A picture can tell a thousand words.
Yeah, we’ve used that opener before – when we launched the revamped charts for statistics. But this time we’ve worked out something really special: charts, graphs, diagrams for your cohort analysis.
The cohort analysis is designed for fair and comparable assessment of different marketing outreach, and any visualizations need to be true to that spirit. That’s why we’ve sat down with our graphics team and have been thinking long and hard about how to represent user engagement, retention, and lifetime values per day, week or month after install - for an infinite amount of time into the future. We’ve learned a lot from the visualization revamp of your main statistics and brought all of those lessons to bear in our new cohort analysis charts.
The new charts allow you to pull up any metric and apply any of the standard cohort filters. With Custom Views, you can save any chart from your cohort analysis to pull it up really quickly.
Some questions you can answer with cohort analysis & with the new charts:
- Do users engage more quickly and intensely with the new version of your app? As time goes on and users spend more time in your app, eventually only the most engaged remain. But just how engaged are they - and how quickly do they engage? To compare two segments from different times fairly, you have to use cohort analysis in order to isolate the results of your optimizations from the natural tendencies of user behaviour as they spend time in the app. Cohort analysis shows you engagement per day after install – allowing a fair and comparable evaluation.
- Does the ARPU increase faster among users from your cross-promotion than from your e-mail newsletters? Different channels may acquire significantly different volumes of users on different days. Cross-promotion is usually a steady stream of new users, whereas e-mail will be concentrated at a few specific times when newsletters are sent. If you compare the ARPU of these two channels by date, the results will be completely different for different dates. Instead, you need to compare by day-after-install to get the true picture regardless of the acquisition volumes.
- Which campaigns bring the users that stick around in the app? Perhaps the most obvious cohort analysis metric is the retention rate - which is always determined by day-after-install, like day-7 or day-14. But why stick to the obvious days, weeks, or months? With adjust’s cohort analysis, you can visualize the full development of the retention rate as you pull out into 30 days, 12 weeks, or 24 months.
Not yet using adjust, but would like to answer any of those questions? Get in touch for a full demo of the platform today.