Churn is a natural part of 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 update weekly and exposed as tags for audience segmentation, analysis in Performance Analytics, and exposed as Tag Change 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 automation or journey.
You can enable predictive churn for your app and web audiences. Both are tracked and reported independently.
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 or website visits for a cross-section of anonymized apps and sites. By including your app key as an input, the model tailors its predictions to your audience based on your app or website's feature usage — recency, frequency, etc.
- Active User
- An active user is a member of your audience that has opened your app, had an active web session, or clicked a web notification in the last 30 days.
- Inactive User
- An inactive user is a member of your audience that had a predictive tag of high, medium, or low and has not opened your app, had an active web session, or clicked a web notification in the last 30 days.
- A churn outcome occurs when a previously active user becomes inactive, i.e., Airship has not seen any activity (measured in app opens, website visits, or web notification clicks) from a user in the last 30 days. Push opt-in status does not factor into the churn outcome, so it is possible that a user who opted out of notifications could still appear active for churn prediction purposes.
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:
- 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 automation or journey based on a change in risk group.
- Send a message in a journey 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 applied to app or web channels — depending on whether you enable Predictive App Churn, Predictive Web Churn, or both. You can use the
ua_churn_prediction tag group to target
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. If you have both Predictive App and Web Churn enabled, you can set the Device Family filter to Web or Mobile to see churn data for either audience.
Predictive tags update every Sunday, and reports default to the most recent update.
- 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.