Flaptekst

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 yourselfUse 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