Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights 1st Edition by Joanne Rodrigues ISBN 0135258529 9780135258521

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Product details:

ISBN 10:    0135258529
ISBN 13: 978-0135258521
Author:       Joanne Rodrigues

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)

Book Website: actiondatascience.com.

Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights 1st Table of contents:

I: Qualitative Methodology

1. Data in Action: A Model of a Dinner Party

   • 1.1 The User Data Disruption
   • 1.2 A Model of a Dinner Party
   • 1.3 What’s Unique about User Data?
   • 1.4 Why Does Causation Matter?
   • 1.5 Actionable Insights

2. Building a Theory of the Social Universe

   • 2.1 Building a Theory
   • 2.2 Conceptualization and Measurement
   • 2.3 Theories from a Web Product
   • 2.4 Actionable Insights

3. The Coveted Goalpost: How to Change Human Behavior

   • 3.1 Understanding Actionable Insight
   • 3.2 It’s All about Changing “Your” Behavior
   • 3.3 A Theory about Human Behavioral Change
   • 3.4 Change in a Web Product
   • 3.5 What Are Realistic Expectations for Behavioral Change?
   • 3.6 Actionable Insights

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II: Basic Statistical Methods

4. Distributions in User Analytics

   • 4.1 Why Are Metrics Important?
   • 4.2 Actionable Insights

5. Retained? Metric Creation and Interpretation

   • 5.1 Period, Age, and Cohort
   • 5.2 Metric Development
   • 5.3 Actionable Insights

6. Why Are My Users Leaving? The Ins and Outs of A/B Testing

   • 6.1 An A/B Test
   • 6.2 The Curious Case of Free Weekly Events
   • 6.3 But It’s Correlated …
   • 6.4 Why Randomness?
   • 6.5 The Nuts and Bolts of an A/B Test
   • 6.6 Pitfalls in A/B Testing
   • 6.7 Actionable Insights

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III: Predictive Methods

7. Modeling the User Space: k-Means and PCA

   • 7.1 What Is a Model?
   • 7.2 Clustering Techniques
   • 7.3 Actionable Insights

8. Predicting User Behavior: Regression, Decision Trees, and Support Vector Machines

   • 8.1 Predictive Inference
   • 8.2 Much Ado about Prediction?
   • 8.3 Predictive Modeling
   • 8.4 Validation of Supervised Learning Models
   • 8.5 Actionable Insights

Appendix

9. Forecasting Population Changes in Product: Demographic Projections
   • 9.1 Why Should We Spend Time on the Product Life Cycle?
   • 9.2 Birth, Death, and the Full Life Cycle
   • 9.3 Different Models of Retention
   • 9.4 The Art of Population Prediction
   • 9.5 Actionable Insights

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IV: Causal Inference Methods

10. In Pursuit of the Experiment: Natural Experiments and Difference-in-Difference Modeling

    • 10.1 Why Causal Inference?
    • 10.2 Causal Inference versus Prediction
    • 10.3 When A/B Testing Doesn’t Work
    • 10.4 Nuts and Bolts of Causal Inference from Real-World Data
    • 10.5 Actionable Insights

11. In Pursuit of the Experiment, Continued

    • 11.1 Regression Discontinuity
    • 11.2 Estimating the Causal Effect of Gaining a Badge
    • 11.3 Interrupted Time Series
    • 11.4 Seasonality Decomposition
    • 11.5 Actionable Insights

12. Developing Heuristics in Practice

    • 12.1 Determining Causation from Real-World Data
    • 12.2 Statistical Matching
    • 12.3 Problems with Propensity Score Matching
    • 12.4 Matching as a Heuristic
    • 12.5 The Best Guess
    • 12.6 Final Thoughts
    • 12.7 Actionable Insights

13. Uplift Modeling

    • 13.1 What Is Uplift?
    • 13.2 Why Uplift?
    • 13.3 Understanding Uplift
    • 13.4 Prediction and Uplift
    • 13.5 Difficulties with Uplift
    • 13.6 Actionable Insights

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V: Basic, Predictive, and Causal Inference Methods in R

14. Metrics in R

    • 14.1 Why R?
    • 14.2 R Fundamentals: A Very Basic Introduction to R and Its Setup
    • 14.3 Sampling from Distributions in R
    • 14.4 Summary Statistics
    • 14.5 Q-Q Plot
    • 14.6 Calculating Variance and Higher Moments
    • 14.7 Histograms and Binning
    • 14.8 Bivariate Distribution and Correlation
    • 14.9 Parity Progression Ratios
    • 14.10 Summary

15. A/B Testing, Predictive Modeling, and Population Projection in R

    • 15.1 A/B Testing in R
    • 15.2 Clustering
    • 15.3 Predictive Modeling
    • 15.4 Population Projection
    • 15.5 Actionable Insights

16. Regression Discontinuity, Matching, and Uplift in R

    • 16.1 Difference-in-Difference Modeling
    • 16.2 Regression Discontinuity and Time-Series Modeling
    • 16.3 Statistical Matching
    • 16.4 Uplift Modeling
    • 16.5 Actionable Insights

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