Policyholder Churn Prediction & Retention
Predict which policyholders are likely to lapse and arm agents with next-best-action during renewal conversations.
Solution Overview
The Challenge
Insurers typically learn a policyholder has left when the renewal date passes, at which point the only available response is a win-back campaign with poor economics.
- 1Churn identified only after the policy has lapsed
- 2Generic retention offers with poor conversion and margin erosion
- 3Agents lacking timely, personalized guidance during the renewal conversation
Our Approach
CloudGate builds churn propensity models on Azure Machine Learning across policy, billing, claims and interaction data, scoring every policyholder well before renewal and explaining the drivers behind each score.
- Churn propensity scoring per policyholder, refreshed ahead of renewal
- Explainable risk drivers so interventions address the actual cause
- Next-best-action and offer recommendations delivered in the renewal conversation
Typical Outcomes
Typical outcomes for this solution pattern, not the results of a named client engagement.
Technologies Used
More Success Stories
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