Analytics for Apps

How you can dive into your app data

By Webtrekk Senior Product Manager Ole Bahlmann


To provide you with quicker, more intuitive analyses for understanding app engagement and retention, we added engagement metrics and predefined cohort analyses to Webtrekk Analytics.

Why does this matter?

Well, engagement can be measured in many ways. But in the field of apps, the established metric is active users. Not registrations or downloads or other more “traditional” web metrics.

Active users measures the number of unique users for a specific timeframe – one day for daily active users (DAU), seven days for weekly active users (WAU) and 30 days for monthly active users (MAU).

So, if you expect your users to use your app on a daily basis, daily active users is the metric you should observe. If once or twice per week is more realistic, weekly active users better reflects your engaged user base.

This data comes to life with engagment metrics and cohort analyses.

Here is an example: Each row shows the cohort, or the group of users that first used your app on that day; each column shows how that usage evolved over time.

The first column (“Day 1”) is the darkest colour because, of course, everybody used the app on the first day. Each column after that shows engagement for the following days.

This example suggests something wrong with the 4 April cohort: It’s smaller than the average cohort. But since the other cohorts are not affected – Day 2 for the 3 April cohort looks just fine – it’s likely a problem for new users only. This could be a marketing campaign went awry, or maybe even a technical glitch.


Let's look at another cohort analysis. In this case, you see that more users’ first day was 4 April (“04.04.2016”) – maybe this reflects a marketing push. (Different events and marketing campaigns can be easily incorporated into cohort analyses as “Events” to remind you why there might be anomalies.)

At the same time, you also see that the users whose first day was 4 April did not have a higher retention percentage: Compared to the 1 April cohort, engagement rates on Day 2, Day 3 and Day 4 are all the same. There are more overall users for 4 April, sure, but the "stickiness" is basically identical.

This next cohort analysis shows that usage spiked on Day 3, hence the darker blue.

Such patterns could come from coupons, push notifications, email offers, etc., that are sent to all users on their third day (a so-called “drip campaign”). Here we can see that whatever is happening on Day 3 is helping – but that it is not carrying over to Day 4.

Let's look at two more.

Here is another anomaly: A diagonal line running through all of the cells that show activity on 6 April, which had a low number of first-time users, as well as suboptimal engagement across each cohort group. This could be a site outage, for instance, or a public holiday.

Cohort analyses can also show you where performance is high. Here the 6 April performance is higher than average, perhaps reflecting a marketing push.

These metrics are great for benchmarking, and provide useful insights into the development of your audience. And once you combine them with Webtrekk’s powerful filter engine, you have access to even greater insights.

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