Designing with Data Improving the User Experience with A/B Testing
Designing with Data Improving the User Experience with A/B Testing by Rochelle King, published by O’Reilly Media, Incorporated in 2017, is a comprehensive guide that explores the intersection of design practices and data science. This 337-page book presents a practical approach to conducting data-driven A/B testing, aimed at helping designers and product managers understand user behavior to create effective digital experiences. It emphasizes how data can enhance design and vice versa, providing a framework for integrating these disciplines into the product design workflow.
Readers will find detailed insights into the relationship between data, business, and design, along with foundational knowledge of data types and A/B testing components. The book outlines a structured experimentation framework that guides users in defining opportunities, formulating hypotheses, and testing various design options. Through real-world examples, it illustrates how to connect hypotheses to key metrics and business goals, design promising solutions, and interpret A/B test results to inform future design decisions. This edition serves as a valuable resource for those looking to enhance their understanding of user interfaces and web design through data-driven methodologies.
Official synopsis Publisher
On the surface, design practices and data science may not seem like obvious partners. But these disciplines actually work toward the same goal, helping designers and product managers understand users so they can craft elegant digital experiences. While data can enhance design, design can bring deeper meaning to data.
This practical guide shows you how to conduct data-driven A/B testing for making design decisions on everything from small tweaks to large-scale UX concepts. Complete with real-world examples, this book shows you how to make data-driven design part of your product design workflow.
- Understand the relationship between data, business, and design
- Get a firm grounding in data, data types, and components of A/B testing
- Use an experimentation framework to define opportunities, formulate hypotheses, and test different options
- Create hypotheses that connect to key metrics and business goals
- Design proposed solutions for hypotheses that are most promising
- Interpret the results of an A/B test and determine your next move
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