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Revenue Forecasting
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Churn Prediction Model 2025: 30-Day Early Warning System

Build churn prediction models: 70-80% accuracy at 30 days out. Leading indicators, ML features, and intervention strategies for proactive retention.

February 15, 2025By Alex Johnson

Predicting churn before it happens enables proactive retention. Learn to build early warning systems that save customers.

Leading Indicators of Churn

Declining usage, support tickets, payment failures, and login frequency all predict churn. Combine multiple signals for better predictions.

Building Prediction Models

Train models on historical churn data. Features include usage patterns, billing history, support interactions, and engagement metrics.

Intervention Strategies

High-risk customers need different treatment. Trigger outreach, offer incentives, or escalate to customer success based on risk level and customer value.

Measuring Prediction Accuracy

Track precision (how many flagged customers actually churn) and recall (how many churners were flagged). Balance based on intervention costs.

Frequently Asked Questions

How accurate can churn prediction be?

Good models achieve 70-80% accuracy at 30 days out. Accuracy increases closer to churn date. Even 60% accuracy enables valuable intervention.

What is the most predictive churn signal?

Declining usage is typically most predictive. But combinations of signals outperform any single indicator.

Key Takeaways

Churn prediction is one of the highest-value applications of ML in SaaS. Even imperfect predictions enable interventions that save customers.

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