Published by Addison-Wesley Professional (July 7, 2021) © 2021
Joanne Rodrigues
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.
Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
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.
- Develop core metrics and effective KPIs for user analytics in any web product
- Truly understand statistical inference, and the differences between correlation and causation
- Conduct more effective A/B tests
- Build intuitive predictive models to capture user behavior in products
- Use modern, quasi-experimental designs and statistical matching to tease out causal effects from observational data
- Improve response through uplift modeling and other sophisticated targeting methods
- Project business costs/subgroup population changes via advanced demographic projection
Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
- 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
- Conclusion