Predict churn before it hits your revenue.

An AI churn prediction tool analyzes behavioral signals, such as reduced login frequency, dropped feature usage, or shortened session lengths, and assigns each customer a probability of churning. Intempt does this continuously for every profile, updating churn scores as new events arrive.

Out of the box, Intempt calculates churn risk (0-100 probability score), LTV (lifetime value based on transaction history), Recency, Frequency, Monetary value (RFM), Activity score, Fit score, and Lifecycle stage. All scores update continuously from live events, not on a nightly batch.

RFM segments group customers by how recently they purchased (Recency), how often they purchase (Frequency), and how much they spend (Monetary value). Intempt calculates RFM automatically and places every customer into one of six buckets: Champions, Loyal, Potential Loyalists, At-Risk, Hibernating, and Lost.

Churn risk is calculated by Intempt's machine learning model using techniques like decision trees and neural networks, trained on behavioral signals from your event data: session frequency, feature engagement, time since last activity, purchase patterns, and more. The model trains on your specific data, so it reflects the actual patterns that predict churn in your product.

Yes. You can define a churn risk threshold (for example, 60% probability) and Intempt will automatically add users who cross it to your At-Risk segment. You can trigger a journey, a notification to your customer success team, or a direct CRM task the moment the threshold is crossed.