Model Construction & Testing
We use your historical data to build churn, propensity-to-buy, and recommendation models with proven open-source tools like scikit-learn, XGBoost, LightGBM, and survival and uplift libraries in Python or R. Churn gets scored with gradient-boosted classifiers and a survival model for time-to-churn. Cross-sell relies on propensity and product-affinity models built from real transaction patterns and lookalike customer behavior. The uplift model singles out the persuadables, so your offers only go to customers whose behavior you can actually change. We backtest every model against your actual outcomes, measuring precision, recall, calibration, and lift in the top deciles. We keep iterating until your reps have scores they can trust, and act on.