Using the right metrics for a data-driven app
By Pratik Shah
Discussions across the mobile ecosystem on the need for application developers to become data driven are ongoing.
To address this, most developers, when launching a new app, opt to add in an analytics SDK (software developer’s kit). But far too often they then experience an inability to go beyond vanity metrics, information overload and the inability to gain truly valuable insights.
While speaking to new app developers, I realized that most of them need to go beyond the analytics hype of “more is better” and focus on a few key metrics.
• Cost per acquisition (CPA)/Cost per customer (CAC)
As the name suggests, this is the cost of acquiring each user for your app.
Developers can acquire users through performance ad networks or organic methods such as cross-promotion and viral campaigns. The idea is to keep the cost per acquisition as low as possible so as to maximize potential profits from each new customer.
• ROI (Revenue/CPA)
In the mobile marketing space, we talk about ROI as the ratio of value contributed by each user acquired as opposed to the overall cost of acquiring that user.
There has been a recent movement from “measurement by downloads” to “measurement by actual revenue/engagement,” meaning that developers are beginning to care about the long-term relationships they can potentially cultivate with new customers.
Measuring the value of users while also understanding the where they came from helps developers understand the true value of each acquisition source.
• MAU, stickiness (DAU/MAU)
MAU (monthly active users), DAU (daily active users) and stickiness (DAU/MAU) are the most basic health metrics applied to understanding the app usage trend.
For example, understanding your MAU trends over time, with some attention paid to seasonal events, can be useful in forecasting.
Similarly, understanding stickiness helps shed light on how frequently users return to the app within a month and also provides developers grounds to experiment with app modifications to improve the rate of return traffic.
• Session lengths and frequency
These metrics provide key insights into engagement levels of your app.
Tracking these benchmarks over time for your app category would provide information on where and how user engagement can be improved.
Improving session lengths and frequency will directly improve your app revenue, whether you monetize using ad based revenue or in-app purchases.
• Retention cohort, average lifetime/user
Measuring retention cohort to track retention of new users acquired over a period of time is very valuable as you keep tweaking app behavior to optimize both user retention and average lifetime, also known as duration.
Cohort analysis is the most important metric you can follow while making changes in your app or campaign and measuring the impacts of such changes.
• ARPU, ARPPU and percent of paying users
ARPU (average revenue per user) and ARPPU (average revenue per paying user) are best measured on daily user level.
Also, it is helpful to split paying users as whales, who spend the most on in-app purchases (5 percent); dolphins, who spend a middling amount within apps (45 percent); and minnows, who will spend the bare minimum, if anything at all (55 percent).
We have seen that whales usually contribute about 50 percent of total app revenue. Whales and dolphins combined contribute up to 90 percent.
• Lifetime value (LTV)
LTV is an estimation of the average value – usually revenue – that individual users contribute over their lifetimes.
Optimization of LTV is the mecca for each app developer, either by increasing the number of paying users, the average payment per user or the lifetime of each user.
Mobile gaming app developers who are adept at optimizing LTV of their users see their average LTV hover around $20.
Again, the devil is in the details: the LTV of whales can be as high as three or even four digits, and minnows’ value can be as low as single digits.
IT IS ONLY through using the right metrics that you can transform your app from a product to the cornerstone of a successful business.
Deep insights into user behavior will tell you more than your income alone ever will, and will ensure the long-term success of your project.