Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights eBook

Joanne Rodrigues

Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights eBook

By Joanne Rodrigues
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Joanne Rodrigues
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This guide shows how to combine data science with social science to gain unprecedented insight into customer behaviour, so you can change it. Joanne Rodrigues bridges the gap between predictive data science and statistical techniques that reveal why important things happen -- why customers buy more, or why they immediately leave your site -- so you can get more behaviours you want and less you don’t. Drawing on extensive enterprise experience and deep knowledge of demographics and sociology, Rodrigues shows how to create better theories and metrics, so you can accelerate the process of gaining insight, altering behaviour, and earning business value. You’ll learn how to:
  • Develop complex, testable theories for understanding individual and social behaviour in web products
  • Think like a social scientist and contextualise individual behavior in today’s social environments
  • Build more effective metrics and KPIs for any web product or system
  • Conduct more informative and actionable A/B tests
  • Explore causal effects, reflecting a deeper understanding of the differences between correlation and causation
  • Alter user behaviour in a complex web product
  • Understand how relevant human behaviours develop, and the prerequisites for changing them
  • Choose the right statistical techniques for common tasks such as multistate and uplift modeling
  • Use advanced statistical techniques to model multidimensional systems
  • Do all of this in R (with sample code available in a separate code manual)

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Table of contents
  • 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