mobile, the Apple mobile users. So again, feel free to do the same technique to find out more about your users 37. Tip 33 34 Linking GA4 with G Ads and Search console: This step will be rather quick but fairly important, I would say. There are a bunch of the data in Google Analytics and some of them are available once we do something that is called product linking. What do I mean right now are two particular cases. First of them is the ability to see on which particular keywords your website was displayed and potentially, then the click occurred from the organic search results. The tool from Google, which is storing this information is called Google Search Console. And there are options to either have a separated view when logging to the Search Console, or you can link the Search Console with Google Analytics, and then the data from Search Console would import directly into the GA four. So the data is then available in the Search Console and queries. So just to show you that it's there, I'm going to report, and these are the search queries that then appear within the interface with ability to see how many clicks occurred from somebody searching, for example, for Google Cloud certified merchandise. There are a bunch of keywords being listed, but in order to see data, and this is what this tip is about, we need to link Google Search Console and Google Analytics. Then we will, of course, analyze it over the course, but this is the prerequisite to see the data. And the similar applies when you're running paid ads within the Google environment, which the tool is called Google Ad, and there's also a dedicated set of the reports to that, and one that is in particular or should be in particular interest for all of you is when I will show you the data under the planning tab called Google Ad because once you interlink the Google Ads account and the Google Analytics account, you will start seeing also the spend data or the ad cost data exactly in the interface. Which is something that you definitely should and want to see because if you're spending for something, you would like to see then the effectiveness of such a spend, right? So once you interlink, again, Google Ads, in this case, and Google Analytics, you will start seeing this data. And as you can see, for example, just like brief look because this is about how to interlink the tools. You didn't see the ad spend or the ads cost. And what was also the cost per one click? What was the total revenue, and what was the return on ad spend? So this is something towards majority of the advertisers businesses are optimizing for. And now, how to link that? As obvious probably, you need to go to Admin section. And there's a prerequisite that you need to have admin access to the Google Analytics account, as well as to Google Search Console and Google Ads account. So how to do that, it's fairly simple if you have the access. So I'm just showing you the way you need to go to Admin. And then on the right side, on the very bottom, there are various product links. All of them are Google's product, but we were discussing the two of those, which is Google Console Search Console link, which is by clicking I have disabled this option because, of course, I don't have admin rights to the GA four of Google Merchandise store, but it's like the fairly easy, easy couple of steps. You just need to have access to both. As I said, admin access to both Tool Search Console and GA four. If you would then click on the Link, a couple of steps and you have it interlinked and the similar applies as well. If I would click right now to the Google Atinks you would see if this is actually a good check. If you have it enabled, then the link button will be blue. Similarly, as in the case of Search Console, you would go through a couple of steps and you would have interlinked. What is a great thing is then the data will retroactively appear in your GA four account. So this is actually pretty cool and wasn't the case in the past. So yeah, feel free to do that. You will enrich the data you already have in the GA four. 38. How to use UTM parameters: UDN parameters are used for traffic trekking across all traffic platforms. They are nothing but perimeters. We have to add to the Earl of Landing Page address if you want to travel. It's a BBC campaigns newsletter, social network posts, book boasts and so on. It means all kind of traffic except direct and organic. One. There are five of them. Source. Medium campaign content In term, let's have a look on a couple of examples of how it's used and show it to that will help us to create you tm parameters correctly. So, guys, we're going to show how to use you. Tm parameters. Maybe it first small window from the history. You might wonder what actually you tm abbreviation means on its urchin tracking module. Urchin was a company which waas a predecessor off Google analytics, and it was acquired in 2000 and five by Google. And then he was lately ah developed Ah, as a Google analytics, but for which we are using right now when we love it. So this is what community means. It's still used. So, uh, you, tim parameters enable us to distinguish between various traffic channels. Let's assume very simple scenario we have this your address, which is my website https. Probably thing that sees that slide hyphen slash e and slash and I'm running and paid search campaign in Google Edwards. And once I want to see this traffic and Google analytics, that is the this traffic iss exactly from this campaign, I have to take it somehow. And how I do that is by adding this small fractions off text into finally rural address which will look like this. So by only having this one, we would see this traffic as direct one, which is not ah, because it's from this paid campaign. So this is what we are heading here. The beginning off the final you're our address is the same https about Brett sick that sees at slash en slash And then there is a question mark beauty in medium, equal CPC you teams force equals Google and you team campaign equals g a course. Don't more Now about the question mark underscore signing equal sign because we're going to explain it. Ah, lately and what is more important, we will show a tool that will do it for us. All this necessary special characters in there. So this is what actually UDM parameters are. And now let's repeat them. There are five of them. Source, medium campaign content and term the quick explanation off what every perimeter s and what should be its value. So the 1st 1 you tm source, it's mostly the domain or at platform Name s O, for example, if we have ah banner campaign on ah, New York Times. So this is what should be there as a domain New York Times Or, if we are running the campaign in Edwards, who are there, can can have their Edwards or Google. So this is what we should Ah, but there, Then there is a 2nd 1 You tm medium. This is ah, always on at type. So if you, for example are running a banner campaign, we should have their Benner as well you or if we are writing a Facebook post than there should be opposed. Or we are running a paid search campaign there for B C B C, which is the most commonly used abbreviation. Then there is 1/3 1 you tm campaign. There's always ah campaign name. Feel free to basically use anything there but try to use something meaningful and the reason for it? It's We're simple because once you will do some comparison, I didn't after a couple of months try to name it somehow, easily to understand. So we don't have to think for half an hour. What exactly was this campaign about? So try to use it something in in a way that he will easy understand, like at Easter Sellout summer campaign, school campaign. And so long. Then there is 1/4 1 you team content. This'd is Ah, I would say, the broadest one. There are no rules or recommendations what should be there? But mostly it's for add details. Uh, which, for example, tried to imagine we are running. Ah, better campaign somewhere and we have multiple banners and we want to find out which one works the best. Which one brings the most traffic, and then which one converts the best and so on. So this is the perimeter that helps us to distinguish between those those banners so it could be just some small naming off the banner like blue or red. Or there can be a vendor size or better damn engines or creative name or something similar , so feel free to use it. It's the broadest one, uh, and play with it. And then there is the last one called you TM term, which is dedicated for keyword and key. Word on Lee. Please try to remember this one. It's quite common mistake that people tend to put their something that does not belong there. It's on Lee. It's It only should be used for bait search campaign. And there should be a key word on which you were, which on a cured on which your adults sold. So this is the only type off campaign where you should use key work where you can term perimeter, and the reason for it is were simple because there's a dedicated report in Google analytics for it. And once we put their something that don't belong, there will just create massive number data. So please remember this one. You, tim, term is on Lee used for paid search campaigns. So, uh, now we're going to show a couple of examples off how the finally our address, including you, Tim Parameters may look like so the first example. ISS. Let's assume I'm earning paid search campaign in Google. And this is how the URL address should look like the 1st 1 you tm sores, which we know is the either dooming or at platform name. I'm running it an Edward, So I'm putting their Google, which is a Google platform, adds type is it's ah, search campaign. So the most common ah, medium value for it is CPC, which is a cost per click can either is BBC, but those two are the most common. Definitely. I named this campaign as a G A course because I'm running a couple of more thin. There is a U team content, which is the broadest one we know on, and it should help us to encourage the information about the campaign. And I'm putting their benefits because once my ad is shown, I had there a couple of words describing the benefits off this course. So this is why put their benefits and then the last one, you tim term keyword on a I. This this ad was shown for a cure Google Analytics course. So this is why I put it into cure because it's paid search campaign, and this is how the final your address will look like? So all of the U team perimeters are there again. Don't worry much about the structure off it all of special character, because we're going to show a tool which will do it for us. So, uh, that was a bait search. Another example. Banners. Let's assume a scenario that I bought one million impressions on on the reddit dot com. So again, we're going todo start from the u T M source. So I said I bought it on a ready dot com, So I'm putting their A domain name a Z u T M source, which is Read it. Then there is a style which we know it is a form of the ad, but we shown which is a banner you can either advantage or display for four banners. Ah, better campaigns by the noble 20 prefer anyway, he was consistently. Then there is a campaign in which is, in this case still the same G course and the U Tim content, which is for a detail. I put their the dimensions of the banner 200 to 200. So this is it because I'm running multiple banners. The wider ones, the bigger ones so I want to distinguish between them. And then there is the last one. You Tim. Term keyword, which is empty because, as we know, it's only used for paid search campaigns. So this is how it looks like. Ah, we only have four parameters here. Ah, Now, uh, or currently there's only one off parameters that is mandatory, and it's ah, you team source. So we the minimum off parameters We use this one, which is a damn source. A couple of years ago, there were three of them. Source medium campaign. Now it's only source. So ah, don't worry that we don't have you 10 term here it will work. So that was Ah, menu example. Another one is, let's assume social network activity. Different scenario. Let's assume I have a couple of thousands off followers on the Facebook and I'm writing. There regularly posts about analytics. So again it's on the Facebook. So we use a Facebook, which is the domain as you team source at stop. This one is the post, so this is it now. We are still in the same campaign J course, and there's a detail. I've put their benefits off tagging because in this post I was describing How good is it to tax some campaign? So this is something that we're doing actually, right now. And this is how our final you are all dress will look like with this you, Tim parameters began. Keyword is empty because it's not the paid search campaign. So social network activity So newsletter is the last one in this case, this is slightly more specific because, uh, there is no specific domain we have or a platform name. Right? We sounded. And we There is no website on which newsletter is displayed or some specific platform name . Right? So this is why I would also year, because there's, Ah, this is this is definitely the most common source and medium, uh, values which are used for a newsletter. So there's almost always a newsletter in a source and in a medium email on as a campaign name, I used their date. Uh, let's assume a scenario that I'm sending a new letters regularly. Let's say every two or three days. So date is definitely the easiest form for me to Teoh distinguish between different newsletter. So this is why I put it there and as, ah, you team content I use here g a course. Ah, you might notice that in a previous examples I use G courses A campaign name on this one. I have it as a U T M content. Onda had a reason for that. Try to imagine off how a newsletter can look like there are multiple multiple pictures, some tax logo, maybe navigation on with user can click and then will be linked to my website. Ah, and let's assume I have their some information about a new block. Boast, I have local there. Uh, I have my recent courses that I'm, uh, are, let's say, public lectures that I have. And then I have one short calm there about G a course about this one that I'm preparing on . I want to see of how many people actually click on it. So this is why I use g a course a za part of the newsletter. Ah, as a u tim content on again, as as in previous two cases. Ah, beauty in terms is empty because we this isn't the bait search campaign. And again, this is our final. You are all address, including beauty and parameters. Eso this were for examples. And now let's have a look on couple off deuce and don't, which is also a tip Number 25. The first don't is Please don't use that. Critics thes are the special characters, which England doesn't have many of them, but especially if you live in a in a central Europe or specifically if you some Slavic language. There are a lot of special characters which, if you will use in Eugene parameters, will be translated into something I would say not reserve flight friendly form, which you go on loose any traffic. But you will see some very strange characters in there won't be easy to read. So if you don't have to for some specific reason, please don't use that critics, please use them consistently, which is something we will describe in a couple of minutes on. If you want to have a blank space in Google analytics, which is especially good for for keywords, please use the plus instead of it. Google analytics then will translate. Plus is a blank space in the interface. Ah, you might notice that I use the blast in the first example, So let me just quickly go back there. It was in a bake search and in the you can come here, which you can see. I have their Google blasts analytics blast course, which then will be translated in Google analytics with the blanks basis. So yeah, please use it. Ah, if you for some reason use a blank space in the game parameter it won't work because you 10 perimeter. It's part of urinal address. And the blank space is not a supporting character there, so it won't work. Please don't leave any of parameters empty. As we said, only UDM source is mandatory. Perimeter wants to use it. So, uh, if you want to use only on Guilford to do that even though I recommend to use at least three of them Ah, please don't leave it empty, right? So don't don't do something like it's shown here that you will have you tm medium equals nothing. It might happen that it won't work and it would be a very stupid mistake. Ah, not being able to to find the tax strafing just because this mistake on if you can please only use small letters because Google analytics is a case sensitive tool, which is something will again. Ah, describe in couple of minutes. So, uh, this were a couple of do's and don't. Ah, And what we're going to show right now are two more tips. The 1st 1 which is a tip number 26 is how to use. Ah, you are all campaign Gilder, which is a tool that will help us to build a fully working. You are all address, including you tm parameter. So let's go and and ah, do that. Ah, but first we have to google it, which is easy. Ural campaign builder On the first link, we have inserts results G a deaf girls app sport. Come if you don't see this, uh, link your serves results on war. You'll find it also in Ah, in this lesson description. So let's get going. And the This is it. It's very simple and straightforward. Tedious? I think so. Let's scroll a bit down and see what we have here. As we can see, the first thing we have to type there is ah, your address to which we want to feeling the user ones he clicks on on on ah, link on. Then we have Ah, five UDM parameters, which with which we already are familiar. So, uh, let's use it. I'm going to use the SE mural address, which waas in a previous example. So it's ah, a little bit signup season slash e m and let's assume I'm running the paid search campaign eso um And it's on a Google in Edward. So what I'm going to use here is Google. Uh, you also have a suggestions. You're under some of the Offiah you 10 parameter. So Google is something I'm going to use as a campaign source. Medium is a CPC because it's based search campaign. So CBC and ah, campaign name ISS Let's say the one that I used, which is a g a fourth on then we have also year ability to use campaign turn and campaign content on as I said that we are running a state search campaign, so I'm also going to fill in a campaign term which is a key work on which my ad was showed on. I wanted to have it shown on Google plus analytics both or us, on insurgency. I'm using a plus here because I want to see that the blank space in Google Analytics. So, uh, this was it. Uh, Now we only have to scroll down a bit and we can see here, uh, full year old dress, including all that special characters as question mark underscore on person equal sign. So we don't have to worry about whether we will write it correctly manually. So it's here. Ah, so this is also tip number 26. Please use the stool. I use it every time I want to tax something. And now comes the tip number 27 which is nothing but checking whether it works or not. And we'll do. I mean by this. We have to copy this address, which we can do by this small button. And or even we can use this folding converter, which is nice recommended to use. Especially if you want to use this link on Facebook or Twitter because you don't want to have the that ugly long you are all address, right? So it works exactly the same way. You just have Ah, nice. Or so we can see here once I shorts. And it's something like this. Anyway, we have carpeted with the longer versions and now we're going to test it. So what do you mean by testing? Ah, we have to open a new tab. Uh, place there. Um, address and press enter. And what I mean by testing is that we have to wait until the pages fully loaded and we have to check is that even after loading on the first page only there are also you 10 parameters included in your address. Because this is the only way how you will then be able to recognize this traffic. Google analytics. So it's a very simple test, and it's worth doing. And why I am saying that is sometimes happens. They do have some very 43 direction on your server, which you, as a user, don't even have to notice. But what it sometimes thus is that it cuts off the UDM parameters. So what it would sometimes do is that it would sorry it would basically delete you, Tim, parameters and your final your address will look like this. So there would be no you 10 parameters which you won't lose this traffic user. We'll see the page and will baby toe will be able to interact with it. But you won't be able to recognize the specific traffic, because once the U R l would look like it looks here, we would see this traffic as a direct traffic and Google analytics, which is not true because it was some different type of traffic which we attacked. But because of the redirection that cut off power you 10 parameters we see as a direct traffic. So it would distort basically both off this traffic cops direct on this specific one. So please do this test. It's just five seconds. Uh, and that was actually these are guys you TN parameters. So please try to take as much of traffic as you can. It's always good to see where exactly the traffic comes from. And in the upcoming lessons, we are going to show how to find this traffic in Google analytics and what is more important, how to evaluate it. So this is for duty, and perimeters are there are a couple of things worth remembering. Please use them consistently, which means that you should create a simple methodology and use every time you take something. The reason for it It's very simple. Google Analytics is case sensitive to which means that B and B are two different characters . So, for example, if you will send traffic from your block post to your web and for the first time you take it s u T and medium equals look post. And for the second time, as you TM equals block post, Google analytics will take it as two separated mediums, which will cost discontinuity of data. When you will filter it, you're not going to lose traffic model, take you more time to get the date. I want it. And as we said, time is money and you don't want to waste it. So please use them by a strict methodology I recommend you to create. You'll find one example in the description. 39. GA4 - Tips - Landing pages: Alright, let's go for another set of dips, and this time we will focus a bit more about the content of your website. So let's start with something that is IC, quite neutral, which is lending page. So I edit their one-dimension which is lending page plus query string and two metrics, sessions and bounce rate, which for initial analysis of landing pages should be enough. So let's use them both sessions and bounce rate. And let's wait for a second or two. And here we are. What do we see? Here are the pages which generates the most traffic as the first page, which means this is probably something that users were searching for. They found it and they clicked and landed on your website. How to work with this data? Probably the first thing you will notice why there is a not SAP as the number one landing page. It also used to be the case in the Universal Analytics and so ways in the GAL4. And the reason for that is that there is no page view for some of the cases. No, Pedro, when the session starts, let me illustrate how it can happen. For example, a user is browsing your website, then ends on some, on one of the, one of the pages. And then he leaves for more than 30 min. And as we know, after 30 min the session expires. And when the user comes back, he might just scroll, for example, or play some video, which is not a page view, but the event is sent to Google Analytics. And as we know, GA is event-based tool, then it's considered as a session start, but there's no page view. So this is why it can happen. So don't be surprised if you will also see a nod sat as lending page there. What can you do with that? Probably the one thing you can work around that is by prolonging the default session time window, which is 30 min, to longer periods. So then this should be, There's not such good either. Probably not disappear but should significantly decreased. Anyway, what do you want to show here is how to work with this data. So in order to have it more meaningful, I will just exclude it by right-click and exclude from selections so the data is recalculated. And what do we have right now? This would be as if your website owners or just analyze that. This is something that should be in your interest. You would like to know or if I would be doing that, I would definitely would like to know which, which content is generating the most sessions, which is exactly what we see here, right? So we see homepages number one, then we see plenty of other pages by the volume of sessions they generated. So feel free to do the same thing. Just just build a report The way it is. And what I also edit there is a bounce rate, which is a metric telling us how many users viewed only one page and left, or they spent at least 10 s on the website. There's this, there's a quite important difference between what the bounce rate is in Universal Analytics and Google and in J4. The difference is exactly with that specific than a second time window after which the session in J4 is not considered as a bounce. So this is one of the reasons why we see so low values of bounce rate. I assume that in, in your case, they should be significantly higher. I would say something 30-70% was the technique right now, what I would do is just look on the top. I don't know, let's say 25 pages as Exactly, exactly in this case. First of all, try to understand what are the most popular ones and think of it, hey, am I promoting those websites properly? For example, in the bait search, am I promoting them enough? In my website architecture? Are they visible to the users? Because if they are searching for that outside of my, of my website, they are for some reason probably very popular. So this is the initial analysis I would do. The second one would be this was actually the tip Number 14 to find out what are the most popular ones. Now how to use the bounce rate? As we said, it tells us probably the attractiveness of the landing page for a user. So this is step number 15. Look on the outlier zero either on something that is very low, which will be probably mistaken the data. But what I would do as a first cut is to look on the top 25, maybe even like 50 pages and search for the pages with very high bounce rate. Which means that they're either can be something wrong with the website. Probably it's not working at all. It might be broken for some reason and people are searching for an app, which is one case. Or you can expect that the high bounce rate, for example, in the pages where you don't expect the user to do any action there. For example, the information about the contact pages. They just come here, they find a contact, they leave and that's fine. There's highest bounce rate, but that's the reason why such a, such a page exists. But if it's a page where you expect some action from the user and you see high bounce rate. Just open that page. I tried to have a look if there's either missing some information, if the website isn't broken because you're losing potential customers or users the first place and then probably customers, that particular page. So just try to focus on the top 25, maybe top 50 and search for very high bounce rate isn't just have a look. You might find something that you can, might fix very quickly and stop losing stabilizing users. So these were two tips about the lending pages. It would be great. And hopefully in the future, we will be able to again use something that was called in Universal Analytics as a weighted sort, which would allow us to sort the, for example, this report by bounce rate. But in the weighted sort, which means that it would consider also the volume of sessions and bounce rate into the sort, not purely the bounce rate, because if I would sort it right now by the bounce rate, this is what would happen. That I would see some of them with 100% but only with one session, right? So that's not something we would, we would like to see if we would have and hopefully someone in the Google who's listening right now, we'd love to have weighted sort back in J4. It would consider also the volume of sessions. So we would see here the pages sorted by the reasonable volume of sessions and high bounce rate. So this is exactly what we would like to see, but for now we just have to do it manually by, by sorting it by the volume of sessions and looking for the high bounce rate is set in the case of GA merchandise store, the bounce rate is very low, so there might be some trekking issues. But the New York case, hopefully it works well. So I'll try to do these two tips I just explained to you 40. GA4 - Tips - Paid traffic to landing pages: Tip number 16 will be kind of extension from the previous one. Let me explain you why. And this time we will focus probably on doing a little bit of saving or relocating your marketing budget. What I mean by this is that I want to have a look on the best and worst performing Landing pages from the Paid Search, which means something for which you spend money. How to do that? The easiest way is to just add traffic dimension, which is the session source medium. Here it is as one of the dimensions. And the only thing I will do, I will not even add it to the report, but use it as a filter. So the Reagan need here into filters. And I only would like to see the traffic that comes from Paid sources, which in this case is something that should continue contain CPC at its name. Yes, here it is. So Google CPC, depending on your region, you might use something, something different. I also know that somewhere It's the median value is PPC or it might be CSE, whatever else. So you should know what the abbreviations are used for the Paid traffic. So in this case, I will use Google CPC. And again we'll click Apply. Wait for a second. And here we are. Right now we only see the traffic or the landing pages from the Paid traffic source and do exactly the same exercises in the previous case. And mainly look for the ones with the high bounce rate because you definitely don't want to pay for the traffic which comes to your website and then leaves immediately after not doing anything. They're just maybe they do something but probably not something that you expect them to do. I don't expect you to around the Paid traffic campaigns for the pages where you don't expect any further action from the users. So just apply that filter of the Paid traffic and search for the pages with a high bounce rate and either try to exclude them from the targeting or try to have a look whether you're not literally wasting your money or spending them out of the window. So there are simple tip, but again, something that can quickly and immediately start saving the money or if possible, reallocating them to the pages which perform exactly, Exactly The other way around, which means the one that really brings it traffic which stays, then continues on your website. So that was it 41. GA4 - Tips - Custom channel grouping: All right, next step will be about the custom channel grouping as it's also stated in the name. We're going to play with the way how our traffic channels are grouped. Let me show you where the basic report is about it. And it's visible also in the report snapshot even here. But let's go to the more detail, which means going to the acquisition and then to the traffic acquisition. This is exactly what we have here. It's called Session Default Channel Group. This is the way how Google, by default, is grouping our traffic channels. It's based on the set of the rules based on which the Google Google is grouping all of our traffic into larger buckets in order to give us some simplicity when analyzing the traffic sources. On the other hand, if there's too many channels grouped into one group, it might cause or it might hide, some of the insights we might get from the data. There is an option that we can group the channels by ourselves by defining the rules completely from the scratch, which be quite complicated exercise, but sometimes worth doing. Anyway, what I would like to do here, as you can see in the case of my Google Analytics, for the purposes of discourse, the traffic source or the channel group number one is referrals, which means the various referring websites which are referring the links to my Pubble Brees website. This is something that can be pretty much anything referral. Just to give you the example, if I filter on the referral one and use the secondary dimensions, session source medium to see what everything is hidden under it. You'll see that there's nine various referrals in the past 30 days. Analytics Mania account, which is the great Google Analytics and GTM resources by Julius Federvicus. Great guy, highly recommend him to follow. But then you can see I have a udemy or where I'm posting also the links for additional material on my website, Trello, agoda, blah, blah, blah, blah, blah. So what I want to do, as you can see, I have 1234 um, referrals and what I would like to do right now is to bring them up as a separated channel group. How to do that? As probably expected, we need to go to the admin section, which is clicking here. Now as we have completely new admin, you might notice that it's slightly different from the slightly, but quite a different from the previous videos. Anyway, this is the way it looks. The functionalities are exactly the same. If you want to define our custom channel group, we need to click here to the data display and to channel groups. As you can see, there's the default channel group which is Google as it's in the description Google Analytics predefined channel group. If you look on it, we can see that there are how many? 18 default channel groups. Don't recommend you to try to define all of the channel groups from the beginning because it's not the best idea. Let me explain why in a couple of seconds. What I recommend you to do is to duplicate the original one and then do the changes you want to do. This is exactly what I am going to do. I want to add one more channel group to the ones that are already existing, because this is the only change I want to do. First of all, I will name it, let's call it, for example, Pablschnlry channel grouping description is optional, so why leave it empty? And what is here now is exactly the same list. This is the brief explanation why it doesn't make sense to do so to, or trying to create the channel list from the very scratch. The reason is that the set of rules is quite specific. For example, this one is the direct channel, which means that default channel group exactly direct, which is itself a very specific condition. But if we would go, for example, like the pay shopping, again, specific default channel group. But what we want to do, for example, organic, social, again, the same one. What we want to do is to exclude, basically from the referral the UDM as a traffic source. Important thing here to remember is that it really matters on the order of the channels. What it means if any user from any traffic source arrives to your website and then Google Anetics decides to do the channel grouping, it goes channel by channel in this particular order. First of all, if there's some traffic source, the first thing that the algorithm does is that it evaluates whether the conditions are met for the direct channel. If not, then it evaluates whether it meets the cross network. And so on and so forth. The first bucket in which particular channel fits, this is the way it is marked. And any other following condition is not evaluating that. Let me show what it means or what it can do if we don't put it into the right order. As we said, I want to add new channel, which I will name Udemy. Now, I need to set a condition based on which the algorithm should define it as the Udemy channel. In the channel grouping, I need to choose a condition, which in this case will be source, it will contain the phrase udemy in it. Click Apply and save a channel. What it automatically does is that it puts the newly created channel to the very last place. Right now I'm doing intentional mistake to show you that it really matters on the order of the channels list. Right now I'm saving this group named Pals channel grouping. If I will go back right now to the acquisition report. By default it's still visible as the session default channel group. But if you click then here you can see a. Here's the new grouping of the channels named exactly as we name it. I will choose it. What will happen is exactly nothing, right? I'm using mine but nothing happened because we still see that there's no udemy here. Why is it so what we just described previously is that it matters on the order of the custom channel grouping. It means that if I will go back now and try to edit the one that I already created here, you can see that it's on the very last place. And then there is a definition for the referral, which means that if the algorithm is going that evaluating line by line based on this particular order, it means that the udemy as a referral was evaluated. It belongs to this bucket and no other subsequent condition is evaluated. This is why there was no change. If we right now do one simple thing which is re ordering, I will go down and drag and drop the udemy in front of referral, right? This is what I just did. And click Apply and say of this group. Now, going back to Acquisition Report, here we are. If I will change it right now to the Pubble channel grouping, A Ola Edem is here because we moved it to the right order before the referral. This was the tip about creating a custom che channel grouping in order to slightly change. And again I stress the word slightly change the original grouping. It's a good idea to always copy the original one and change on what you really want to change and leave the rest of the grouping as it is because you then might do more harm than good. This was the tip about the custom channel grouping. Another thing worth remembering, which is a very good one, is that if you're creating the channel grouping the custom one, you're basically not touching the underlying collecting data no matter how you define it. You're not touching the original data, you can't break anything. You're just changing the way the data is reported in the GA interface. Feel free to play with it. You can even remove the channel grouping if you want to. You can still like edit that. It's also working retroactively is one of the good features that you're not breaking anything inside the reporting or inside the core data. Yeah, that was it. 42. GA4 - Tips - Site search data: Another tip, it will be about how to access the internal site search data. In my opinion, one of the most precious data you can collect on your website about your users. But first of all, let me just remind you what I mean by internal site search data. If I will go to Google Merchandise Store.com it means that I'm clicking on this small loop and I'm trying to search internally within the website of Google Merchandise Store, let's say I'm searching for good. And I will click on the Go. If I will wait for a couple of seconds, I will see some search results. But what is important here is that I was searching for the keyword hoodie. In one of the previous videos, we showed how to set up the measurement for the internal Sor data. Right now we are going to have a look how to access this data within the GA four interface. As we said multiple times during the course, G four is so far mostly about the customizations and creating custom set of the data. This is also one of the examples. First of all, we need to know what's the dimension name in which this data is stored. Which means I need to add one of 182 available there. And the name of the dimension is called search term. It's one of those general ones, not the custom ones with the capital S. This is the dimension I'm looking for. I'm importing that as we also know that Google G four is event based measurement. What I want to see is how many times particular search term was searched for, which means how many times a search event happened. The metric I'm looking for is event count, which is this one. This is pretty much all I need. Now what I need to do is just double click on the dimension name and do the same thing for the event count. Here I am. What I see right now is for the period of October 1 and November 15, the most search terms within the all website, what you might see here is okay. But what's the number one thing? Like over 3.5 million event counts. These are all of the other events which are not the search term ones if you want to exclude it, which is possible in the custom report is that we need to filter it out from here. In order to do that, we need to add another dimension which is event name. I'm looking for it, here it is. I need to import it. I don't want to include it in the report itself, but as I want to filter it out, I need to add it under the dimension part here. Right now I have available here also the event name. As a filter, I'm clicking on that. I want to include only the one which the event is called, search results, which is the event from which all the data in the search term originates from. This is the only event I want to include. If I will click Apply. Right now you see that the empty line with the 3.5 million counts disappeared, and we right now only see the real data. This is the way how to access this data. That's pretty much all what is available right now in the GA four, we can see what are the most search terms within the website. It at least gives us the information what customers or users are searching for. For further analysis, what I recommend to do is just go through the top, I don't know, maybe 50 search terms. Of course, depending on the total volume of search terms you have, just check whether you have the relevant content for all of the search terms or not. Hopefully, in the future, more metrics related to the search data will appear in the Google Analytics, because this is just like not giving us the picture or not giving us the lead. What should we do with the search terms in terms of seeing which search term converts the best? Which search term has the highest exit rate which is causing the most conversions and so on and so forth. This is the data that were available in Universal Analytics and hopefully in the upcoming months, or at least the years will appear again. This was another tip, how to access the site search data. 43. GA4 - Tips - Product performance: This tip will be a little bit different because we will only partially stay in the GA four because of its limitations. Let me show you why. In this tip I'd like to focus on the E Commerce data. It's particularly for those guys of you who are running the e commerce business and want to analyze the item data or the product data as they were used to call it in the universal analytics. What I mean by this, if I would go to monetization and then to e commerce purchases. And I would like to focus purely on analyzing how every product or the item is being sold or in better words words, how it's performing in the whole customer journey. This is the report where I would go again, hopefully just the temporary limitation because of a lot of metrics about the E commerce still not exist by default in the interface, but again, they will hopefully be there in the upcoming months. What we so far have is just the basic data about the items, which is the item name. And then there is a set of the metrics. And none of them is relative, which means when I'm looking on the report, I see how many times, for example, particular product was viewed. It means like the detail of the product was viewed. Then, for example, how many times it was added to cart. But if you want to at least basically analyze the data, we would need the relative metric, which means seeing something like the Ds to card rate, which would be a very simple number when we would divide, for example, for that Google 25th birthday Hudi 195 divide by 1077, which would give us the add to cart rate. The reason is simple, if you want to compare all the lines, we can't expect that they will have the same volume of items used. This is why we need the relative metrics. Similarly with the items purchased. Yeah, we see how many times a particular item was purchased, but there's not much of comparison we can do. For example, if somebody is performing good or bad, what I'm aiming to, that we still don't have availability to see something like the item conversion rate, which would be, again, simple number. For example, for the case of, again, Google line number six, Google 25 GL birthday T. That would be like dividing items purchased 70 by 900 and then see the conversion rate. Again, it will allow us to compare the items among themselves. And so far this metrics doesn't even exist in the custom reports. We're not going there because it's just not there. What so far exists is the availability to do to create a few calculated metrics. But in the standard accounts, standard four accounts, there's a limitation to have only five of them. We need to be very careful when creating the calculated metrics. I will cover that in one of the upcoming videos. But what I want to show you right now is the adoption, how to play with the GA four data until the moment all of the metrics are available. Or all of the metrics we were used to from universal analytics. The solution for that is another Google product, which is called Lo Studio, formerly known as Google Data Studio, but after the acquisition is Luker Studio. All we have to do is just to log in there and everyone of you who has access to any Google product will be easily able to log in also to the Luker Studio and try to build at least the basic report to analyze the data available in the GA account. What I'm going to do right now to Google for the studio, if you're basically familiar with using any online tool, it will be fairly easy for you to use also the Luker Studio. Here we are clicking on this link, Luker Studio@google.com which brings me to this of the canvas. If you are here for the first time, you might just acknowledge one or two consents. And then you should see something like this. Let's have a looker. Studio allows you to build like plenty of the data visualizations, but the goal of this exercise isn't to go to the very detail of it, but just to show you how easily it is to build something based on the GA four data so we don't have to export it. Then going to Xl for example, or the Google spreadsheet is also possible to do it within the Luker Studio. And what is great about it is that you can save the report every time you login into. That you can see then the rolling data means that every time you login, you might have access to the fresh data if you want to. How to do that? Let's build it from the very, very squa, clicking on the blank report, waiting for a second or two. The first thing I need to do is to use something called Google Connector, which is nothing but the data source from which we want to base the report on. I'm clicking on the Google Analytics. I need to select from all of the accounts I have access to. I'm going to that demo account and to Google Four Google Merchandise store, which is exactly this Google Analytics account. Here I am, I click selecting and then clicking on a. Again, waiting for a second or two until I'm connected there, which happened just now. Okay. Yes, I want you exactly to do this. I'm clicking to add to Report. Here I am. Now I have the blank canvas with a very simple table here which is the event name and how many times something occurred. As we are right now familiar how to build a custom report within the four interface. This is very similar. What we see here is the slightly different view, the comparing to what we are used to from GA four. But the principle is the same is the drag and drop tool where we can easily move the dimensions and metrics and then they will start to appear within this report. Let's build the simple one. We have the data source here, which is the Google Merchandise store. And we have two default dimensions here, but we want to build it from the scratch. I'm clicking on the X here and I'm leaving here the view. For now, let's leave it here. But my main dimension I want to base the report on is the one which is here. And it's called item name. Going back here and searching for the item name within the dimensions, here it is. I'm clicking on the item name and I have it. It's invalid configuration, right? Because I have the views here, which is the metric connected with the page. This is the reason. But right now I can remove it because the report can stay empty without any dimension or metric in it. I'm removing the metric right now. I want to add here, for example, items viewed and items added to card. This two is definitely something I'm looking for. First item view, hold on just a second to have the right name items view. All right, that's the plural items viewed here it is. We have the metric here. It was viewed into the metric, yes, now we start to appear. Here's just the order, so I can start minimizing this one just to see the better names of metrics. And then I said items added to card, adding also this one here, we can also play with the order here. Let me enlarge this one. Here we are, we have items and items added to card. I will add one more metric here which is items purchased. And then we will create two calculated or custom metrics, which is great feature. Now items purchased here, we have pretty much exactly the same basic view we had. We can play with the size of the table, it automatically shows us more and more lines. Let's stay to something like here. 20 should be just fine for the purpose of this tip or set of the tips. Here we are right now, I want to build the first calculated metric, which is exactly telling me how many people or how many times if some item is viewed. It is also added to card, but in a relative way. I'm going to create one metric, But right now I'm not going to select it from the set of predefined, but I'm going to build it from the scratch, which is clicking on this small blue plus icon. And this is what I'm going to do. And then creating another small blue plus icon, which is create the field. Here I have the dialogue, which means, first of all I need to name it. I will name this metric as at two card, which is my favorite name. And adding also the word rate. Right now, I need to select the data type, which in this case will be the percentage percent here I'm defining the formula for it. You don't need to remember exact names because the whisperer works just perfectly here. What I'm going to do is to find this name on the metric items added to Card, which is this one. I'm clicking on this. You can use operators which is dividing by items used. Here we are. Now we wait for a second until we should have this green tick box. Yes, perfect. So the syntax is valid, and now I'm clicking Apply. It should appear here. Okay, here it is. It was added to the very end of that. I want to move it first here, don't worry, we do some filtration after that. Right now, we can see like plenty of the numbers, which doesn't make much sense because the data is broken with Google within the merchandise store. But that's not the point of this exercise. The point is to find the ability, or have the ability, how to build such a report from the scratch. Again, the second metric I'm going to create, one is like the conversion rate for every item. Which in this case would be the items purchased divided by the items viewed. Again, clicking on the small blue, another small blue, I will name this conversion rate. It'll be again. Percentage metric defined as items purchased divided by items, items yes, viewed. We are again waiting for the validation for the green tick box. Okay, here it is. And I'm clicking on Apply and should appear every second. Okay, here it is. Now I have it intentionally ordered this way because first of all, I would like to see what's the add to cart D, which is the first step on the customer journey. Right? If some product is about to be or item to use that naming, if some item is about to be purchased, it first needs to be edited to card, which is exactly what I'm going to examine here. And that it needs to be purchased, which is this number, how it works. I want all of the metrics to be seen with the full name. Here we have, the way we can play with it is by if I, for example, click here, the report will automatically sort it by the items added to card, which you can see in this case, there's like 155,000,001 item being added to card, which obviously can't be true. But I'm just showing you the possibilities here. I want to sort it by items viewed. Again, I still see quite a lot of lines which does not make any sense. The good thing here for the purposes of this exercise, and maybe it'll also be your case, is to do some filtering. There's a lot of other options available here, but what I want to do is to filter those outliers. I will call them, for example, something that was added over 1 million times to card, which is definitely not the case if it was used only something like slightly less than 3,000 times. How to do that? Clicking on ads to filter, I need to name it. I will name it like outlier ad to card. Here it is. And I want to exclude anything that has items added to card more or greater than, let's say. I think that even like 100,000 should be okay. Here we are. Let's click on the Save and see what happens with the report. Okay, starts making more sense. And also we have some null here, which apparently for some reason appear here. But if I will filter it, it shouldn't be the case. Yes, right now the data starts to look quite okay. Another fancy thing we can do here is to change the type of the chart. We see the heat map here, which is changing it to this one. It automatically uses the heat for every column here. Again, it changed the default sort. Again, I need to sort it by items viewed here I am. Now when it comes to the analysis, I have great data to do, the very basic analysis which would tell me, okay, which products are popular when it comes to adding some of the products to card. So we can see that there are quite a differences, right? The first product is 14% of adds to card trade. The second 118, but the third one is not even 3% Considering that this is the third most popular product when it comes to the items, it's probably not working that well. It's also confirmed here by the item conversion rate which is very low, right? 0.1% Out of the 1,000 views, only one happened that this item was purchased. Okay. This is definitely the product I would like to examine. What I would do as a first thing. I would go to the website and try to find out if there isn't something wrong with this product. If it's not like viewed too many times or we don't promote like wrong information about the product or like 1,000 other reasons. But this is telling me, okay, it's not like even being edited to card properly, and if it is, then it's not purchased. There's definitely something wrong on the whole customer journey. This is the set of the data and if we go line by line, we can see quite a lot of differences. And again, I need to stress out that the Google's data is probably broken or not probably, but definitely broken as we saw that 155 million items added to card for one item. But the point is that you can build such a report quite easily, even outside the G Four interface and be able to do the quick analysis of particular products and see whether something is broken, broken, or not. This is the way you can play with the report. You can save this report, or it's automatically saved once you created that, anytime you log in next time. If you name it, let's say, let's name it like something reasonably like a product performance. You can, for example, also view it automatically and not seeing it like this editing area, you can play with it exactly the same way as we were playing then or previously. We can sort it by any metric you want to and explore the data to see, hopefully of this metric or something similar to this metric will be soon available also in G four. But since it's not the case, you can play with it outside the GA where the data is available and you can build the calculated fields or calculated metrics as you wish. That was about the product performance. So quite a few set of the tips, if you like the Google Luc Studio, or Luc Studio is the current name, feel free to play with it as well. But that's not the point of this course. Just wanted to give you the idea or the tip where we can play outside with the Google Analytics for hope you enjoyed it. 44. GA4 - Age, gender and enabling demographic data: Let's continue with practical tips. This time we'll focus a little bit on the demographics data that Google Analytics is providing you and you don't need to do that much actually to collect them. First of all, what do we mean by demographics data? If I will click on the user Attributes report, I can see here a dedicated step called Demographics details. So when I click there, we see here by default the first dimension, which is called Country. But what I'm more focusing this time will be about the age and the gender. So when I click on the age here, you will probably see something that you expect, which is the breakdown of your user base based on the age ranges, which is 18, 24, 25, 34, and so on and so forth. First thing we need to do in order to see the data, we need to enable it within the Google Analytics interface. How to that? It's pretty easy to do. So what we need to do is to go into the admin section, Then clicking on the data collection, which is exactly where I am now, even though it shows the internal error. Just a second to prove you that it really works this way. If I would go from the very beginning, then getting back to data collection, and here is the Google signal data collection. This checkbox must be enabled. If you do that from that moment on, Google will start collecting the demographics data about your user. Please do this as a first thing, then we'll get back to the reports. When I'm going back to the reporting part of that, It might happen to you that you won't see any data here. First of all, it takes some time, let's say a couple of days before the first data will appear in your interface, plus there's a signal certain data thresholding, meaning that you need to have sufficient volume of the data, what that is no one knows. But let's assume at least a couple of hundreds of sessions a week in order to see this data, since Google is still very strict when it comes when it comes to anonymization of the data. So if you only have very small traffic, you probably will never see this data. Plus, be sure that in majority of the cases, line number one will be unknown, meaning that Google measures those users, but it doesn't have enough data or doesn't have the consent from the users to provide the demographic data. Anyhow, we don't need 100% of the data to be collected because if we have enough data, The distribution among these unknowns will be very much the same as in the case of the data we already measured. So this is how to enable that. So this is another tip. And now how to get to the way to understand the data itself. What I see here right now is predefined set of the data, and as we all know, it's never enough, so GA four is mainly about customization and creating something from the very scratch, and this won't be the exception. So I'm going to the custom report. I prepare for this set of tips. Go there, as you can see, and getting internal error. Anytime you see something like this, just refresh the window and then it suddenly starts working again. When I'm going to the explorations, I already prepared a report, we know how to work with that. Just a short description, what I see here. I added their age and event name as dimensions. The reason why I have here event name is because I want to filter the conversion rate or as is now newly called user key event rate. Only for the purchases because this is what the main conversion of the Google merchandise store is. And then I have hear metrics as a number or volume of the active users, user engagement, purchase revenue. So these are the metrics I editor plus average purchase revenue. So this is the set of metrics I editors report, and I want to understand basically what my user group is, who they are. Who are the users who are coming to my website? I want to understand them. And this should give me another layer of understanding who they are. I will right now minimize the settings and variable to purely focus on the data. What it tells me, as I said, we have here unknown as line number one because Google does not provide the data about the users for which Google doesn't have enough data. But what is important for us is to understand the rest because as I said, the distribution among the unknown will be the same as from the data that we collected. What it tells me, It tells me that the Number one, user group are young people below 24-years-old, and then it goes like this as we see. What I would like to understand is, this is the group number one or the most frequently visiting group, going to the Go Google merchandise store. I can see when comparing the total revenue they generate, which is, as I said, number one conversion for Google merchandise store. If in your case, it's you're not in e commerce business, have there your conversion, your main goal or key event. As it's an out called. Then I want to understand. Engage in rate of this group is okay, I would say, slightly better than the average. But then when I'm looking on the right side, meaning of what's the key event rate, which is in this case filtered for the purchases, I can see that it's okay, but comparing to the rest of them, not even like half to the one that is the best performing when it comes to key event rate, which is something that is not the best in terms of volume, but definitely the one that converts the best. Pretty decent insight I'm getting here. Even though on the first side, it might seem that, this is my top he group I should focus on, maybe yes. But when it comes to the age group that converts the best. It's actually 45 to 54-years-old. What to do with that? First of all, when I'm running, for example, a paid campaigns, maybe this is exactly the target group I should focus on for some reason because they convert the best. Another thing might be trying to understand why young people coming to my website do not convert that well actually. Why is it so? Of course, it requires maybe like additional research. This is starting point telling me, I should probably focus on these guys because even though this is the largest group, it definitely does not convert that well, going forward, when looking on okay, one thing is whether particular age group converts or not, which is shown here. I can see 45 54 is the one that converts the best. But when looking on whether someone is already converting, meaning purchasing something, how much are they willing to spend? Then when I'm scrolling down to the the highest average purchase revenue, which is 240 bugs here, surprise, these are 65 plus. It seems like they're not converting good at all, actually is the worst conversion rate we see here. But if they convert, they buy for almost not twice as much, but 50% more than the average purchase rate. For some reason, 65 plus buy significantly more or by four significantly more than is the average. Again, this is inside for me, that maybe when I'm planning, for example, again, the performance campaigns, I should be maybe targeting a bit more expensive products for this age group because seems like they are organically willing to buy a bit more expensive stuff. This is another tip, how to try to understand the age groups, and also a nice example how not to ache act stily when reading the data and just getting confused that the total volume of active users is probably something that we should focus on. This was the tip based around the age group, and similarly, if I will right now open the variables and the settings, and I will add there one more dimension, which in this case would be gender and add it to report and replace it for the age. Again, minimize that. Again, helps me understand whether males or females are somehow different when it comes to interact with interacting with my website. So again, I need to stress that we are in the Google merchandise store, where I would say that a lot of people coming there are males or seems to be that they should be more engaged with the website itself. And this is something that we also see here looking on the first of all, there's more males coming to the website than females. Yes, probably I can understand that. Whether it's something that is wanted or not, I don't know that that probably needs somebody from Google to understand. But also males have higher engagement rate and higher key event rate or conversion rate here. Again, something that is interesting, that the males or men are buying more. On the other hand, again, when trying to understand a certain behavior or differences between males and females, here it is. Once the female or women decide to buy something, they buy for significantly more than males, for example. Again, this is the insight for me, how to work with that particular user group. So this tip was based on the gender. The great thing that you could do, and I leave it up to you as something to be maybe a surprise for you. If you try to combine gender and age, what we will find out, who is actually probably or maybe trying to understand what's your most profitable target group or the one that is like the least engaged with you. So according to that, you can start maybe changing the content of your website or the way you communicate to certain group of people. So this is what the combination of age and gender should help you to understand or in general, the demographics data to understand what and who my target group is. So yeah, this were couple of tips about the demographics data. 45. GA4 - Calculated metrics: Another tip we're going to show is about the pretty new feature in G four, which is called calculated metrix. And as you might now thinking of, it might be, it's something that used to be for years in GA, and now it's also available here. So let's have a look what it is and how to create it. We need to go to Admin section, In order to create a calculated metric, you need to have at least added level of permissions within the particular GA account. So if you won't be able to perform exactly what I'm doing, somebody will need to increase your permissions. So I need to scroll down a bit in the admin and go to custom definitions. So this is the place where we can create something that is custom and Google Analytics does not measure and collect it by default. And right now we're going to have a look on the calculated metrics. So I'm clicking here and in the standard account, we can create up to five calculated metrics. What is calculated metrics? It allow us to create a new metric based on the existing metrics in the GA four. So how to do that, pretty simple. When I click here on the create calculated metric, Here's the dialogue window that opens up and I can create here a new metric. It's pretty cool feature, even though it's necessary to know that not all of the metrics currently available in the GA four can be used in the formula when creating a calculated metric. Hopefully, this will change in time. But I want you to be already aware of such a feature. So for the sake of this tip, I decided to create a simple metric, which is telling me, what is the volume of inactive users? Or let's call it like the inactive users. As we already know what actives are. So this would be pretty simple calculation. So we create the name, then automatic it automatically creates the API name. So this is something that will allow you in case of your exporting the data to BI query to also have a dedicated API name for such a metric. So in this case, it will be something like this. The description is something that is optional. So I can write here, for example, a number of inactive users, but if you use properly self explanatory name of the metric itself, you don't need to even write a description, and now the formula. You need to type in, and it automatically opens all available metrics which can be used in a formula. You can see it's a lot of them, but not all of them. So as we said, we want to calculate the number of inactive users. How to do that? I just need to subtract total users and active user. If you just start typing, it'll show you everything that matches there. Here's the formula, it's valid because the validation is done automatically, if it wouldn't be valid, that input field will be marked with a red color, and I can select the unit of measurement, so it can be either standard, which means the number, currency or a distance or time. This is the type of the number we have. If I have it done as I did, now I click on the safe, I will wait for a second and now I can see that it's already there. Now we're going to show where to find it and use it in the interface. But you have here the option also to edit it in time if you want to. You can copy that in case you want to create something very similar, or you can archive that, which means that it will completely disappear from the list of this available calculated metrics. What is important to know that if you create the calculated metric, it will create or will calculate all of the available data from the first moment you set up the account, which is quite cool thing because some of the features in GA work in a way that they will start calculate the data just from the moment you create them, but that's not the case, and we will show that immediately. So we have set up the calculated metric and now where to find it. As you probably know or I hope that you know, we need to create a custom report, of course. Let's go. I'm going to create one from the scratch. Let's go here. I'm creating the blank. I will use one dimension to show that it works. Let's say my favorite one, which is device category. Let me find it. Here it is. I'm adding it here. Now going to metrics, and now as you can see, there's the one within the custom ones. This should be the one that I just created now. So here it is. If I will open that, yes, here is the inactive users. Adding that to the report, then double clicking on the dimension, double clicking on the active users. As you can see right now, this is just number one, which means that only one of the users didn't do anything. But let me create a chart maybe with a little bit longer period of time, let's say, Let's say here up until Here, I'm clicking Apply, and maybe let's do the weekly granularity to see how many of the inactive users I had. I'm looking on one of my GA four accounts on my block page where I don't have much of a traffic. This is why the numbers are either one or zero, meaning like everybody probably spent at least 10 seconds on my website reading the blog post. Yes, this is why the number looks a bit weird, but I think you understand the point or the logic of this feature, meaning creating a custom calculated metric out of the available one. There's a bunch of other we can create, even though as I said, not all of the available GA metrics are it's possible to use all of the available metrics within the calculation field. For example, if you're not interested in the absolute number of inactive users, you can also create, let's say, percentage of active users, which would be then dividing the active users by the total users, and then you would see the percentage, not the absolute number, but there are multiple options how to create them. I just wanted to show you that such a functionality already exists and can be used. So yes, This is how to use and create the calculated metrics. 46. GA4 - Exit and exit rate: Another tip we are going to show is about better understanding the content of the website and its performance. I'm thinking something that I used to use very often in the previous version of GA, which is still not available in easy to digest form in GA four, so we will need to explore the data, but that's the easy thing. I'm speaking about the exit rate. What that is is something that we're going to describe just now. In ideal case, we would be able to use the exit rate as a metric created as a calculated metric, but this is the one which is still not available to be used as a calculated field, so we need to create it by ourselves. What I meant, is the create the metric, which is helping us to understand what are the pages where majority of the sessions are ending. So the exit is a metric telling us, in what percentage of the cases, if particular page was viewed, was this page also the last one during the session? So this is something that is not available here in the standard report, so we need to create it from the very basic, and then export the data. Let's do this. In ideal case, it should be somewhere here, but not yet. So let's go to the customization, which is our new bread and butter in the GA four, and we're going to create it. Again, opening a new custom report. I'm adding here Dimension called page path. And screen class. It covers also the app data, if we have them. I'm adding here two metrics. First of them is views, and another one I want to use is exits. It's actually quite cool that we at least have the number of exits for every page. If I'm now double clicking on both dimensions and metrics, again, I will minimize the set up part of that. What do I see right now is the following. Let me prolong a little bit the time window. So let's say something like from here up until the very let's say something like this, apply. And what I see now is, of course, the most popular block posts on my block page. And now I here have the metric exits, which is telling me, in what percentage of the cases, if particular page was viewed was also the last one during the session, or historically we used to call this metric as a session killer. So what's the point? The point is trying to find out whether you won't see among the top exiting pages the ones where you wouldn't expect them to be the exiting ones. I have a blog post. So pretty much like any page can be the last one because probably users just look for particular content I created, they read it, and they just just go away. So that's fine. But especially if you're in the business where you wouldn't expect on particular pages to be the last one during the session, this is something that will exactly help you to find such pages. So, as I said, we unfortunately don't have a metric exit rate here, but we can easily create it outside the GA. How to do that, I will just click here and export the data, let's say directly to Google Sheets, which we can easily do. So I'm just clicking there. I'm importing the data. And if I will wait for a second or two, I already see the data exported here. All I need to do right now is just to create a simple metric, which is working like this. I want to divide exits by all views, and I want to, yes, please Google, help me with auto fill this. I want to represent this number as percentage, so I can easily then digest the data. Then, what I recommend you do to do the exactly same exercise as I did with, of course, much a longer list of the pages, and go line by line, whether you won't spot a particular page to be exiting much more than you would expect them to be, especially when you're looking on the pages which are supposed to support the decision making process of users on the website and moving them towards the conversion. Feel free to look at it because you might spot that some of the pages actually might not work as well as you probably thought, or it might be some blocker or whatever else. Google and nets won't tell you what exactly the problem might be, but where it might be. Exit rate. Let me even name it like this because this is my favorite one is one of the metrics that might help you with this. So yeah, feel free to do the same exercise, create a custom report where you use views asymmetric, exit asymtric, then export the data to Google Sheets and try to find out the weak spot. Of your content or of the content of your website. Sorry. Yeah, that was it. This is how to create the exit rate as a calculated metric outside GA four. 47. GA4 - Search query and search keyword: Another tip will be about understanding what user types into the Google Search engine before they visit your website. So another surprise that we need to create a custom report, of course, to see such a data, but the good news is that the data exists. So this one applies to all of you who run paid campaigns within the Google Search engine, which I believe majority of you do. So how to build that. For understanding which queries are actually bringing traffic to your website. We need to first see, of course, the volume of sessions. So this is one metric, I added there, and then bound trade for do a simple exercise of understanding which one probably perform better, which one worse. And I added here two dimensions which are commonly used. And unfortunately, still, the one which is used more is called Session Google Ads keyword text. I will show you both and also explain what's the difference between So let me first of all add this dimension, which is session ad keyword text. Of course, there's a lot of sessions which haven't been triggered by a Google search engine, so this is why we have not sat here. But other than that, we only see six lines telling us, hey, that these are all the keywords that triggered impression in the ads system, and then there was a click occurring from them bringing session to the website, which is hard to believe, right that only six keywords were used by users all across the globe. And it's the point of understanding what actually keyword means. Keyword is not something that users type into the search engine. It's sort of the keyword which somebody who is managing the Google ads can use in the Google targeting as the keyword. So it's not something that users normally type into search engine. It's sort of like the group of the real ads queries. So just to explain you that this is the difference, that keyword isn't something that users actually type in, it's more like the group of the queries. Or something that performance marketing specialists type into as a part of the ad ad groups. Where the interesting data is is when I will cancel the keyword text and add their session Google at query. Let me remove keyword text also from here, and now you can see that it's much richer report. It's much more lines, right? I'm scrolling down to 57, something like this, and this is where I believe is the very precious information because it tells you what exactly users were typing in. After that, when they clicked on the ad, which then triggered the session to your website. You can see line by line telling you exactly what they were typing in and then you can start immediately doing the analysis. First of all, understand what exactly they were searching for before they landed on the website, whether you have relevant content for them or not, or whether you shouldn't start creating actually a new content, on your website because people are searching for it and then getting to your website. And the metric that should help you understand which of these search queries are relevant to or you have relevant content for particular search queries is a boundary, telling you how many of them actually make it at over 10 seconds on your website or seeing at least two pages on your web. This is something that helps you understand. The first thing I would definitely do is to see the volume of the search queries users are typing in and then understanding how many or which are the ones that actually doesn't work well in terms of that customers or users move forward to your website, which would be the ones with a high bound rate. This is the first exercise I would do seeing this report and the real data and the real queries that user types in. And another one I would immediately start doing once seeing the data itself is to start maybe excluding some of the keywords, which, even though bringing traffic to your website are not relevant. It might be the case that in some of the keywords, you just will appear because the way you set the ads in the ad manager, but you just don't want to appear there. So we have the ability within the Google ads admin section to exclude some of the add queries for which you don't want to appear, and you stop wasting money for such a keyword because they just are not relevant to your website or they perform so poor, that it doesn't make sense, or you just don't want to advertise on them for any other reason. So this is something that I would immediately start doing seeing this data. So, to sum it up, super important data, super rich one because basically with the queries, your users are telling you what exactly they were searching for. So super precious one. Use it. 48. GA4 - Session query and landing page: We already know what is session Google Ad squary. Another tip will be enhancing the information we already know how to get, which is the volume of sessions being triggered by a particular ad squary. Now we will go one level deeper and let me first simulate what experience I'm thinking of. So let's assume we are still at the GA merchandise store, and somebody like me will try to search for Google Merge. So this is the search query that shouldn't appear in the report, if it's measured properly, and if I enter that, I then can see various results in the search engine results page. So let me then click on, let's say, I don't know this on the Bas. And this is the landing page where I just arrived or the page through which I entered the Google merchandise store. So there is like strong connection right between the query that I typed in, and then the lending page to which I got. And this is exactly what we're going to examine in this tip. So up until now, we looked only to the volume of the sessions that we brought and then the bound rate to try to figure out whether particular query works good or bad. And now what we now do is pretty simple thing, which is adding landing page as a dimension to the report. L et me add it there, and that will do a tremendous breakdown to helping us understand what is then happening on the website. Let me just add it to the report. I will minimize this part. Now I have much richer report helping me understand whether somebody if types the particular query, whether I am directing those users to the right content. This is exactly what this report is telling me. It's not that visible from the Google merchandise store data that there's something particularly good or bad because majority of these ads are branded one, meaning that they are directing traffic to the home page, which should perform just well, especially if somebody was searching for Google Google Store or Google Google merchandise store. But I think you have a point right. If I know what users were searching for, and I know where they landed, And then I see the data if it worked well or not. So it helped me again understand pretty quickly both strong and weak spots in terms of where do I direct the paid traffic, which is expensive, of course. So I want to perform it as good as possible. And also, it allows me to understand which landing pages shouldn't be the one that I direct the traffic. From the front the page search ads. So, this is it, pretty simple tip tip, but very strong one because it helps to understand exactly where do I direct users and how do they perform them. So I don't need to guess what can be good or bad just by seeing the Google ad queries. Simple exercise of primary and secondary dimension and connecting the right dimensions which are right one after another, when thinking of user journey. Yeah, that was it. This is the connection between ad query and landing page. Feel free to do the same. 49. GA4 - Traffic sources evaluation: So we already know what are UTM parameters and what are traffic session dimensions. There are five of them. Now let's have a look on how to evaluate the traffic sources. I'm going to show you the example or the technique I'm using pretty much every time I'm evaluating this. So the first place where it makes sense to start with traffic evaluation is going to the report and then to traffic acquisition. First of all, what I highly recommend you to do is to get rid of that primary channel grouping if you didn't create your custom one shown earlier in the course because it's still like grouping a lot of channels under the one hood, and it just doesn't provide granularly enough information to make a business decision. First thing, what to do, I'm going to switch from the channel grouping to the session source medium And then what I'm looking at is to understand which channels probably perform better, which perform worse. This is like the first cut so far, pretty simple, I think and easy to do. What I need to do is to select what is my primary conversion towards which I optimize all of my activities. And if I switch right now to the purchase, which means buying something online, I'm seeing, Okay, so what is the traffic? Okay? Seems like First five, six lines have reasonable volume of traffic, majority is the direct one. But I'll try to simulate the exercise, which is pretty often. And especially it's very common when you are running paid campaigns, which in this case, are represented by Google CPC channel. So I would like to see, if I'm investing money in bringing the pace traffic there, I would like to see what's its performance, and I'm going to the right one and looking on the key event rate or formally called conversion rate. I see that the website average is 1.12, and I see that the Google CPC is 0.3, which comparing to the first six lines, seems like it's the worst one by far. It's only like one quarter of it. This is something that definitely spots my attention, and I would like to understand it. This is another set of the tips we're going to show during this one that every time you spot something, please try to get always under the hood of that. When we see here 0.3, the total number is composed out of many numbers below this Google CPC as a source medium, which in this case, is a campaigns. So I would like to understand whether all of the campaigns within the Google CPC as a traffic source, perform that bad. Or there are some of them which perform probably slightly better or slightly worse than the average. So please try to avoid averages when evaluating anything because average is the number that never happens in reality, and yet we make our decisions based on this number. I mean, averages far too more often. Anyway, so we spot that Google CPC is something that doesn't perform that well, and we want to explore it in a bit more detail, which means effectively, that we need to create a custom report to understand it a bit more better. This is something I already prepared on this tab. I'm going there and I'm going to first explain what I prepared. I edited a couple of dimensions. I have here session source medium, of course, I have your session campaign, which is the great breakdown of the session source medium. I have your event name because I want to filter the session key event rate only for purchases, and I have your lending page. I'm going to explain you this one just in a moment. Why do I have it here? Then I have here the metrics which are sessions bound rate, engagement rate, and session key event rate. This should be just fine for initial understanding what's going on under the hood. What I did in this setup is that I filtered the traffic sources only for the Google CPC, which is something that we want to examine. I added there the event name filter for session start and purchase to properly calculate the session key event rate. This is something we've already shown a couple of times. But just to remind it's done like this that we have the event name matches regular expression, session start and purchase. This is what we're using here. And yeah, this is it. This is the initial setup. Let me then minimize the settings and variables to see what I have here. We have that low session key event rate about like 0.3. And now as I'm scrolling down and I'm looking on the campaigns, I can see that some of them are around the average like the first one, then the second one is far below the average, right? Bringing 40,000 sessions with a conversion rate of 0.05, that's something that definitely is not what I would expect from a paid campaign. But then scrolling down, I can also see that some of them works pretty well like this one, 01.64 or even this, but this is organic, so probably some wrong tagging from Google. Google, fix that, please. But scrolling down, you probably understand what I'm trying to show you, right? So not all of the campaigns perform that bad or that good, as it seems from the initial view. So, I see that some of those campaigns perform greatly, some of them completely terribly. So let's assume that I see that this one, Google and Lets demo, has really terrible c session key event rates. The next logical step I would like to do is to see even more detailed breakdown for this particular campaign. What I need to do right now is to filter on this one, and this is also very small tip. I'm going to show you. If you do the right click on any of the dimension values, you can either exclude or include only the particular one, and what it does it automatically creates a filter. So it's like a nice shortcut when you want to filter something. If I only want to look on this particular one, I just click here, and only one line will stay, What I want to do right now is to add there one more dimension, which is something that I have here and I was mentioning that a minute or two ago, which is to understand to which landing pages is this campaign directing the traffic. I again want to see in much more detail whether all of the landing pages perform that bad in combination with this campaign or all of them perform that terribly. I'm just going to double click on this. Again, the report will be far more extensive. Yes, that's true. Again, let me minimize the variables and settings to better understand it. We see that the average is 0.05, which is very low conversion rate. If I will be scrolling down, let's see whether all of them are really like that bad. Seems like that, but this one goes to baskets. Again, somebody from Google should be fixing this one. I wouldn't expect the campaign to be landed to the basket page. Even though the conversion rate is higher, this is definitely not a real campaign, I would say. But again, like scrolling down seems like all of them perform terribly terribly bad. Again, this one also goes to the store at HTML with very high conversion rate, this campaign. Go down, we are getting to the point when there is very low volume of sessions. Nothing seems to be evaluated properly considering the low sessions volume. But I think you have a point when I'm trying to show you to do the more detailed breakdown. If I will right now go back to the settings and variables and try to repeat the same exercise with another lending page that exists there. Let me for now just exclude also the lending page from this. Let's assume that we have here the first one, which is 0.25, which is slightly below the average, but again, let's filter on this one to include it and see whether we won't spot something a bit more interesting. Again, I did the same exercise. I filtered only one particular campaign and added lending page as a secondary dimension. To better understand it, again, minimizing that, and going line by line, whether all of them are that bad when it comes to session key event rates. Again, scrolling down, whether I won't suppose something. Here we are, for example, even though the average is 0.25, seems like there's at least one which performs a little bit better twice as good as the average, even though it's low, but it's better than the average one. There's another one going to home page. Seems like directing traffic from the performance campaigns to home page brings far better conversion rate than than doing that to the rest of the pages. Now we can scroll down and again do the same exercise trying to spot whether there's something that works pretty well again this one. Bringing the traffic from this campaign to this landing page seems like performs quite well. 1.83 is far far better than the average for the whole campaign, which is 0.25. So I think you understand now the technique that I'm trying to show you, which is never look on the averages and try to understand what's the distribution under the hood of particular either traffic source or session campaign to understudy to the very detail. And the next logical step should be excluding particular combinations of the session campaigns and lending pages from the Google ad settings, because seems like some of them really just just burn the money investing into this, or trying to understand why it doesn't perform that well, maybe changing the keywords, which will require the further analysis, of course. But this kind of the breaking down of the traffic sources dimension will very quickly show you what works quite well, what is far below the average, and what should be fixed or maybe like stopped immediately because it literally just burns the money, like example of this combination of the session campaign, The lending page which has zero conversion rate. If we bring almost 1,400 sessions to that page and there's a zero conversion, that's probably something that we wouldn't expect if we would treat the business seriously. That was the simple technique, how to evaluate the traffic sources. 50. Tip 31 32 Checkout flow analysis: Alright. In this tip, we are going to show a real analysis of the customer journey flow. So this time, I'm again on the Google Merchandise store, and I want to focus on something that every owner of the ecommerce website should be doing, which is trying to analyze the checkout or the purchasing process. Let me show you what I mean. I'm particularly interesting on the part of the customer journey where customers already have something in a basket and are proceeding towards the purchase. So let's have a look on the simple example of how that might look like. So, I went to the listing page of Google Merchandise store. Let's assume I like this. Backpack, I'm opening the detail of it. I'm adding one to card, and then proceeding towards the checkout. So here I am, and this is exactly the part of the process that I'm interested in. I would like to see how easily or not easily are customers proceeding from that step towards the purchase? If I was about to and make an analogy from the offline shopping. It basically means that somebody already selected some goods, some product, it came to the case desk. So this is pretty much where we are in the online environment. So when customer clicks on the Continue, he or she is then directed towards the billing and shipping information, which is loading a bit more than I would expect. Anyhow, let me try to reload this since okay, it wants me to log in. So let me log in myself there. Now it should be working. Hopefully in a second or two, it will. Okay, here I am. If I would now click on the Continue, I need to click that this is my address. This is my residential address. This is the Damia address from the US, which I found just in order to be able to proceed with this tip for you. Here we are now if I would click on the Continue, I would get to the step where I need to, um, input my credit card number and then pay it. So particularly from the first step of the checkout process, I'm interested how good or bad the experience for customer is, how to find it out. We need to go to Google Analytics, and one of the reports is available within the prebuilt ones, but as we want to have a bit more of a freedom when creating the report, let's build one funnel from the very scratch. So I'm clicking on the funnel, and I would like to build funnel from the scratch. Here's the one that I already built quite some time ago, but I will clean it up and start from the very beginning. So here we are. I want to delete all of these steps, and I will start from the very scratch. I don't even want to see any breakdown. So how to proceed with this one? As we know that Google Analytics is primarily event based measurement tool, we need to specify the sequence of events which are occurring in the checkout. So I believe if you are owning your own website, you should know what events you're collecting. If you don't know, just go into one of the basic reports into the reports, engagements and event, and you will see all of the events that you're collecting. Anyhow, as we are here, the first event that I'm interested in and is being collected is basically beginning the checkout, which is the first step I'm interested in. So let me just name it as like begin. Check out, then in case I would be interested, I can add another condition to be more strict, and I would see a little bit less data. Anyhow, we should know already how to use this type of the report called funnel. But I'm adding another step, which is then followed by another event, which in this case is adding the shipping. This is the call name of the event, which is, let's call it the Shipping info, I'm adding another step, which in this case is a vent called payment info. Let me again, name it. And I'm adding the last one in this case, which will be purchase, meaning that customers actually really paid for the goods. The event name is probably not a surprise that it's purchase. I'm applying this and we'll wait for a second, and here I have the report. There's one more thing I would like to add there, which is the breakdown, the one that I really like a lot, which is the device category. Let me add it there. Now I'm going to minimize the setup part of that and trying to understand what's going on. So what is the report telling me? I believe we all are familiar what the data is telling me. It shows me what is the abandonment rate or drop off in every single step. So if I would be, again, bringing the analogy from the offline world, meaning that somebody beginning checkout means that somebody is standing at the cash desk and we want to see how good or bad the flow is towards the purchase, we might see that it's actually not that good, in my opinion. We see that customers are once they initiate the checkout, majority of them 99.4% made it to the step called shipping information when they are selecting the particular shipping method. Then here comes the first part of the issue I would like to understand, which is about, okay, approximately one third of customers won't make it from the shipping information to the payment info. So this is something I would like to understand a bit better. What's going on? Hey, one third of the customers is being lost here. And then another one as I'm proceeding towards the purchase, I can see that again, another more than one third of the customers or almost 40% won't make it from the payment information towards the purchase. So on the very last mile of the customer journey, again, Google merchandise store is losing almost 40% of the customers, just not like paying for the goods, even though they selected the shipping method and the payment method as well, they just don't complete the purchase. So the first check I would obviously do would be to scroll down and see whether the completion rate or the abandonment rate, which is the inverted value, one of another, if it's the similar or the same for both desktop and mobile. I would ignore the tablet, which is super low traffic, but just to have the sense and comparing it to the average. So I can see, Okay, from the checkout to the next step, it's pretty similar like 99% of the customers make it. But then looking on the second step which is shipping information, there already is quite a difference since about 70%, which is still low, I would expect this number to be higher. But when looking at the mobile, only 50% of the customers make it, actually, from the step of the shipping information to the payment info, not good probably. And when looking on the further step, the one which is telling me how many of the customers make it from the payment information step towards the purchase, meaning here in the payment information, all the customers need to do is just to add their credit card number, and they just can't proceed towards the next step. So again, 50% of the customers won't make it on the mobile devices from the payment information towards the purchase. So a lot of space for improvement, I would say, anyhow, we now know where and I stress the word where in the customer journey, the potential issue or the space for improvement might be, which is, first of all, the flow from the shipping information towards the payment info and then from payment info to purchase. So we have now here two options. We either ask experienced DX designer to have a look on it and potentially try to find out what's going on why customers are not proceeding. There are many methods how to do that. They are either senior enough, so they just look at the website and tell you, Hey, guys, you need to fix XYZ, or we can help them. And this is exactly where still GA four can help us, how to do that. Let's remember the flow we were trying to analyze. GA has one cool feature which wasn't available in the previous version, and it's called Path funnel Analysis. So let's have a look on it. So if I will open felt basically like copy and paste. The same URL address, but I will create a new report. But this time, I will create a path exploration. Again, somewhere in the beginning of the course, we were showing how to work with this report. But right now I want to focus on the particular step where we see the abandonment and we want to understand what is happening and why the customers potentially do not proceed from the shipping information to the payment info. So how to do that. It's actually fairly simple. Again, I will clean this report or actually, all I need to do when I come to this report is I only need to click on the start over. So let me minimize The configuration tabs, and now I'm clicking on the start over. And what I am interested in is to understand the journey from the starting point, which in this case is the event name called and we can help ourselves. It's about, from the shipping information, right? So this is the one we are searching for. So it's somewhere down below at shipping Info. So this is our starting point. All right. And now we have two options here that we can look on what was the next event that occurred after the selected one, which is at shipping info, or we can have a look on the page title or finally having also page path, meaning that we can see particular URL address to which customers went after proceeding from this step. Unfortunately, when I will click now on the page path and screen class here, you will see what will happen, which is seeing a lot of here is still so far okay. This is the checkout, which is the URL address. But the point is that way the URL addresses are then implemented in case of Google merchandise store, it's not much of helpful because there are a lot of sets. So I just wanted to show you this one. But I'm switching to the event name back to understand what potentially happened. Okay, we can see that after a chipping information, almost everyone viewed at least another page. Meaning, okay, so let's just click on this report and try to understand it in a bit more detail. All right. And now I'm starting to understand what happened, why approximately one third of the customers didn't make it to the payment information. So this is the goal I'm trying to understand here. All right, so I can see that at about two thirds made it to the payment info and what happened with the rest of them. Okay. 300 out of those who didn't proceed, viewed card again, meaning they probably clicked somewhere here on the page I'm showing you right now and got back to the card. They either could click on the back. Maybe there's some other icon, which they, here it is, back to card, even when searching here. Meaning that they are going back, 300 out of the 900 which haven't proceeded, one third of those who didn't didn't make it directly to the at payment info went back, even to the step preceding this one. It should be somewhere here if I would have it here. This is what they did. You can then have a look what happened with them. But again, then they viewed the page, of course, because this is what immediately happens after that. And then they again begin the checkout. So it means that this is basically creating a loop where customers somehow got stuck. They probably didn't get the necessary information, but we know that one third of them just goes to the view card. So when I'm minimizing this one, okay, this is a new information I have. Of course, if A four would be implemented better, we would have far more events. So I encourage you to implement in your case, far more events so we can understand such a behavior in much better detail. But I think you have the point what I'm trying to show you. We can easily see what was happening with the customers. And then when looking on that step of those customers who made it through payment, what happened next is trying to look on the similar way what happened from the customers from the payment info step towards the purchase. Why I still lost out of 1.5 thousand only slightly less than 1,000 made it through to another step, meaning I lost 40%. So what happened? These who proceeded again. They haven't ended up the session, but they proceeded to see another page view, and what happened then? All right, seems like the next event that occurred was scrolling. So what happened next? After scrolling, some of them again made it back to adding to the payment info. Again, creating a loop seems like something is unclear for customers because they are constantly getting back to the previous step. If I would go back now here and minimizing this step and trying to understand what happened here, again, some of them scrolled, some of them purchased. So I'm analyzing it furthermore, trying to understand whether I won't see something major among those small next steps I see here. Some of them again, viewed card again, viewed promotion, meaning they probably got to the page where there was a promotion carousel or a banner, or this is what I would associate with a particular event like view promotion. So again, we might see very precise detail of what was happening towards the funnel and try to understand or help the UX designer who is then supposed to analyze those drops off. They're telling him, hey, we see loops that are happening, especially in the first step that customers are getting back to view card step, which is preceding the checkout. Or some of them are just going constantly to the previous step of the checkout funnel. So something might be unclear and not properly explained and so on and so forth. So yes, these were another two steps, trying to show you how to work with a very detailed analysis of customer flow from one step to another or through a particular larger chunk of the customer journey. In this case, it was a checkout process. So yeah, feel free to do the same. 51. GA4 - Account structure and user's permissions: Another two tips will be about how data is organized within the account property structure in Google Analytics and how to manage the user permissions, adding them to either property or the whole account. So in order to explain you that in more detail, I need to go to the admin section and here we can see how the data is organized. So the highest entity in Google Analytics is something that is called account. And one account can have multiple properties. How to translate it into human language? Let's assume that account is something like the business or the business owner who can have multiple businesses as a properties. What that means in reality is, let's assume there is a company who owns multiple domains in multiple businesses and would like to have the data organized within one account, so we can easily then manage the properties within that. If I would about to describe what is a property, probably the best definition is its domain. So unless there's not something like super specific as a reason that you would like to measure two domains within one property or like within the one data stream, Please don't do that. You will save a lot of time by fixing the things in terms of like crunching the data for the feral analysis. So if you keep the rule that one domain equals one property, you'll be pretty much on the safe side when it comes to either proper technical setup and then data analysis. This is what is property. This is the closest definition, I would say, property is domain. And of course, one company, one business can have multiple properties. So if you keep that rule, as I said, one property is one domain, you will save a lot of time, and you'll be on the safe side in terms of both technical setup and the analysis. So this is about the relationship between property and account. And then the second thing I would like to show you and please keep that in mind as well, is the way you give the permissions to either property or to account. So bear in mind that the same level of permissions in terms of what somebody wants it's added to either property or to account. Can do. So let me show you if you would like to add someone to account, meaning on the highest data organization entity within GA four, what are the options? If I will click here on account access Management, wait for a second. You can already see that there are some automatically added accounts to my GEF account, and there's also myself as the administrator as the one who created the whole account. If we want to add somebody new with different level or maybe like the same level of permissions, I will need to click here and click to add users. Here the window opens. We need to input here e mail address, which is a Google account. It needs to be Google account, not necessarily G mail account, but Google account. And here are the levels of permissions that exist there. From the non, meaning like not even seeing the Google Analytics account to the administrator. So feel free to read it in very detail what that is. But the first level of when somebody can edit actually in the setup and change the data, the way the data are calculated is the marketer role. So from the marketer and above, please rather have somebody who is experienced and know what she or he is doing when playing with GA because you don't want the data to be broken. Editor can pretty much do everything except adding new people to the Google Analytics property or account. So editor should be somebody who's quite skilled and know what he or she is doing. Again, when he's fixing the data or changing the campaign windows or changing the way the goals are set up or pretty much like any kind of the set up that is possible. So we've already been through a couple of examples, what everything can be set up, so we know how much it can change the underlying data, and then also the way the data is displayed within GA. Then there is administrator, meaning like having all of the permissions within the roles which are below that, plus ability to add and remove new users, including himself or herself. There's one more setup which is possible. It's for the reason if you have somebody like externally managing your data or helping you, for example, with performance campaigns, and you don't want, for example, them to see the revenue metrics or for some reason, where it makes sense to you not to see the cost metrics, if you connect it, for example, Google Ads and Google Analytics four. Each can just like, choose this option, and the people won't see the cost or revenue metric. So it's the option. I leave it up to you where it makes sense, where and when it makes sense for somebody not to see this metric. So this is the way the data, the user permissions are organized, and please keep that in mind. For every property and for at least every account, please have at least two people with administrator rights because anything can happen. You know, the businesses are split. So if the only guy who had the admin rights left, then you no longer have anyone who can add new users. Or somebody just forgot the password. Also happens, and you have no one who can add new people to the GA account. So it's sort of like treated it as your own house, right? So only very few of us has only one key from the house. There's only at least two of them, and maybe we give another key to our friend or to our family in case of something happens. So treated very similarly and have at least two people with admin rights. It's always better to be safe than sorry when anything happens. So yeah, pretty simple thing, but please don't forget about it. 52. OUTRO: Congratulations. I hope you found the course useful and full of interesting tips and information. Keep in mind that analytics is the way you think about the later. Verify the hypothesis for multiple views. And don't be hasty when making decisions. I would really appreciate your feedback. Please write me a couple of lines. Would you Liked what you didn't like that much or what should be improved. Thanks and happy analyzing.