Who This Book Is For

This how-to guide shows how to combine data science with social science to gain unprecedented insight into customer behavior, so you can change it.

Product Owners

Build KPIs to complement your data strategy and execute plans of action to leverage your data to achieve your business goals

Data Scientists

Explore causal effects, reflecting a deeper understanding of the differences between correlation and causation

Marketers

Conduct more informative and actionable A/B tests and alter user behavior in a complex web product

About The Book

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.

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Thing's you will learn

A how-to guide to maximize the usefulness of your data and generate actionable business insight.

Identify and create good KPIs to drive growth

Tracking the right KPIs is essential for data-driven decisions.

Avoid common pitfalls in understanding your data

There a data errors that are made again and again in industry.

Move from raw data to inference and strategy

We must use conceptual tools to move from raw-data to actionable insight.

Conduct more informative and actionable A/B tests

Avoiding common pitfalls in A/B testing is necessary to generate valuable insight.

Explore causal effects

To truly have insight we must answer the "why", we need tools and heuristics for causal inference.

Generate actionable business insights

Rather than a focus on theory, this book will give you tools to apply these algorithms to real-world business examples.

Data & Resources

Resources to practice the techniques learned in the book & course materials for educators

Practice Worksheet Book Data Full Course Manual

      

Data Resources for Product Analytics Practice Data

UCI Machine Learning : https://archive.ics.uci.edu/ml/datasets.php

Harvard University Dataverse: https://dataverse.harvard.edu

University of Michigan ICPSR: https://www.icpsr.umich.edu/web/ICPSR/search/studies

American Community Value Survey: https://www.ipums.org

About the author

Joanne Rodrigues

My passion is to analyze large amounts of real-world behavioral data structured, semi-structured or unstructured to generate actionable business insights.

I am an experienced data scientist and enterprise manager with a masters' degree in mathematics (London School of Economics), political science (University of California, Berkeley), and demography (University of California, Berkeley), and a bachelor's degree in international economics (Georgetown University).

She pioneered new techniques at Sony Playstation, led all of MeYou Health's data science efforts, and founded a company ClinicPriceCheck.com, featured on TechCrunch Battlefield SF 2020.

  • Multidisciplinary academic background
  • Readable, accessible, and practical
  • Each chapter ends with actionable insights

Reach Me on Linkedin

Contact with Author

Need consulting services, interested in corporate trainings in product analytics, or have simply have questions? Please fill out the form below and reach out!

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Chapters of the book

A how-to guide for how to apply your product data to making business decision. This book is essential for those who use data to make impactful business decisions.

Chapter 1 Data in Action

Common pitfalls to data analysis and how we should think about social behavior.

Chapter 2Building a Theory of the Social Universe

How we should approach metric development and understanding customer behavior.

Chapter 3 The Coveted Goalpost: How to Change Human Behavior

Understanding theories of human behavior change

Chapter 4Distributions in Product Analytics

A brief overview of statistical concepts that for product analytics

Chapter 5Retained? Metric Creation and Interpretation

A deep-dive into the process of metric creation and common metrics in product analytics

Chapter 6Why Are My Users Leaving? The Ins and Outs of A/B Testing

An overview of important concepts of correlation and causation and a guide to setting up A/B tests

Chapter 7 Modeling the User Space: k-Means and PCA

A simple, introductory quide to unsupervised learning in product analytics

Chapter 8 Predicting User Behavior: Regression, Decision Trees, and Support Vector Machines

An introductory guide to supervised learning techniques in product analytics

Chapter 9 Forcasting Population Changes in Product: Demographic Projections

An introductory guide to demographic poplation projection techniques in product analytics

Chapter 10 In Pursuit of the Experiment: Natural Experiments and Difference-in-Difference Modeling

An introduction to natural experiments and difference-in-difference modeling in a product analytics context

Chapter 11 In Pursuit of the Experiment: Regression Discontinuity

An introduction to regression discontinuity and interrupted time series designs

Chapter 12Developing Heuristics in Practice

An introduction to statistical matching and causal inference heuristics

Chapter 13 Uplift Modeling

An introduction to uplift modeling for A/B testing results

Chapter 14 Metrics in R

A guide to building your defined metrics

Chapter 15 A/B Testing, Predictive Modeling, and Population Projection in R

A guide to running A/B testing, predictive modeling and population projection in R

Chapter 16 Regression Discontinuity, Matching, and Uplift in R

An example of an RD design, matching and uplight example in R

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Get a free course manual

* simple course manual for the first 6 chapters

The Video Course

Product Analytics for Data-Driven Decisions: Derive Insights from Web Analytics Data explores core concepts that will help viewers work with their data, identify bias in data sets, differentiate good data from bad data, and ultimately derive insights to help make actionable business decisions. Learners will see real-world examples of successful product analytics and learn how to utilize qualitative and quantitative measures for desirable outcomes.

7+ hours of instruction

Simple, visual explanations.

Product Goals

Acheive your product goals with effective KPIs, A/B testing, and data analysis

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