Everyone Builds a Churn Model. Almost Nobody Reduces Churn.

1. 1Build a churn model using historical customer data
2. 2Analyze the results to identify key factors contributing to churn
3. 3Develop targeted interventions to address these factors
4. 4Test and refine these interventions using A/B testing or other experimental methods
5. 5Implement and monitor the effectiveness of the interventions in reducing churn

Data scientists and product managers can benefit from understanding the limitations of churn models and how to effectively use them to reduce customer churn

💡 Predicting customer churn is not enough; targeted interventions are necessary to actually reduce churn

💡 Predicting churn is easy, but reducing it is hard. Focus on targeted interventions, not just models #datascience #churnreduction