An emerging breed of data-savvy marketers are bridging new tech and proven statistics to level up their app retention strategy.
No matter the app size, category, or business model, retaining app users is a big problem -- and an opportunity.
The Apple App and Google Play Stores each offer over 1.5 million apps vying for the same audiences. These stores are buyer’s markets filled with products that often have high substitutability. Just search for “photo editor” or “subway maps” and brace yourself for literally hundreds of options. The market for app downloads is additionally characterized by cut-rate switching costs -- with the table-stakes-ization of free-to-download and ever-decreasing download times among the most prominent drivers of shrinking barriers to install.
If you are an app publisher today, it’s likely that a user can find and download a substitute app that seemingly provides the same value as your app - in less than a minute and or free. No wonder retention rates are abysmal.
Yet, quickly leading users to their ‘a-ha’ moment and encouraging them to ‘snackify’ their experience across shorter, more frequent intervals will become critical for the largest publishers earning revenue through ads, in-app purchases, or both. And even for the long-tail of smaller app publishers, high engagement likely has a positive effect on discoverability in the app stores.
Enter Predictive Churn Management
Now here’s the good news: retention, generally, is not a new problem. For decades, the minds and budgets of data-savvy marketers from big traditional industries from telecom to retail have invested in sophisticated predictive modeling techniques to predict subscriber and shopper churn risk and preemptively engage at-risk customers.
Three key trends are now surfacing opportunities for app publishers to unlock this same control and predictability over user behavior:
The result will be a supercharged approach to how retention is treated across the user lifecycle, based on a forecast of how likely each user is to churn. Here’s how it will work:
Ongoing, Evergreen Journeys
This predictive approach to user retention will eventually scale with the support of marketing automation towards ongoing, evergreen journeys humming quietly in the background of more pointed, one-off blast campaigns. To help illustrate what this future approach will look like, let’s consider the example of a fictional sports media app: SportsMob.
As soon as SportsMob’s active users reach a predefined threshold of churn risk, they will be funneled into a journey of chained touch-points designed to optimize retention per dollar spent:
By staying one step ahead of users’ next moves, this predictive approach will ultimately give marketers the ability to measure and control the long-term value of their user base.
So what can you do today to get started with a predictive approach to churn management?
The first step is making critical inquiries into the nature of your product, and value of your users:
At Localytics, our data science and predictive analytics team is dedicated to helping our customers answer these questions and putting into place the appropriate methods for forecasting and controlling churn at scale. It’s just our first step in leveraging predictive modeling for deeper app marketing automation.
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