Overview

This guide shows how to combine data science with social science to gain unprecedented insight into customer behaviour, so you can change it. Joanne Rodrigues 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 behaviours you want and less you don't. Drawing on extensive enterprise experience and deep knowledge of demographics and sociology, Rodrigues shows how to create better theories and metrics, so you can accelerate the process of gaining insight, altering behaviour, and earning business value.

Full Product Details

Author:   Joanne Rodrigues
Publisher:   Pearson Education (US)
Imprint:   Addison Wesley
Dimensions:   Width: 17.50cm , Height: 2.50cm , Length: 22.90cm
Weight:   0.680kg
ISBN:  

9780135258521

ISBN 10:   0135258529
Pages:   448
Publication Date:   07 January 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Available To Order  
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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 Hil'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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Author Information

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 behaviour by using economic and sociological theory in the context of complex mathematical models.

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