Designing with Data
Improving the User Experience with A/B Testing
1st Edition
Published on 29. March 2017
370 pages
978-1-4493-3496-3 (ISBN)
Description
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 designGet a firm grounding in data, data types, and components of A/B testingUse an experimentation framework to define opportunities, formulate hypotheses, and test different optionsCreate hypotheses that connect to key metrics and business goalsDesign proposed solutions for hypotheses that are most promisingInterpret the results of an A/B test and determine your next move
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05/2017
O'Reilly
€47.00
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Content
- Intro
- [ Foreword ]
- [ Preface ]
- Introducing a Data Mindset
- Data as a Trend
- Three Ways to Think About Data
- What Does This Mean for You as a Designer?
- Data Can Help to Align Design with Business
- With a Little Help from Your Friends.
- What If You Don't Have Data Friends (Yet)?
- Themes You'll See in This Book
- Summary
- Questions to Ask Yourself
- The ABCs of Using Data
- The Diversity of Data
- Why Experiment?
- Basics of Experimentation
- A/B Testing: Online Experiments
- Your Hypothesis and Why It Matters
- Running Creative A/B Tests
- Summary
- Questions to Ask Yourself
- A Framework for Experimentation
- Introducing Our Framework
- Three Phases: Definition, Execution, and Analysis
- Examples: Data and Design in Action
- Summary
- Questions to Ask Yourself
- The Definition Phase (How to Frame Your Experiments)
- Getting Started: Defining Your Goal
- Identifying the Problem You Are Solving
- Building Hypotheses for the Problem at Hand
- The Importance of Going Broad
- Which Hypotheses to Choose?
- Summary
- Questions to Ask Yourself
- The Execution Phase (How to Put Your Experiments into Action)
- Designing to Learn
- Designing the Best Representation of Your Hypothesis
- Summary
- Questions to Ask Yourself
- The Analysis Phase (Getting Answers From Your Experiments)
- Vetting Your Designs Ahead of Launch
- Launching Your Design
- Evaluating Your Results
- What Does the Data Say?
- Rolling Out Your Experience, or Not
- Case Study: Netflix on PlayStation 3
- Summary
- Questions to Ask Yourself
- Creating the Right Environment for Data-Aware Design
- Principle 1: Shared Company Culture and Values
- Principle 2: Hiring and Growing the Right People
- Principle 3: Processes to Support and Align
- Summary
- Questions to Ask Yourself
- Conclusion
- Ethical Considerations
- Last Words
- Resources
- [ Index ]
