The Growth Operator with Fexingo · 2026-07-11 · 9 min
In this episode of The Growth Operator, Lucas and Luna explore how B2B brands are using AI to predict customer churn before it happens. They dive into the specific case of a mid-market SaaS company that reduced churn by 25% in six months using a machine learning model trained on usage data, support tickets, and billing history. Lucas explains how the model flags at-risk accounts weeks in advance, allowing sales and customer success teams to intervene proactively. Luna pushes back on common pitfalls like false positives and data silos. The hosts also discuss the importance of building a feedback loop to continuously improve prediction accuracy. By the end, listeners will understand the key metrics to track (like declining login frequency and increased support volume) and how to implement a practical early warning system without a massive data science team. A must-listen for anyone in B2B revenue operations looking to move from reactive retention to proactive churn prevention.
Other episodes covering the same guests and topics, from across The B2B Podcast Index.
How AI Is Personalizing B2B Sales Sequences That Actually Convert72 / 100How AI Is Automating B2B Contract Review and NegotiationHow AI Is Revolutionizing B2B Customer OnboardingHow B2B Brands Use AI to Predict Customer Lifetime ValueHow B2B Brands Use AI to Predict Deal Velocity
