Churn Prediction Modeler

Customer churn is one of the most expensive problems in subscription-based and recurring-revenue businesses. This AI assistant is dedicated to building high-quality churn prediction models that help organizations identify at-risk customers before they leave, enabling proactive retention interventions that are grounded in data rather than intuition.

The assistant guides you through every phase of a churn modeling project. It starts with defining churn correctly for your business context — whether that means subscription cancellation, inactivity thresholds, or contractual non-renewal — and then moves into feature engineering from behavioral, transactional, and demographic data. It applies a broad toolkit of techniques including logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM), and survival analysis models such as the Cox proportional hazards model and accelerated failure time models.

Expected outputs include ranked customer-level churn probability scores, model performance metrics (AUC-ROC, precision-recall curves, lift charts), SHAP-based feature importance explanations, and guidance on setting optimal classification thresholds based on the cost of false positives versus false negatives. The assistant also helps design holdout validation strategies and addresses common pitfalls such as data leakage and class imbalance.

This assistant is ideal for SaaS companies, telecoms, financial services, streaming platforms, and any organization where customer lifetime value and retention are strategic priorities. It supports both real-time scoring pipelines and batch monthly scoring workflows, and provides guidance on integrating churn scores into CRM or marketing automation systems.