Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights by Joanne Rodrigues
English | October 1, 2020 | ISBN: 0135258529 | True PDF | 447 pages | 6 MB
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 productsThink like a social scientist and contextualize individual behavior in today's social environmentsBuild more effective metrics and KPIs for any web product or systemConduct more informative and actionable A/B testsExplore causal effects, reflecting a deeper understanding of the differences between correlation and causationAlter user behavior in a complex web productUnderstand how relevant human behaviors develop, and the prerequisites for changing themChoose the right statistical techniques for common tasks such as multistate and uplift modelingUse advanced statistical techniques to model multidimensional systemsDo 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 wantModel, understand, and alter customer behavior to increase revenue and retentionConstruct better frameworks for examining why your customers do what they doDevelop core metrics for user analytics, and conduct more effective A/B testsMaster key techniques that most books ignore, including statistical matching and uplift modelingUse R and this book's many R examples to implement these techniques yourselfUse data science and social science to generate real changes in customer behaviorBuild better theories and metrics, and drive more of the behaviors you wantModel, understand, and alter customer behavior to increase revenue and retentionConstruct better frameworks for examining why your customers do what they doDevelop core metrics for user analytics, and conduct more effective A/B testsMaster key techniques that most books ignore, including statistical matching and uplift modelingUse R and this book's many R examples to implement these techniques yourself
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