Description
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
- Whatever your product or service, this guide can help you create precision-targeted marketing campaigns, improve consumer satisfaction and engagement, and grow revenue and profits.
Features
- Use your data to model and shape human behavior, not just make predictions
- Combine advanced qualitative, statistical, and machine learning tools to grow revenue and retention, lower costs, and achieve other business goals
- Learn through practical examples, intuitive visual explanations, and proven descriptions tested with hundreds of business professionals
- Use R to apply this guide’s powerful techniques to your unique business challenges
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
Back Cover
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)
- Build better theories and metrics, and drive more of the behaviors you want
- Model, understand, and alter customer behavior to increase revenue and retention
- Construct better frameworks for examining why your customers do what they do
- Develop core metrics for user analytics, and conduct more effective A/B tests
- Master key techniques that most books ignore, including statistical matching and uplift modeling
- Use R and this book’s many R examples to implement these techniques yourself
Use data science and social science to generate real changes in customer behavior
- Build better theories and metrics, and drive more of the behaviors you want
- Model, understand, and alter customer behavior to increase revenue and retention
- Construct better frameworks for examining why your customers do what they do
- Develop core metrics for user analytics, and conduct more effective A/B tests
- Master key techniques that most books ignore, including statistical matching and uplift modeling
- Use R and this book’s many R examples to implement these techniques yourself
Author
Joanne Rodrigues 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.
approx. 7-9 days
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