Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights (Pearson Business Analytics Series)

646 Pages·2020·16.604 MB·English

by  Joanne Rodrigues

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Product Analytics
Product Analytics
Applied Data Science Techniques for
Actionable Consumer Insights
Joanne Rodrigues
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Library of Congress Control Number: 2020940464
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Cover image: Mad Dog/Shutterstock
Page 6: “By 2018, the U.S. . . . deep analytic talent.” McKinsey & Company.
Page 21: Introduction to Bayes’ Rule, Thomas Bayes.
Page 51: “if people are . . . adversely and resist.” Thaler, R. H., & Sunstein, C. R.
(2008). Nudge: Improving decisions about health, wealth, and happiness. New
Haven, Conn.: Yale University Press.
Pages 256-257: “1. Strength of the . . . you could easily test?” Hill, Austin Bradford
(1965). “The Environment and Disease: Association or Causation?”. Proceedings of
the Royal Society of Medicine. 58 (5): 295–300.
Page 271: “Fewer variables are better: There is . . . number of chimneys swept in a day
to determine dosage effects.” Radcliffe, Nicholas, and Surry, Patrick. “Real-world
uplift modelling with significance based uplift trees.” White Paper TR-2011-1,
Stochastic Solutions, 2011.
Page 281, Figure 14.1: Screenshot of R Studio window with four panels © 2020 Apple
Inc.
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ISBN-13: 978-0-13-525852-1
ISBN-10: 0-13-525852-9
02 22
To my children Sahana and Ronak, whose infectious laughter
kept me focused.
Contents
Preface
Acknowledgments
About the Author
I Qualitative Methodology
1 Data in Action: A Model of a Dinner Party
1.1 The User Data Disruption
1.1.1 Don’t Leave the Users out of the Model
1.1.2 The Junior Analyst
1.1.3 The Opposite of the Misguided Analyst: The Data Guru
1.2 A Model of a Dinner Party
1.2.1 Why Are Social Processes Difficult to Analyze?
1.2.2 A Party Is a Process
1.2.3 A Party Is an Open System
1.2.4 A “Great” Party Is Hard to Define
1.2.5 Party Guests’ Motives and Opinions Are Often Unknown
1.2.6 A Party Presents a Variable Search Problem
1.2.7 The Real Secret to a Great Party Is Elusive
1.3 What’s Unique about User Data?
1.3.1 Human Behavior Is a Process, Not a Problem
1.3.2 No Clear and Defined Outcomes
1.3.3 Social Systems Have Rampant Problems of Incomplete Information
1.3.4 Social Systems Consist of Millions of Potential Behaviors
1.3.5 Social Systems Are Often Open Systems
1.3.6 Inferring Causation Is Almost Impossible
1.4 Why Does Causation Matter?
1.5 Actionable Insights
2 Building a Theory of the Social Universe
2.1 Building a Theory
2.1.1 Won’t Fancy Algorithms Solve All Our Problems?
2.1.2 The Pervasive (and Generally Useless) One-Off Fact
2.1.3 The Art of the Typology
2.1.4 The Project Design Process: Theory Building
2.1.5 Steps to a Good Theory
2.1.6 Description: Questions and Goals
2.1.7 Analytical: Theory and Concepts
2.1.8 Qualities of a “Good” Theory
2.2 Conceptualization and Measurement
2.2.1 Conceptualization
2.2.2 Measurement
2.2.3 Hypothesis Generation
2.3 Theories from a Web Product
2.3.1 User Type Purchasing Model
2.3.2 Feed Algorithm Model
2.3.3 Middle School Dance Model
2.4 Actionable Insights
3 The Coveted Goalpost: How to Change Human Behavior
3.1 Understanding Actionable Insight
3.2 It’s All about Changing “Your” Behavior
3.2.1 Is It True Behavior Change?
3.2.2 Quitting Smoking: The Herculean Task of Behavioral Change
3.2.3 Measuring Behavior Change
3.3 A Theory about Human Behavioral Change
3.3.1 Learning
3.3.2 Cognition
3.3.3 Randomized Variable Investment Schedule
3.3.4 Outsized Positive Rewards and Mitigated Losses
3.3.5 Fogg Model of Change
3.3.6 ABA Model of Change
3.4 Change in a Web Product
3.5 What Are Realistic Expectations for Behavioral Change?
3.5.1 What Percentage of Users Will See a Real Change in Our Product?
3.5.2 Are Certain Behaviors Easier to Change?
3.5.3 Behavioral Change Worksheet
3.6 Actionable Insights
II Basic Statistical Methods
4 Distributions in User Analytics
4.1 Why Are Metrics Important?
4.1.1 Statistical Tools for Metric Development
4.1.2 Distributions