The CMO Podcast with Fexingo · 2026-07-08 · 10 min
In this milestone 100th episode, Lucas and Luna dig into a specific marketing analytics tactic: using machine learning models to predict customer churn before the customer ever signals intent to leave. They walk through a real case from a mid-market SaaS company that reduced churn by 22 percent in two quarters by acting on AI-generated risk scores. Lucas explains the difference between a basic logistic regression and a gradient-boosted model in plain language, why most churn models fail because of stale data, and the one metric CMOs should track instead of retention rate. Luna pushes back on the cost of implementation and asks whether smaller teams can realistically build these models in-house. The hosts also touch on the ethics of predictive targeting and what happens when a model flags a customer who wasn't actually at risk. Concrete, actionable, and grounded in a real number.
Other episodes covering the same guests and topics, from across The B2B Podcast Index.
Privacy, Power, and Prediction: Reclaiming Control of Your Personal Data, with Carissa Véliz | Lawyerist PodcastLegal Talk Network · on Predictive analytics85 / 100
Drones Are Changing the Future of FarmingFood Tech Talk · on Predictive analytics81 / 100
Finding the Students Schools MissTech & Learning Conversations Podcast · on Predictive analytics81 / 100
Why DTC Brands Lose Customers They Think Are Already Theirs | Agnes Seville, Little SleepiesThe MarTech Matrix · on customer retention79 / 100
Why Customer Retention Is the Most Valuable PE Growth StrategyDemand Revenue · on customer retention67 / 100
Trying out new versions of our best selling product - founder product development processMatt Kelly · on customer retention60 / 100
How CMOs Turn Customer Feedback Into Product Roadmaps76 / 100How CMOs Use Zero-Party Data to Fix PersonalizationHow CMOs Measure Marketing ROI Without GuessworkWhy Your Best Customers Leave Without Saying GoodbyeWhy CMOs Are Killing The Brand Fund