How Data Scientists Build Churn Models That Actually Predict

Episode 125 of The Data Science Podcast digs into the practical craft of customer churn prediction. Lucas and Luna walk through a real telecom case where a data team reduced false positives by 40 percent by switching from logistic regression to a gradient-boosted tree with engineered tenure and usage features. They discuss why accuracy is a misleading metric for churn, how to frame the business cost of false positives versus false negatives, and the one feature engineering trick that improved recall from 62 to 81 percent. The conversation also touches on deployment gotchas like data drift and the importance of setting a clear intervention budget before building the model. If you have used a churn model or are planning to build one, this episode gives you a concrete framework and a specific example you can reference. #DataScience #ChurnPrediction #CustomerChurn #MachineLearning #GradientBoosting #FeatureEngineering #Classification #ImbalancedData #Recall #FalsePositives #Telecom #ModelDeployment #BusinessMetrics #DataDriven #PredictiveModeling #Technology #FexingoBusiness #BusinessPodcast Keep every episode free: buymeacoffee.com/fexingo