Analyzing User Retention: Insights on Cohorts and Engagement Patterns

Okay, so retention, retention overview, again, it's looking at returning users versus new users and user retention by cohort. Percentage of new user cohort on charted date. So people came in on that date who returned each day over the next X number of days.

And it says, it looks like a forward view, we're only calculating if there's enough data available. So this might not be relevant to everyone based on their traffic. And then engagement by cohort.

Average engagement time on new users on the date who return each day and look at how long they stay, right? So it's looking to see, again, user retention. You had the last 42 days. For everyone who came in on that day, how many of them came back each additional day? And if you see a small percentage each day, less than 1% each day, right? And you can see if that tracks over time or changes over time, and how long they stay on over time.

Start off peak there, day two, but then you see another high mark there. So you see the patterns of that over time, and then the same thing around the value of the customer, lifetime value of the customer, right? All right, so that discusses the life cycle. You're looking at acquisition, where they came from, engagement, what they're doing on that site, monetization of the e-commerce store, are they buying, what is the revenue picture, and retention, are they staying, are they returning, are they dropping off, how long over time, how does engagement look amongst them, et cetera.

All right, so now let's look at the last couple of categories of user attributes. And now this is getting at the demographics, you know. We talked about the different types of reports and the demographics of the audience.

This is audience demographics, which is obviously essential for your marketing purposes. So you start with an overview. So where they come from by country, and you can click on there, there'll be a lot more countries.

By city, by gender, right? So male, 61%, 38% female, kind of makes sense. I think that a targeted audience for this would be techies, people in the tech industry, which is still more predominantly male, right? Hopefully changing, but still today. So that would make sense, right? If it were health and beauty, it might be skewed the other way, right? By interest, though, so interestingly, no pun intended, you can also see the people visiting the website, well, look at that.

The number one interest is technology, technophiles, right? What I was just saying, who's gonna buy a Google sweatshirt? Maybe someone in the industry worked for Google, an SEO specialist, perhaps. Banking and finance, news. So where's this information coming from? These are the websites and the blogs that these users are engaging in.

So it's demographics and its interests, right? Behaviors, because that's how they know that they're consuming this type of content, right? So coming back by age, right? So, what is another assumption we might make that these are younger workers or a younger audience? And sure enough, you know, tech, right? So we're looking at 18 to 34, you know, the, what is that? Over 13,000 of their user base are within those two months. You know, so those two age groups, right? And it pales off as it gets older, with the smallest amount being, you know, the 65 and older. Right? So, you know, so you do, and then also by language, right? So predominantly English, but some Spanish.

Even as a global website, global brand, you can see how that might just be useful. Let's say, well, if you see that a particular, you know, a growing percentage or a sizable portion of your audience, let's say you have a website here in the States in a multilingual city, or, you know, the whole country multilingual, you find out a large percentage of your audience is speak Spanish or Chinese or something else. Maybe that, you know, will be an impetus to create a site in that language, or they can click over to that language.

You know, it's expensive to translate your websites. So you would only want to do it if you feel that there's a demonstrated need to do it. Right? And then again, the lifetime value.

But again, you're looking at demographic information, where they come from, their age, their gender, and their interests. This is where you find it. And you can, this is an overview. You can go into the demographic details.

That gives me some insight. On October 1st, 2025, active users in India dropped to 129, a sharp decrease from the expected 245. When it says expected, their models were predicting it should be that high based upon previous patterns.

And it says that they dropped off in search, right? And in Chrome, right? So that is something that, yeah, this marketing group could follow up on and try to figure out why they're doing worse in organic search. Maybe one of their competitors is, you know, been getting, you know, a lot more visibility there or something along those lines. Or, you know, maybe competitor is selling, you're running ads and not even getting to your, you might be in first position, but they even purchase Google Merch Store as a keyword, and they're showing their ads in front of that.

So you would look to see if any of those factors are in play. And it tells you by country, how many were active users, how many were new users, engaged sessions, engagement rate, event count, and average online active user engagement time. So among those who are active, how much, you know, how much time they spend, event count, the total number of events, total number of key events, right? Because not every event is a key event, as we know.

It sort of looks at it by sessions and by users. That's where I wanted to point out. Remember, those are the two ways of looking at visits: how many total visits or sessions, and how many people.

So it's looking, you know, engagement time by active user and for active sessions, right? And then it tells you for all the different countries, Mongolia, like whenever you click on a particular column, which I accidentally did as I was trying to scroll, it then sorts by that column. So it's just showing us that in Mongolia, they had the highest active online engagement time, and in Kuwait, Aruba, and Taiwan. So some of the countries that actually had the most users were not getting the same level of engagement time.

So, however, that's relevant, if you understand their business, it might lead to some insight, seeing which countries have the highest engagement time and how that correlates to the sales from that country, and why it is particularly the case? If these sites are in different languages and something about the language, try to figure that out and understand, like why so much higher engagement in certain places than in other places, and if that's correlating with sales, then it might be one of the things you would sense. All right, so that's that for that page. And then audiences.

Now these are audiences that have been created or are custom audiences, or suggested audiences, or template audiences. These have been put in place by the Google Merge team, and this is what's being tracked, right? The various audiences and how many of these audiences, you can compare it to all users, those who are non-purchasers, how many of them are new to the site, how many sessions do they have, how many views, right? How much is the duration of their session, the total revenue for each of these groups, and the total number of users in those groups? And look at this, of those people who have scrolled at least 75% of the page, remember that and create an audience, offer the various metrics and dimensions.

And in this case, there are 309,000, so you can see how, if this correlates to more sales revenue or not, if you compare it to your normal rate of sales, you're only targeting now the people who, when they came to the site, they scrolled more than 75% of the time. Now let's see how that correlates to their purchases, right? Low average purchase value, family day, comparison shop is now again, this means something to them. This is one of those, the suggested audiences you might remember, particularly 28-day top spenders, what did that correlate to more revenue versus other audiences? Well, that's what you would look to see.

This is what Google predicted would be our top spenders. Does it actually work out to be the case or not? You can determine, now that you've segmented this information by audiences, you can see what their revenue is compared to and the average revenue, et cetera.