Behavioral Data Analysis with R and Python

Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis. Common data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as any other data. Instead, this practical guide introduces powerful methods specifically tailored for behavioral data analysis.Advanced experimental design helps you get the most out of your A/B tests, while causal diagrams allow you to tease out the causes of behaviors even when you can't run experiments. Written in an accessible style for data scientists, business analysts, and behavioral scientists, this practical book provides complete examples and exercises in R and Python to help you gain more insight from your data--immediately.Understand the specifics of behavioral dataExplore the differences between measurement and predictionLearn how to clean and prepare behavioral dataDesign and analyze experiments to drive optimal business decisionsUse behavioral data to understand and measure cause and effectSegment customers in a transparent and insightful way

Florent Buisson is head of Behavioral Science at Allstate Insurance Company. He manages a team of behavioral scientists who run data analyses and implement behavioral interventions such as nudges.He previously worked for a French strategy consulting firm where he used economics and data analysis to answer complex measurement questions, for example building an index measuring the stability of agricultural policies in developing countries on behalf of the UN Food and Agriculture Organization. He also worked in specialty healthcare analytics, analyzing the behaviors of patients with severe diseases.Florent has published academic articles in journals such as the peer-reviewed Journal of Real Estate Research. He holds a Master's degree in econometrics as well as a Ph.D. in behavioral economics from the Sorbonne University in Paris.