In this post we will take a look on AppMetrica brand new cohort reports. These are Retention Analysis report, and Cohort Analysis.
Let's see new opportunities for app analysis brought by these reports.
Retention Analysis report
The Retention Analysis lets you group users by various sections. We also have a new Retention Dynamics metric.
Split your audience in a smart way
Users can be grouped not only by the date of installation but with some other properties:
- Media source - traffic source level
- Tracker - ad campaign level
- Tracking URL parameters (sub_id, creative_id and others).
These user splits allow you to evaluate the effectiveness of the advertising channel: higher the return rate — higher the traffic quality. Thus you can easily find ad partners which bring the users with the highest retention index.
With the the appropriate conclusions having made, you can set retention as KPI for your ad partner.
Retention Dynamics in details
Retention Dynamics shows a change in the average level of retention over time. In the report, you will see how the retention of a certain day/week/month is changing (W1, W3, W14, etc.), depending on the installation period.
This metric is useful if you want to estimate how external factors affect users return rate (e.g. PR campaigns, seasonality). Were users more likely to come to the application after adding a new feature? How strongly did seasonality affect returnability? Retention Dynamics helps to find answers to these questions.
Also Rolling Retention metric is still here. You can read more about it in one of our previous posts.
Minimal cohort size
Another novelty that makes the report more convenient. This option will exclude low-population cohorts when you're grouping users by traffic sources.
New Cohort Analysis— new insights
The “Cohort Analysis” report is focused on the user engagement. Its table shows the cumulative number of events per cohort, as well as the average number of events per cohort user, on any day of your interest (D1, D2... D50).
The report displays the frequency with which users come to the application and perform target actions.
Here, as in Retention analysis, you can group users by traffic sources. So you can look at the effectiveness of the advertising channel from a different angle:
The report allows you to visually compare the frequency with which users perform target actions, coming from the various channels. This gives an understanding of which of the channels brought the most valuable users. In particular, from the point of ROI and monetization.
This approach opens up scope for insights.
For example, each purchase in your app gives you 1$ and the estimated life time of your user is ~180 days. Knowing that you can calculate LTV for users from different cohorts and compare them.
To do this, open the Cohort Analysis report for 180 days and select the “purchase” event in the “Event” filter. Multiply the purchase price by the average number of events on the last day of the period and get your average LTV.
We hope that the new cohort reports will help you not only measure the quality of traffic, but also look at the activity of users from different sides.
If you have any questions about applying these reports or about how new metrics are considered, ask them in the comments section, and we'll help you figure it out.