At Apsalar, we’re focused on providing the most powerful set of discovery and actionable analytics for mobile app publishers to increase user engagement and revenue. Our mission is to help our customers improve retention and increase monetization from engaged users. We recently launched an educational blog series to help developers understand the various types of analyses available to better analyze user behavior and increase user engagement. Catch up by starting here.
Happy New Year everyone!
In recent weeks, we’ve given you information on how to best use cohort analysis in evaluating user behavior. It’s a powerful form of analysis that every publisher should have in its toolbox for building a sustainable mobile business.
A cohort is a group of users that has completed a specific action within a specific period of time: for example, a set of new users that launched your app for the first time on Wednesday, January 18. A cohort can be used to analyze three key dimensions of user behavior:
That brings us to this week’s post. (Hint: it’s all about how to make more money).
Whether you generate revenue through digital goods purchases, m-commerce, display advertising, 3rd party offer-walls or cross-promotion upgrades, you need to assess how well your application is monetizing. Top line revenue numbers that may be gradually increasing, can in fact hide a decline in average revenue per user (“ARPU”) and the life time value of a user. As long as you keep adding new users, things may appear fine, but are you really maximizing the value of your user base? To understand revenue drivers, ask yourself these questions:
To answer these questions, all you need are cohort monetization analyses.
Let’s look at a few examples:
In (fig. 1a), we’ve grouped users together by the first time they have launched the app, and then calculated the percentage of users went on to make a purchase. For example, in the first row (12/25), there were 34,851 users of which 1.11% made a purchase on Day 1. But as you move to the 2nd and 3rd day, the percentage drops sharply to 0.39% and 0.22%, respectively, before it evens out to roughly 0.10% per day. We can take the same cohort (fig. 1b) to show how much revenue has been generated, and fig. 1c to see what the ARPU for that cohort is.
If you want to increase revenue, you can: i) test a personalized incentive program, perhaps on day 3 or 4, or ii) make a change to the app designed to drive more sales beyond the first day. Again, cohort analysis can be used to determine if the incentive program or app change has a positive effect by looking at the cohort groups and their spending after these changes, and comparing them to the prior cohorts.
Segmentation of users into further sub-groups can also provide valuable monetization insights. In this example, we can look at the same cohort for all users in that group (fig. 2a) and compare that to only those users in the cohort who have finished the tutorial. We can see that users who finished the tutorial spend more money than those who bypassed it.
The goal now is to get more users through the tutorial to increase revenue. This might be achieved by making the tutorial easier to complete and by displaying it more prominently within the app so that more users access it.
There it is – the power of cohort monetization analysis. Use it or lose it as we like to say. Check back in for our next post as we show how to use cohort analysis to measure the performance of users from different acquisition channels.
If you have any questions, feel free to drop me a line at firstname.lastname@example.org.