Predictive Churn

Predictive churn analyzes your audience for users that exhibit behaviors indicating they are likely to become inactive, and tags the users as High, Medium, or Low risk. Use predictive churn to identify users who are likely to leave your app, and take steps to retain them.

Churn is a natural component of mobile engagement ebb and flow, and while a certain amount of churn is normal and healthy, there are ways to identify churn risk factors and take actions to prevent your user base from eroding.

With predictive churn, you can identify users by their likelihood to churn, based on risk profiles that we generate via machine learning, using gradient boosted decision tree methodology. Our churn prediction model is trained to detect the most relevant risk factors for a churn outcome, and assigns either a high, medium, or low churn factor to each user who has been active in the past 60 days.

Risk factors are updated weekly and exposed as tags for audience segmentation, analysis in Performance Analytics, and exposed as events in Real-Time Data Streaming.

You can also target your audience by risk profile or use changes in churn risk as a trigger for an Automated message.

The Predictive Churn Model

Predictive churn belongs to Airship's Predictive suite of products, which uses machine learning to predict user behaviors and optimize engagement strategy for customer lifecycle marketers.

The predictive churn model is trained on recency and frequency of notification sends and app opens for a cross-section of anonymized apps. By including your app key as an input, the model tailors its predictions to your audience based on your app's feature usage — recency, frequency, etc.

active user
A user in your audience with at least one app open within the last 30 days.
inactive user
A user that had a predictive tag of high, medium, or low at a given point, and has become inactive, showing no app opens within the last 30 days.
A churn outcome is one in which a user goes from active to inactive, i.e., Airship has not seen any app activity (measured by app opens) in the last 30 days. Push opt-in status is not a factor, so it is possible to have an active user that is opted out of notifications but still active.

A churned user is not the same as an uninstalled user.

churn risk

Predictive churn makes a prediction about the likelihood of a future churn outcome, meaning that a user will go inactive. We assign one of three measures of risk for such an outcome as follows:

  • High — Users most likely to become inactive
  • Medium — Users who exhibit signs of potentially becoming inactive
  • Low — Users least likely to become inactive

Predictive Churn Use Cases

  • Target users with offers before they churn.
  • Run an A/B Test with a single variant and a control group to measure the message's impact on churn.
  • Trigger an automated message based on a change in risk group.
  • Create an audience segment that blends risk groups based on the type of messaging and your goals.

Predictive Churn in the API

In the Airship API, predictive churn is represented as a tag group. You can target churn tags using the ua_churn_prediction tag group to target high, medium or low risk members of your audience.

Predictive Churn Data and Analytics

If you are a Performance Analytics customer, the Predictive dashboard helps you track churn risk factors over time. The Predictive dashboard provides a view into Predictive Churn risk groups, distribution of users across risk groups, and the performance of churn mitigation tactics.

Predictive tags update every Sunday, and reports default to the most recent update.

Use Cases

  • Explore added or removed Predictive tags.
  • Slice user behavior by churn risk tag.
  • Export ad IDs, named users, and channel IDs based on their risk category.
  • Export named users and ad IDs based on app opens, uninstalls, and risk category.
  • Find churn cohorts and slice by the users' current tags.
  • Find churn cohorts, filter, then analyze a funnel of past behavior.

Predictive Churn Events

Predictive churn is represented as a tag applied to a channel. Therefore, changes in predictive churn status are represented as TAG_CHANGE events in Real-Time Data Streaming.

Videos About Predictive Churn

Before diving into the user documentation, have a look at these short overview videos: