Prediction is the easy half: field research shows that contacting customers in order of churn risk can waste budget, and in one large experiment it raised…
Risk tells you who will leave. Measuring a one-point churn improvement needs roughly 8,100 customers per arm. When It Comes to Retaining Customers, It May Be Best to Leave Them Alone +2 A churn model cannot see this.
Key takeaways
- What “30 days early” actually means Thirty days is not a property of your model.
- If the model says 20%, roughly 20% of those customers should churn.
- It may include customers with lower baseline risk.
A churn score tells you who is leaving.
Read the full article: https://www.aipersonalization.cloud/ai-retention-loop/
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