Librería Portfolio

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



Chapter 1Introducing 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
Chapter 2The ABCs of Using Data
The Diversity of Data
When is the data collected?
How is the data collected?
Why Experiment?
Basics of Experimentation
A/B Testing: Online Experiments
New users versus existing users
A big enough sample to power your test
Your Hypothesis and Why It Matters
Running Creative A/B Tests
Summary
Questions to Ask Yourself
Chapter 3A Framework for Experimentation
Introducing Our Framework
Three Phases: Definition, Execution, and Analysis
Examples: Data and Design in Action
Summary
Questions to Ask Yourself
Chapter 4The Definition Phase (How to Frame Your Experiments)
Getting Started: Defining Your Goal
Competing metrics
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
Chapter 5The Execution Phase (How to Put Your Experiments into Action)
Designing to Learn
Revisiting the minimum detectable effect
Designing the Best Representation of Your Hypothesis
Not all variables are visible
Different problems for summer camp
Running parallel experiments
Thinking about "Experiment 0ö
Summary
Questions to Ask Yourself
Chapter 6The Analysis Phase (Getting Answers From Your Experiments)
Vetting Your Designs Ahead of Launch
Launching Your Design
Is your experience "normalö right now?
Evaluating Your Results
What Does the Data Say?
Revisiting "thickö data
Seasonality bias
Rolling Out Your Experience, or Not
Case Study: Netflix on PlayStation 3
Summary
Questions to Ask Yourself
Chapter 7Creating 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
Spreading data across the organization
Summary
Questions to Ask Yourself
Chapter 8Conclusion
Ethical Considerations
Last Words
Appendix Resources
Keywords
Books
Online Articles, Papers, and Blogs
Courses
Tools
Professional Groups, Meetups, and Societies
Appendix About the Authors