# How Data Scientists Build Churn Prediction Models That Actually Work Page: https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-build-churn-prediction-models-that-actually-work Text version: https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-build-churn-prediction-models-that-actually-work.md Podcast: [The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations](https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831) Published: 2026-06-14T20:20:35+00:00 Episode link: https://audio.fexingo.com/business/the-data-science-podcast/episode-0051.mp3 Audio file: https://audio.fexingo.com/business/the-data-science-podcast/episode-0051.mp3 Processing state: not_requested JSON: https://stenobird.com/v1/public/podcasts/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/episodes/how-data-scientists-build-churn-prediction-models-that-actually-work Duration seconds: 356 ## Resource Churn prediction is one of the most common — and most frustrating — problems data scientists face. In this episode, Lucas and Luna dig into why many churn models fail in production and what separates the ones that actually reduce customer loss. They walk through a concrete example from a mid-size telecom company that cut churn by 14 percent in six months by focusing on the right features and the right deployment strategy. Along the way, they discuss the trap of over-relying on recency features, why boosting models often beat neural nets on tabular churn data, and how to build a simple intervention framework that turns predictions into action. If you've ever built a churn model that looked great in the notebook but went nowhere in production, this one's for you. #ChurnPrediction #DataScience #MachineLearning #CustomerAnalytics #FeatureEngineering #XGBoost #Telecom #ModelDeployment #PrecisionRecall #CustomerRetention #LifetimeValue #TinyML #ProductionML #BusinessAnalytics #Technology #FexingoBusiness #BusinessPodcast #DataDriven Keep every episode free: buymeacoffee.com/fexingo ## Actions - request_transcript: `POST https://stenobird.com/v1/public/podcasts/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/episodes/how-data-scientists-build-churn-prediction-models-that-actually-work/transcription-requests` — Idempotently request low-priority transcript generation for this episode. - read_markdown: `GET https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-build-churn-prediction-models-that-actually-work.md` — Read the agent-friendly Markdown representation of this episode resource. A page view does not enqueue transcription. Agents should invoke `request_transcript` explicitly when they need this episode processed. ## Transcript Full transcripts are not published on public pages unless there is a clear rights basis.