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Synopsis
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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About the Author
Rochelle King is Global VP of Design and User Experience at Spotify where she is responsible for the teams that oversee user research and craft the product experience at Spotify. Prior to Spotify, Rochelle was VP of User Experience and Product Services at Netflix. where she managed the Design, Enhanced Content, Content Marketing and Localization teams at Netflix. Collectively, these groups were responsible for the UI, layout, meta-data (editorial and visual assets) and presentation of the Netflix service internationally across all platforms. Rochelle has over 14 years of experience working on consumer facing products. Sean Power has worked as a web systems administrator since the mid 90s, has worked with online communities for companies such as MTV Northern Europe, and helped users reduce the headaches of managing and monitoring web infrastructures through Coradiant, a web performance monitoring vendor.
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Designing with Data: Improving the User Experience with A/B Testing
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