Log in or create a free Rosenverse account to watch this video.
Log in Create free account100s of community videos are available to free members. Conference talks are generally available to Gold members.
Summary
Experimentation can be intimidating to non-data science folk. But Erin wants to get everyone excited about A/B testing. In this talk, Erin shares the Conversion Design process. It centers A/B testing as a way to gather high-quality evidence to make highly informed decisions to improve your digital product. She also introduces the Good Experimental Design toolkit. These easy-to-follow templates usher teams through the logic needed to design trustworthy experiments that you can learn from.
Key Insights
-
•
Conversion design combines design, science, and business to intentionally create measurable improvements, not just arbitrary changes.
-
•
Ronald Fisher’s 1919 work exposed how poor experimental design led to decades of unreliable scientific data.
-
•
Conversion originates from the Latin word meaning to transform or change, which is broader than just sales or profit.
-
•
Traditional linear product development processes miss the complex, iterative nature of real-world systems.
-
•
Systems thinking offers a more accurate way to understand and manage design experiments within interconnected environments.
-
•
A rigorous conversion design process includes seven phases: understand, hypothesize, prioritize, create, test, analyze, and decide.
-
•
Experiments grounded in well-documented hypotheses based on research have higher success rates than guesses.
-
•
Randomized 50/50 AB testing is the gold standard for isolating the true effect of design changes by evenly distributing confounds.
-
•
Experimentation buckets—product foundations, content/motivation, accessibility/usability, and bug fixes—help prioritize work effectively.
-
•
Ethical considerations and guardrail metrics are crucial to ensure changes benefit all stakeholders sustainably and without manipulation.
Notable Quotes
"Conversion means change, not just sales or profit."
"Design is the rendering of intent — bringing ideas into form that solve the problem."
"Decades worth of agricultural experimental data was garbage because of poor experimental design."
"Most product teams stay on the bottom rungs of evidence, relying on opinions or observational data instead of randomized trials."
"You can never purely test an idea, only the implementation of the idea."
"Randomization is magic — it evenly distributes confounds so the observed effect is caused by your change."
"Not all changes create value; some do nothing or even make things worse."
"You have to think critically about how a change impacts all stakeholders, not just the main business metric."
"Ethics evolve faster than laws; just because something is legal doesn’t make it ethical."
"If an experiment I design made the front page news tomorrow, how would I feel about it?"
Or choose a question:
More Videos
"Everyone was doing a little bit of design ops here and there, which created a lot of gray area around decision making."
Michelle ChinThe DesignOps Starter Kit
September 29, 2021
"If you have a terrible boss, you can do an ethnography of your own organization disguised as friendly connection."
Robin Beers Sonja Bobrowska Mujtaba Hameed Josh MoralesHow to create actionable insight in the face of politics and silos [Advancing Research Community Workshop Series]
October 12, 2023
"Polaris was not just a database but a system involving humans and a strong curatorial role."
Michele MarutResearch Repositories Reconsidered
February 14, 2019
"The CEO wanted rapport, not just productivity, and the collaborative poster created that from scratch."
Elizabeth ChurchillExploring Cadence: You, Your Team, and Your Enterprise
June 8, 2017
"Unpaid design exercises are unfair because they don’t take into account people's real-life constraints."
Russ UngerOnboarding: The Ecosystem, not the Afterthought
November 7, 2017
"Excluding key people might save time but it often leads to surprise blockers and misaligned decisions later on."
Anne MamaghaniHow Your Organization's Generative Workshops Are Probably Going Wrong and How to Get Them Right
March 28, 2023
"Our folks are not looking for information about what are wireframes. They want information specific to your organization."
Gabrielle VerderberDocumentation Your Team Will Actually Use
October 3, 2023
"Executives only like things in threes, but if you do a three by three, you can get away with nine boxes—that's the max executive information processing."
John CutlerThe Alignment Trap
November 29, 2023
"Don Norman was named the top accomplished woman in UX by AI, even though Don is a man."
Dan SafferWhy AI projects fail (and what we can do about it)
May 14, 2025
Latest Books All books
Dig deeper with the Rosenbot
How does allowing users to explore interactive AI prototypes affect feedback quality?
How do you formalize qualitative coding rules to enable automation in quantitative ethnography?
Why is Mary Shelley’s Frankenstein a useful metaphor for considering the responsibilities of creators in UX research tool development?