Product Analytics Applied Data Science

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)

Joanne Rodrigues-Craig is an experienced data scientist with master’s degrees in mathematics, political science, and demography. She has six years of experience in statistical computing and R programming, as well as experience with Python for data science applications. Her management experience at enterprise companies leverages her ability to understand human behavior by using economic and sociological theory in the context of complex mathematical models.

Part I: The Basics 1 Data in Action 2. Building a Theory of the Universe 3. No Effect? Missteps in Metric Creation and Interpretation 4. Why Are My Users Leaving? 5. The Coveted Goal Post: How to Change Human Behavior
Part II: Advanced Analytics Tools 6. What Groups Should We Target? 7. Generating Insight 8. Which users will leave? 9. The Life of a Web Product Part III: Applying R Statistical Tools

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Product Analytics Applied Data Science Techniques for Actionable Consumer Insights

Summary

Author Biography

Table of Contents

Supplemental Materials