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References: Advanced Analytics and Experimentation

  1. A/B Testing - Wikipedia Covers the methodology of controlled experiments comparing two variants, including statistical foundations and application contexts, essential knowledge for PMs designing product experiments.

  2. Statistical Significance - Wikipedia Explains p-values, confidence intervals, and hypothesis testing fundamentals that PMs must understand to correctly interpret A/B test results and avoid common statistical pitfalls.

  3. Extract, Transform, Load - Wikipedia Overview of ETL processes for moving data between systems, covering pipeline architectures and transformation patterns that PMs encounter when building data infrastructure for analytics.

  4. "Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing" by Ron Kohavi, Diane Tang, and Ya Xu (Cambridge University Press, 2020) The definitive guide to online experimentation from leaders at Microsoft and Google, covering experiment design, statistical methods, and organizational practices PMs need for rigorous A/B testing.

  5. "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die" by Eric Siegel (Wiley, Revised Edition, 2016) Accessible introduction to predictive modeling and its business applications, helping PMs understand how machine learning and statistical models drive customer segmentation and product personalization.

  6. Optimizely Experimentation Guide Practical guide to running A/B tests covering sample size calculation, experiment duration, and result interpretation from a leading experimentation platform used by product teams worldwide.

  7. Google Analytics: Attribution Modeling Official guide to attribution models including first-touch, last-touch, and data-driven approaches, helping PMs understand how conversion credit is assigned across marketing and product touchpoints.

  8. Evan Miller: A/B Testing Sample Size Calculator Interactive statistical calculator for determining required sample sizes in A/B tests based on effect size and significance levels, a practical tool PMs use during experiment planning.

  9. Apache Kafka Documentation Official documentation for the leading event streaming platform, covering real-time data pipeline architecture that PMs managing event tracking and real-time analytics systems need to understand.

  10. Segment: Customer Data Platform Guide Comprehensive guide to customer data collection, event tracking standards, and data pipeline best practices, helping PMs design unified analytics architectures across multiple product touchpoints.