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Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights

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Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights

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  • Estimated Release: Aug 28, 2020

eBook (Watermarked)

  • Your Price: $31.99
  • List Price: $39.99
  • Estimated Release: Aug 14, 2020
  • Includes EPUB, MOBI, and PDF
  • About eBook Formats
  • This eBook includes the following formats, accessible from your Account page after purchase:

    ePub EPUB The open industry format known for its reflowable content and usability on supported mobile devices.

    MOBI MOBI The eBook format compatible with the Amazon Kindle and Amazon Kindle applications.

    Adobe Reader PDF The popular standard, used most often with the free Adobe® Reader® software.

    This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.

About

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 

Description

  • Copyright 2021
  • Dimensions: 7" x 9-1/8"
  • Pages: 448
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-525852-9
  • ISBN-13: 978-0-13-525852-1

Use Product Analytics to Understand and Change Consumer Behavior at Scale

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.

This book fills the gaps that many other data science book leave behind: how to start a new data science project; how to conceptualize complex ideas; building metrics from the statistic and demographic fundamentals; projecting consumer populations and material needs for a business; and causal inference beyond simple A/B testing techniques, such as difference-in-difference, regression discontinuity, propensity score matching, and uplift modelling.

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 the R programming language, 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
  • 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.

Sample Content

Table of Contents

Preface
Acknowledgments
About the Author


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

Bibliography
Index

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