AI-powered churn prediction for loyalty programs.
Use purchase, redemption and engagement signals to understand which transacting members may be at risk of lapsing. TrueLoyal brings churn risk labels into Rewards so your team can build more focused retention audiences.
See signs earlier. Plan your next retention action.
What is loyalty churn prediction?
Loyalty churn prediction estimates the risk that a member will stop participating or purchasing, using patterns in past behavior. TrueLoyal applies this approach to loyalty-program data and makes risk labels available in Rewards. Teams can use that signal to prioritize retention activity; a prediction is not a guarantee that a member will leave or respond to an offer.
How TrueLoyal churn prediction works
Learn from loyalty behavior
The model uses your program's purchase, redemption and engagement history to learn patterns associated with member activity.
Add industry context
Industry patterns provide context for interpreting your members' behavior. Review the supported industry model and its suitability for your program with the TrueLoyal team.
Turn risk into usable labels
Transacting members receive a churn propensity score, grouped into four labels. Use the labels to distinguish audiences for further review and retention planning.
Use labels inside Rewards
Find churn labels in member profiles and use them in campaign filters and exports within TrueLoyal Rewards.
Connect to your retention tools
Use API connections to share churn information with your CRM, email service provider or other supported systems. Confirm the integration scope for your stack.
Work from a weekly refresh
Churn scores refresh weekly, giving your team an updated signal for its next retention planning cycle.
Turn a risk signal into a retention test.
Start with the members your team needs to review. Combine the risk label with relevant program information, choose an appropriate retention approach and compare outcomes over an agreed period. The goal is to learn which actions help, not simply to contact every member with a high-risk score.
Is churn prediction right for your loyalty program?
Explore the feature if your team wants a clearer way to review member lapse risk and connect that insight to retention activity. Bring your current program, available behavioral history and activation tools to the conversation so the data and implementation requirements can be assessed.
Buyer questions answered
What does AI churn prediction tell a loyalty team?
It estimates which members may be at risk of lapsing, based on behavioral patterns. The score helps a team prioritize review and retention activity. It does not determine with certainty who will leave, or prove that a particular offer will change a member's behavior.
What data does TrueLoyal use for churn prediction?
TrueLoyal's model uses loyalty-program behavioral data, including purchases, redemption activity and program engagement. Before implementation, confirm the required history, eligible member population, data quality and identity mapping for your program.
Where can our team use the churn labels?
Churn labels are available inside TrueLoyal Rewards in member profiles, campaign filters and exports. API connections can also support sharing the information with other systems. Confirm the required fields, destination and implementation scope for your workflow.
How often are churn scores updated?
Scores are refreshed weekly. Confirm the refresh schedule, timezone and data cutoff for your program so campaign planning uses the appropriate scoring period.
How is a churn score different from an inactivity segment?
An inactivity segment selects members using a rule such as time since their last purchase. A churn prediction model estimates future lapse risk from behavioral patterns. They can support different decisions; the appropriate comparison depends on the model, data and retention outcome you want to measure.
Does a high churn score mean we should send a discount?
No. A risk signal is a reason to review the member, not an automatic instruction to discount. Consider the member's context, contact permissions, program economics and available retention actions. Test whether an intervention improves outcomes before expanding it.
Will churn prediction automatically reduce churn?
Prediction alone does not retain a member. Results depend on the reliability of the signal, the audience selected and the action taken. Evaluate prediction quality and campaign outcomes separately, using a defined measurement window and a suitable comparison group.
Can we use churn prediction with our existing CRM or email platform?
TrueLoyal describes API connections for sharing churn information with CRM, email and other systems. Review your specific stack with the team. This does not mean that every system has a ready-made native connector or that a standalone deployment is included.
See how churn prediction could
fit your program.
Explore the scoring workflow, discuss your data and review where risk labels can support your retention activity. Start with a demo request tailored to your loyalty program.
Request a Churn Prediction DemoExplore the TrueLoyal Platform
