Designing with Data. Improving the User Experience with A/B Testing

Spis treści książki

• Designing with Data: Improving the User Experience with A/B Testing
• Praise for Designing with Data
• Foreword
• Preface
  • Design and Data: A Perfect Synergy
  • Our Focus: A/B Testing
  • Some Orienting Principles
  • Who Is This Book For?
  • Scope
  • About Us
    • A Word from Rochelle
    • A Word from Elizabeth
    • A Word from Caitlin
  • How This Book Is Organized
  • How to Read This Book
    • Introducing our Running a Camp Metaphor
  • OReilly Safari
  • How to Contact Us
  • Acknowledgments
    • Rochelle
    • Elizabeth
    • Caitlin
• 1. 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
    • On Data Quality
  • With a Little Help from Your Friends...
    • Data Producers
    • Data Consumers
  • What If You Dont Have Data Friends (Yet)?
  • Themes Youll See in This Book
  • Summary
  • Questions to Ask Yourself
• 2. The ABCs of Using Data
  • The Diversity of Data
    • Many Dimensions of Data
    • Why are you collecting data?
  • When is the data collected?
  • How is the data collected?
    • How much data to collect?
  • Why Experiment?
    • Learning About Causality
    • Statistically Significant, not Anecdotal
    • Informed Opinions about what will happen in the Wild
  • Basics of Experimentation
    • Language and Concepts
    • Race to the Campsite!
    • Experimentation in the Internet Age
  • A/B Testing: Online Experiments
    • Sampling Your Users Online
    • Cohorts and segments
    • Demographic information
  • New users versus existing users
    • Metrics: The Dependent Variable of A/B Testing
    • Detecting a Difference in Your Groups
    • How big is the difference you want to measure?
  • A big enough sample to power your test
    • Significance level
  • Your Hypothesis and Why It Matters
    • Defining a Hypothesis or Hypotheses
    • Know What You Want to Learn
  • Running Creative A/B Tests
    • Data Triangulation: Strength in Mixed Methods
    • The Landscape of Design Activities
    • Exploring and evaluating Ideas
    • Thinking Global and Thinking Local
  • Summary
  • Questions to Ask Yourself
• 3. A Framework for Experimentation
  • Introducing Our Framework
    • Working with Data Should Feel Familiar...
  • Three Phases: Definition, Execution, and Analysis
    • The Definition Phase
    • The Execution Phase
    • The Analysis Phase
  • Examples: Data and Design in Action
  • Summary
  • Questions to Ask Yourself
• 4. The Definition Phase (How to Frame Your Experiments)
  • Getting Started: Defining Your Goal
    • Defining Your Metric of Interest
    • Metric sensitivity
    • Tracking multiple metrics
    • Getting the full picture
    • Your metrics may change over time
  • Competing metrics
    • Refining Your Goals with Data
  • Identifying the Problem You Are Solving
    • Remember Where You Are
  • Building Hypotheses for the Problem at Hand
    • Example: A Summer Camp Hypothesis
    • Example: Netflixtransitioning from DVD Rentals to Streaming
  • The Importance of Going Broad
    • Multiple Ways to Influence a Metric
    • Focus on New and Existing Users
    • Revisit the Scope of Your Problem
    • Example: Netflix on the PlayStation 3
    • Involve Your Team and Your Data Friends
  • Which Hypotheses to Choose?
    • Consider Potential Impact
    • Using What You Already Know
    • Using Other Methods to Evaluate Your Hypotheses
    • Consider the Reality of Your Test
    • How much measurable impact do you believe your hypothesis can make?
    • Can you draw all the conclusions you want to draw from your test?
    • Balancing learning and speed
    • Keep Your Old Hypotheses in Your Back Pocket
  • Summary
  • Questions to Ask Yourself
• 5. The Execution Phase (How to Put Your Experiments into Action)
  • Designing to Learn
    • Engaging Your Users in a Conversation
    • Having Quality Conversations
    • Designing to extremes to learn about your users
  • Revisiting the minimum detectable effect
  • Designing the Best Representation of Your Hypothesis
    • Understanding Your Variables
  • Not all variables are visible
    • Your Design Can Influence Your Data
    • Example: Netflix Wii
    • Revisiting the Space of Design Activities
    • Avoiding Local Maxima
  • Different problems for summer camp
    • Directional testing: Painted door tests
    • Picking the right level of granularity for your experiment
    • Example: Netflix on Playstation 3
    • Example: Spotify Navigation
    • Experiment 1: Defining the hypothesis to get early directional feedback
    • Experiment 1: Designing the hypotheses
    • Interlude: Quick explorations using prototypes and usability testing
    • Experiment 2: Refining the tabbed navigation
    • Designing your tests
    • Other Considerations When Designing to Learn
    • Polishing your design too much, too early
    • Edge cases and worst-case scenarios
    • Taking advantage of other opportunities to learn about your design
    • Identifying the Right Level of Testing for Different Stages of Experimentation
  • Running parallel experiments
  • Thinking about Experiment 0
  • Summary
  • Questions to Ask Yourself
• 6. The Analysis Phase (Getting Answers From Your Experiments)
  • Vetting Your Designs Ahead of Launch
    • Lab Studies: Interviews and Usability Testing
    • Surveys
    • Working with Your Peers in Data
  • Launching Your Design
    • Balancing Trade Offs to Power Your Test
    • Weighing sample size and significance level
    • Getting the sample that you need (rollout % versus test time)
    • Who are you including in your sample?
    • Practical Implementation Details
  • Is your experience normal right now?
    • Sanity check: Questions to ask yourself
  • Evaluating Your Results
    • Revisiting Statistical Significance
  • What Does the Data Say?
    • Expected (Positive) Results
    • Unexpected and Undesirable (Negative) Results
    • When the World is Flat
    • Errors
    • Replication
    • Using secondary metrics
    • Using multiple test cells
    • Rolling out to more users
  • Revisiting thick data
    • Getting Trustworthy Data
    • Novelty effect
  • Seasonality bias
  • Rolling Out Your Experience, or Not
    • Whats Next for Your Designs?
    • Were you exploring or evaluating?
    • Was your problem global or local?
    • Knowing when to stop
    • Ramp Up
    • Holdback Groups
    • Taking Communication into Account
  • Case Study: Netflix on PlayStation 3
    • Many Treatments of the Four Hypotheses
    • Evolving the Design Through Iterative Tests
    • What If You Still Believe?
  • Summary
  • Questions to Ask Yourself
• 7. Creating the Right Environment for Data-Aware Design
  • Principle 1: Shared Company Culture and Values
    • Depth: Communicating Across Levels
    • Breadth: Beyond Design and Product
    • The Importance of a Learning Culture
    • The rewards of taking risks: Redefining failure
    • The value of developing your customer instinct
  • Principle 2: Hiring and Growing the Right People
    • Establishing a Data-Aware Environment Through Your Peers
    • Hiring for Success
    • Building the team with data involved from the start
  • Principle 3: Processes to Support and Align
    • Establishing a Knowledge Baseline
    • Establishing a Common Vocabulary
    • Developing a Rhythm Around Data Collection and Sharing
    • Project review meetings
  • Spreading data across the organization
    • Creating a Presence in the Office
    • Learning from the Past
  • Summary
  • Questions to Ask Yourself
• 8. Conclusion
  • Ethical Considerations
    • Ethics in Online Experimentation
    • Design Experimentation Versus Social Experimentation
    • Two Power of Suggestion Experiments
    • Toward Ethical A/B Testing
    • Key Concepts
    • Asking Questions, Thinking Ethically
  • Last Words
• A. Resources
  • Keywords
    • Chapter 1
    • Chapter 2
    • Chapter 3
    • Chapters 4, 5, and 6
    • Chapter 7
    • Chapter 8
  • Books
  • Online Articles, Papers, and Blogs
  • Courses
  • Tools
  • Professional Groups, Meetups, and Societies
• B. About the Authors
• About the Authors
• Colophon
• Index
• Copyright

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