Post by mdsakilhasan765 on Oct 2, 2023 5:19:35 GMT
t the post you have understood the importance of knowing how the scope affects the way data is collected, processed and used in Analytics. The most important key is that sessions are inferred, not stored, and that scopes form a one-way hierarchy. Isn't it easy? Consider that every time you ask for a dimension or session metric , such as the number of page views, average session time, source / medium or conversions, the data is first processed by grouping it by sessions and discarding intermediate hits . When you ask for hit data, such as the url or title of the page, the loading speed or the time of the visit, the data is not grouped that way. So far so good. But what happens if we try to mix dimensions or metrics from different areas? Well anything can happen... hehehe Sessions per page vs Landing page First example, this one is quite common. You want to know how many sessions have passed through a certain url. Here you are mixing a session metric (total sessions) with a hit scope dimension (page url). Analytics will first identify that there is a session metric and group all the information into sessions, with the information summarized for each session, discarding all the hits in the middle. So the only url that will be left in that result will be the url where the session started (the url of the first hit of the session). TRUE? So when you think you're seeing sessions that have gone through a url, you're actually seeing sessions that have started at that url .
But nobody tells you that there are no sessions that have COUNTRY EMAIL LIST started at another url and have passed through there. Imagine then that you are trying to get a conversion rate from an objective. 5% comes out and you say “wow, I can invest a lot in publi with this high conversion rate.” You run your campaign and you crash. Do you catch? Tip: try the “unique page views” metric. Conversions per page Conversions that each page brings us? Imagine that you have a main menu with a “register” button that opens a popup. In that popup, a form and, when the form is submitted, a conversion in Analytics. Being in the main menu, you can register from any url. Since you have a lot of blog traffic, you want to know which articles are bringing you the most conversions. The logical thing would be to think about painting a little board whose dimension was “page” and the metric Objectives met. Conversion rate? Well too. But again we find ourselves in the situation where goals met or conversion rate are session metrics and the page is a hit dimension . Hits do not have associated sessions, broken reporting. At most you could get a list of which landing pages bring you the most conversions. It would be fine, I'm not saying no, but it's not what you're looking for. So do you understand where the shots are going? Fields calculated with metrics of different scope? A few weeks ago a student in my Data Studio course asked me why a calculated field wasn't working well for him. In this case, I wanted to analyze a landing page and the scroll depth that people reach. As a user scrolls down a page, with Tag Manager it sends events to Analytics with that scroll percentage reached.
Making a table with the number of unique users per event would give you an idea similar to a conversion funnel , since there would be many fewer users who reach 50% than those who reach 20%. Of course. Yeah? Since the events are hits and the hits have a user associated with them, this works fine. But this student, who does not stop at the easy (bravo for him) also wanted to add “the rate of” or, in other words, what percentage of the total users reach each point on the scroll. Are you following me? Then create a calculated field : “total events” / “users”. But that would be the total of the website, so you decide to add a filter to that Data Studio control, which only includes a specific url. But the results are far from reality . Can you think of why that might be? TRUE! Because you are calculating between metrics of different scope . The total events is a session metric, users is there but, the moment you add the filter at the page level, you are messing things up. When you perform this type of calculations, almost without realizing it, the information you see is biased... be careful with this. Last conclusions My mother! Hahaha. If it is impossible for me to make short posts. But hey, at least I fulfill the challenge I have with Vicent . And I plan to fulfill it... Perhaps this post seemed very technical to you, perhaps too dense. I hope not. You may think that it is too advanced to need it but, with all my love, it is the opposite. This post is basic, very basic. It is something that we should learn from the beginning, to save ourselves headaches . Digital analytics is not something extra but rather something that helps us make the rest of the things we do go better. We can learn a lot from analyzing.
But nobody tells you that there are no sessions that have COUNTRY EMAIL LIST started at another url and have passed through there. Imagine then that you are trying to get a conversion rate from an objective. 5% comes out and you say “wow, I can invest a lot in publi with this high conversion rate.” You run your campaign and you crash. Do you catch? Tip: try the “unique page views” metric. Conversions per page Conversions that each page brings us? Imagine that you have a main menu with a “register” button that opens a popup. In that popup, a form and, when the form is submitted, a conversion in Analytics. Being in the main menu, you can register from any url. Since you have a lot of blog traffic, you want to know which articles are bringing you the most conversions. The logical thing would be to think about painting a little board whose dimension was “page” and the metric Objectives met. Conversion rate? Well too. But again we find ourselves in the situation where goals met or conversion rate are session metrics and the page is a hit dimension . Hits do not have associated sessions, broken reporting. At most you could get a list of which landing pages bring you the most conversions. It would be fine, I'm not saying no, but it's not what you're looking for. So do you understand where the shots are going? Fields calculated with metrics of different scope? A few weeks ago a student in my Data Studio course asked me why a calculated field wasn't working well for him. In this case, I wanted to analyze a landing page and the scroll depth that people reach. As a user scrolls down a page, with Tag Manager it sends events to Analytics with that scroll percentage reached.
Making a table with the number of unique users per event would give you an idea similar to a conversion funnel , since there would be many fewer users who reach 50% than those who reach 20%. Of course. Yeah? Since the events are hits and the hits have a user associated with them, this works fine. But this student, who does not stop at the easy (bravo for him) also wanted to add “the rate of” or, in other words, what percentage of the total users reach each point on the scroll. Are you following me? Then create a calculated field : “total events” / “users”. But that would be the total of the website, so you decide to add a filter to that Data Studio control, which only includes a specific url. But the results are far from reality . Can you think of why that might be? TRUE! Because you are calculating between metrics of different scope . The total events is a session metric, users is there but, the moment you add the filter at the page level, you are messing things up. When you perform this type of calculations, almost without realizing it, the information you see is biased... be careful with this. Last conclusions My mother! Hahaha. If it is impossible for me to make short posts. But hey, at least I fulfill the challenge I have with Vicent . And I plan to fulfill it... Perhaps this post seemed very technical to you, perhaps too dense. I hope not. You may think that it is too advanced to need it but, with all my love, it is the opposite. This post is basic, very basic. It is something that we should learn from the beginning, to save ourselves headaches . Digital analytics is not something extra but rather something that helps us make the rest of the things we do go better. We can learn a lot from analyzing.