Product Analytics
8%
off

Product Analytics : Applied Data Science Techniques for Actionable Consumer Insights

By (author) 

Free delivery worldwide

Available. Dispatched from the UK in 3 business days


When will my order arrive?

Available. Expected delivery to the United States in 8-11 business days.


Not ordering to the United States? Click here.

Description

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)
show more

Product details

  • Paperback | 448 pages
  • 180 x 230 x 20mm | 682g
  • Addison Wesley
  • Boston, United States
  • English
  • 0135258529
  • 9780135258521

Back cover copy

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
show more

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
show more

About Joanne Rodrigues

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.
show more