Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights
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
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 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 yourself
- Use 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