Customer churn prediction helps businesses retain users by identifying those at risk of leaving and taking proactive steps to re-engage them. This is particularly useful for subscription-based services, e-commerce platforms, and streaming services.By using Kumo AI, businesses can:
Train a churn model tailored to their data and customer behavior.
Use the Kumo REST API to export predictions to a CRM system.
Send targeted notifications via email, SMS, or push notifications to customers likely to churn.
Kumo’s Predictive Query Language (PQL) allows for flexible churn definitions, including:
Subscription churn: Predict users likely to cancel within the next 3 months.
Inactivity churn: Predict users unlikely to log in within the next 7 days.
Purchase churn: Predict users unlikely to make a purchase in the next 30 days.
This predicts users who will not make a purchase in the next X days, given that they have been active in the last Y days.2. Predicting Streaming/Inactivity Churn
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PREDICT COUNT(events.* WHERE events.type = 'stream', 0, X, days) = 0FOR EACH users.user_idWHERE COUNT(events.* WHERE events.type = 'session', -Y, 0, days) > 0
This predicts users who will not stream content in the next X days, given that they had active sessions in the last Y days.3. Predicting Subscription Churn
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PREDICT COUNT(events.* WHERE events.type = 'unsubscribe', 0, X, days) > 0FOR EACH users.user_idWHERE LAST(users.subscription_status, 0, -Y, days) == 'active'
This predicts users who will unsubscribe in the next X days, given that they were subscribed in the last Y days.