Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights, 1st edition
Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights
ISBN-13: 9780135258521
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$49.99
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Overview
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
- 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
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