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, thispractical 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 data
- Explore the differences between measurement and prediction
- Learn how to clean and prepare behavioral data
- Design and analyze experiments to drive optimal business decisions
- Use behavioral data to understand and measure cause and effect
- Segment customers in a transparent and insightful way
Table of contents
I. Understanding Behaviors
1. The Causal-Behavioral Framework for Data Analysis
2. Understanding Behavioral Data
II. Causal Diagrams and Deconfounding
3. Introduction to Causal Diagrams
4. Building Causal Diagrams from Scratch
5. Using Causal Diagrams to Deconfound Data Analyses
III. Robust Data Analysis
6. Handling Missing Data
7. Measuring Uncertainty with the Bootstrap
IV. Designing and Analyzing Experiments
8. Experimental Design: The Basics
9. Stratified Randomization
10. Cluster Randomization and Hierarchical Modeling
V. Advanced Tools in Behavioral Data Analysis
11. Introduction to Moderation
12. Mediation and Instrumental Variables
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