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Use Product Analytics to Understand Consumer Behavior and Change It at Scale
Product Analytics is a complete, hands-on guide to generating actionable business insights from customer data. Experienced data scientist and enterprise manager Joanne Rodrigues introduces practical statistical techniques for determining why things happen and how to change what people do at scale. She complements these with powerful social science techniques for creating better theories, designing better metrics, and driving more rapid and sustained behavior change.
Writing for entrepreneurs, product managers/marketers, and other business practitioners, Rodrigues teaches through intuitive examples from both web and offline environments. Avoiding math-heavy explanations, she guides you step by step through choosing the right techniques and algorithms for each application, running analyses in R, and getting answers you can trust.
About the Author
Part I: Qualitative Methodology
Chapter 1: Data in Action: A Model of a Dinner Party
Chapter 2: Building a Theory of the Universe–The Social Universe
Chapter 3: The Coveted Goal Post: How to Change User Behavior
Part II: Basic Statistical Methods
Chapter 4: Distributions in User Analytics
Chapter 5: Retained? Metric Creation and Interpretation
Chapter 6: Why Are My Users Leaving? The Ins and Outs of A/B Testing
Part III: Predictive Methods
Chapter 7: Modeling the User Space: k-Means and PCA
Chapter 8: Predicting User Behavior: Regression, Decision Trees, and Support Vector Machines
Chapter 9: Forecasting Population Changes in Product: Demographic Projections
Part IV: Causal Inference Methods
Chapter 10: In Pursuit of the Experiment: Natural Experiments and the Difference-in-Difference Design
Chapter 11: In Pursuit of the Experiment Continued: Regression Discontinuity, Time Series Modelling, and Interrupted Time Series Approaches
Chapter 12: Developing Heuristics in Practice: Statistical Matching and Hill’s Causality Conditions
Chapter 13: Uplift Modeling
Part V: Basic, Predictive, and Causal Inference Methods in R
Chapter 14: Metrics in R
Chapter 15: A/B Testing, Predictive Modeling, and Population Projection in R
Chapter 16: Regression Discontinuity, Matching, and Uplift in R