Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights

Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights

by Joanne Rodrigues
Released October 2020
Publisher(s): Addison-Wesley Professional
ISBN: 9780135258644

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Book description

This guide shows how to combine data science with social science to gain unprecedented insight into customer behavior, so you can change it. Joanne Rodrigues-Craig 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 behaviors you want and less you don’t.

Drawing on extensive enterprise experience and deep knowledge of demographics and sociology, Rodrigues-Craig shows how to create better theories and metrics, so you can accelerate the process of gaining insight, altering behavior, and earning business value. You’ll learn how to:

  • Develop complex, testable theories for understanding individual and social behavior in web products

  • Think like a social scientist and contextualize 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 behavior in a complex web product

  • Understand how relevant human behaviors 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)

Table of contents

  1. Cover Page
  2. About This eBook
  3. Half Title Page
  4. Title Page
  5. Copyright Page
  6. Dedications
  7. Contents
  8. Preface
  9. Acknowledgments
  10. About the Author
  11. I: Qualitative Methodology
    1. 1. Data in Action: A Model of a Dinner Party
      1. 1.1 The User Data Disruption
      2. 1.2 A Model of a Dinner Party
      3. 1.3 What’s Unique about User Data?
      4. 1.4 Why Does Causation Matter?
      5. 1.5 Actionable Insights
    2. 2. Building a Theory of the Social Universe
      1. 2.1 Building a Theory
      2. 2.2 Conceptualization and Measurement
      3. 2.3 Theories from a Web Product
      4. 2.4 Actionable Insights
    3. 3. The Coveted Goalpost: How to Change Human Behavior
      1. 3.1 Understanding Actionable Insight
      2. 3.2 It’s All about Changing “Your” Behavior
      3. 3.3 A Theory about Human Behavioral Change
      4. 3.4 Change in a Web Product
      5. 3.5 What Are Realistic Expectations for Behavioral Change?
      6. 3.6 Actionable Insights
  12. II: Basic Statistical Methods
    1. 4. Distributions in User Analytics
      1. 4.1 Why Are Metrics Important?
      2. 4.2 Actionable Insights
    2. 5. Retained? Metric Creation and Interpretation
      1. 5.1 Period, Age, and Cohort
      2. 5.2 Metric Development
      3. 5.3 Actionable Insights
    3. 6. Why Are My Users Leaving? The Ins and Outs of A/B Testing
      1. 6.1 An A/B Test
      2. 6.2 The Curious Case of Free Weekly Events
      3. 6.3 But It’s Correlated …
      4. 6.4 Why Randomness?
      5. 6.5 The Nuts and Bolts of an A/B Test
      6. 6.6 Pitfalls in A/B testing
      7. 6.7 Actionable Insights
  13. III: Predictive Methods
    1. 7. Modeling the User Space: k-Means and PCA
      1. 7.1 What Is a Model?
      2. 7.2 Clustering Techniques
      3. 7.3 Actionable Insights
    2. 8. Predicting User Behavior: Regression, Decision Trees, and Support Vector Machines
      1. 8.1 Predictive Inference
      2. 8.2 Much Ado about Prediction?
      3. 8.3 Predictive Modeling
      4. 8.4 Validation of Supervised Learning Models
      5. 8.5 Actionable Insights
      6. Appendix
    3. 9. Forecasting Population Changes in Product: Demographic Projections
      1. 9.1 Why Should We Spend Time on the Product Life Cycle?
      2. 9.2 Birth, Death, and the Full Life Cycle
      3. 9.3 Different Models of Retention
      4. 9.4 The Art of Population Prediction
      5. 9.5 Actionable Insights
  14. IV: Causal Inference Methods
    1. 10. In Pursuit of the Experiment: Natural Experiments and Difference-in-Difference Modeling
      1. 10.1 Why Causal Inference?
      2. 10.2 Causal Inference versus Prediction
      3. 10.3 When A/B Testing Doesn’t Work
      4. 10.4 Nuts and Bolts of Causal Inference from Real-World Data
      5. 10.5 Actionable Insights
    2. 11. In Pursuit of the Experiment, Continued
      1. 11.1 Regression Discontinuity
      2. 11.2 Estimating the Causal Effect of Gaining a Badge
      3. 11.3 Interrupted Time Series
      4. 11.4 Seasonality Decomposition
      5. 11.5 Actionable Insights
    3. 12. Developing Heuristics in Practice
      1. 12.1 Determining Causation from Real-World Data
      2. 12.2 Statistical Matching
      3. 12.3 Problems with Propensity Score Matching
      4. 12.4 Matching as a Heuristic
      5. 12.5 The Best Guess
      6. 12.6 Final Thoughts
      7. 12.7 Actionable Insights
    4. 13. Uplift Modeling
      1. 13.1 What Is Uplift?
      2. 13.2 Why Uplift?
      3. 13.3 Understanding Uplift
      4. 13.4 Prediction and Uplift
      5. 13.5 Difficulties with Uplift
      6. 13.6 Actionable Insights
  15. V: Basic, Predictive, and Causal Inference Methods in R
    1. 14. Metrics in R
      1. 14.1 Why R?
      2. 14.2 R Fundamentals: A Very Basic Introduction to R and Its Setup
      3. 14.3 Sampling from Distributions in R
      4. 14.4 Summary Statistics
      5. 14.5 Q-Q Plot
      6. 14.6 Calculating Variance and Higher Moments
      7. 14.7 Histograms and Binning
      8. 14.8 Bivariate Distribution and Correlation
      9. 14.9 Parity Progression Ratios
      10. 14.10 Summary
    2. 15. A/B Testing, Predictive Modeling, and Population Projection in R
      1. 15.1 A/B Testing in R
      2. 15.2 Clustering
      3. 15.3 Predictive Modeling
      4. 15.4 Population Projection
      5. 15.5 Actionable Insights
    3. 16. Regression Discontinuity, Matching, and Uplift in R
      1. 16.1 Difference-in-Difference Modeling
      2. 16.2 Regression Discontinuity and Time-Series Modeling
      3. 16.3 Statistical Matching
      4. 16.4 Uplift Modeling
      5. 16.5 Actionable Insights
      6. Appendix
  16. Conclusion
  17. Bibliography
  18. Index
  19. Code Snippets

Product information

  • Title: Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights
  • Author(s): Joanne Rodrigues
  • Release date: October 2020
  • Publisher(s): Addison-Wesley Professional
  • ISBN: 9780135258644