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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, 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
Designing for Behavior Change
Designers and managers hope their products become essential for users—integrated into their lives like Instagram, Lyft, and others have become. Such deep integration isn’t accidental: it’s a process of careful design and iterative learning, especially for technology companies. This guide shows you how to apply behavioral science—research that supports many products—to help your users achieve their goals using your product. In this updated edition, Stephen Wendel, head of behavioral science at Morningstar, takes you step-by-step through the process of incorporating behavioral science into product design and development. Product managers, UX and interaction designers, and data analys...
Quantifying the User Experience
Quantifying the User Experience: Practical Statistics for User Research, Second Edition, provides practitioners and researchers with the information they need to confidently quantify, qualify, and justify their data. The book presents a practical guide on how to use statistics to solve common quantitative problems that arise in user research. It addresses questions users face every day, including, Is the current product more usable than our competition? Can we be sure at least 70% of users can complete the task on their first attempt? How long will it take users to purchase products on the website? This book provides a foundation for statistical theories and the best practices needed to appl...
Designing for Behavior Change
A new wave of products is helping people change their behavior and daily routines, whether it’s exercising more (Jawbone Up), taking control of their finances (HelloWallet), or organizing their email (Mailbox). This practical guide shows you how to design these types of products for users seeking to take action and achieve specific goals. Stephen Wendel, HelloWallet’s head researcher, takes you step-by-step through the process of applying behavioral economics and psychology to the practical problems of product design and development. Using a combination of lean and agile development methods, you’ll learn a simple iterative approach for identifying target users and behaviors, building t...
Marketing Data Science
Now, a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Star...
Think Bayes
If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer and you'll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but there aren't many resources available to help beginners. Based on undergraduate classes taught by author Allen B. Downey, this book's computational approach helps you get a solid start. Use your programming skills to learn and understand Bayesian statistics Work with problems involving estimation, prediction, decision analysis, evidence, and Bayesian hypothesis testing Get started with simple examples, using coins, dice, and a bowl of cookies Learn computational methods for solving real-world problems
