What is Predictive Mobile Analytics?

Predictive mobile analytics help companies make data-driven predictions of what specific actions and events a user will take in an app.

With over 2.8 million Android apps and 2.2 million iOS apps, it’s crucial for companies to understand their mobile app users, including what keeps them engaged and what makes them leave the app. Predictive mobile analytics gives companies the ability to look into the future and understand which of their app users are likely to convert or churn. This allows companies to shift from being reactive to proactive in how they engage with their app users, which helps to reduce churn and improve conversions and retention.

Why Predictive Mobile Analytics Are Important

In the last few years, machine learning algorithms have become very good at building user behavior models. With the right software applying these models, you can go from passively collecting data on in-app activity to identifying key behaviors that improve conversions.

Predictive mobile analytics can help a company make data-driven predictions of what specific actions and events converts a user to a paying customer.

Manual data analysis is too slow, too backward-looking, and too simplistic to gain the kinds of insights companies hunger for.

Use Cases for Predictive Mobile Analytics

  1. Being able to predict which user actions within 30 days of onboarding are most likely to result in users converting to customers.
  2. Automatically triggering push notifications based on what mobile app users are likely, or unlikely, to do in the feature.
  3. Determining key experiences that users of your mobile app need to have to be more likely to convert to customers.
  4. Identifying churn patterns: patterns of behavior that are more likely to lead to undesirable outcomes.
  5. Focusing on social data of users, identify user behavior patterns that are just missing being likely to try, or adopt, or convert to new customers. In other words, what is missing from the experiences of these less likely customer.

The Limitations of Predictive Mobile Analytics

There are three big factors in how well predictive mobile analytics will work for your company or application:

  1. How good, for the purposes of predictive mobile analytics, is your data set? Is there enough data for analysis and the right kinds of data? Is the data granular enough so that iteration after iteration of machine learning algorithms can work?
  2. How appropriate, given your data, are the algorithms your PMA software uses? There’s a huge and growing number of different approaches to different data sets.
  3. How well do innovations in machine learning algorithms make their way into to tools or applications you are using?

This kind of work is more prediction than analytics: You are not going to find hard and fast answers. The key is in testing the models as they continuously develop and improve.

Deep evolving understanding of your customers and their journeys through your app will lead to understanding what the data conclusions mean and how your company can act on these insights.