CARRE: Counterfactual Action Retrieval and Reason Evaluation for Explainable Churn Prescription
Churn models typically identify high-risk customers but do not specify which feasible retention action should be considered or why that action is appropriate. We present CARRE (Counterfactual Action Retrieval and Reason Evaluation), a three-stage framework that combines retrieval-augmented candidate generation, cost-aware counterfactual scoring, and large language model (LLM) reasoning. CARRE retrieves a predefined catalog of retention actions, estimates model-predicted churn-risk changes under explicit feature transformations, and generates a structured churn reason and a profile-grounded explanation for the selected action. On the IBM Telco Customer Churn dataset, CARRE achieves 79.8% g...
| Authors | Minjoo Kim, Sangjin Park, Seung Hwan Cho |
|---|---|
| Primary category | cs.CL |
| Published | 9 September 2026 |
| Last updated at source | 10 September 2026 |
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