Customer churn prediction estimates which customers are likely to stop buying a product or service. It extends rules-based targeting by combining more information, but its value depends on the organization’s ability to prevent or mitigate churn. section 3.3 section 3.3.1
Use churn prediction as part of a measured predictive-marketing progression:
- Define churn before building the model; organizations may struggle to agree on the precise event or time window that counts as churn. section 3.3.1
- Build on campaign experiments so the team can test whether predictions lead to effective interventions rather than treating prediction itself as the outcome. section 3.3
- Connect predicted risk to journeys or offers that prevent or mitigate disengagement, while preserving privacy through consistent, compliant first-party data. section 3.3 section 1.6.2
Re-engaging lapsed customers
A re-engagement strategy used customers’ past purchases to offer a limited-time discount and performed better than earlier attempts. Churn prediction can provide a more systematic way to identify customers who may need such intervention. section 1.6.2 section 3.3
