1
Predictive Churn Models That Work
Building a churn model that's 80% accurate is easy. Building one that actually prevents churn is hard. We cover the feature engineering, intervention framework, and feedback loops that make the difference.
Issue #2
AI Growth Stack Weekly #2
This week's focus: retention and personalization. We dive into churn prediction models that actually prevent cancellations, AI personalization at scale, and the latest thinking on A/B testing with ML.
2
AI Personalization at Scale
From segment-level rules to individual-level ML: the personalization maturity model and how to move up it without rebuilding your entire stack.
3
AI-Powered A/B Testing: Beyond the Basics
Multi-armed bandits, contextual bandits, and Bayesian methods are changing how growth teams experiment. Learn when each approach makes sense and how to implement them.
4
Personalization Engines for Growth
A deep dive into recommendation system architectures — collaborative filtering, content-based, and the hybrid approaches that work best in practice.
5
Research: The Impact of Real-Time Personalization on NRR
New research showing that companies with real-time personalization maintain 15-25% higher net revenue retention than those using batch personalization approaches.