Articles exploring machine learning through the lens of behavioral science and experimentation. Practical frameworks for growth leaders who measure in revenue, not vanity metrics.
7 articles
Machine learning and A/B testing are complementary, not competing. Learn how ML improves experiment design, analysis, and the speed of optimization cycles.
Learn when and how to fine-tune large language models for your business. A practical guide covering data prep, training, evaluation, and deployment.
By the time a user cancels, the decision was made weeks ago. This article explores how to build churn prediction models that read behavioral signals early enough to intervene, the difference between voluntary and involuntary churn indicators, and why intervention timing matters more than intervention content.
Why framing AI personalization and A/B testing as competing approaches is a strategic mistake. Learn how the sequential relationship of test, segment, and personalize creates optimization systems that neither discipline achieves alone.
Explore when and why AI-generated copy variants outperform human-written alternatives in A/B tests, the creative constraint paradox that makes machines surprisingly effective, and how to integrate AI variant generation into team workflows without losing creative quality.
Explore how AI and large language models are transforming A/B test hypothesis generation by eliminating confirmation bias, surfacing non-obvious patterns in historical data, and accelerating the path from insight to experiment.
AI transforms hypothesis generation, test velocity, and real-time personalization in experimentation programs while the fundamental requirements of statistical rigor, sample size, and causal inference remain unchanged.