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
"Infrastructure must evolve to accommodate the realities of climate change."
Alex Hurworth Bonnie John Fahd Arshad Antoine MarinDesigning a Contact Tracing App for Universal Access
October 23, 2020
"A simplifier can explain complex situations in the easiest way possible without losing nuance."
Kate SternScaling Learning for the Future
September 9, 2022
"Big projects tend to get out of hand and scare stakeholders, so starting small is key to scaling impact."
Irina Tikhonova Kari DietrichSmall Wins, Big Impact: Leveraging and Elevating User Engagement
December 9, 2021
"The story of the Indian king and grains of rice perfectly illustrates exponential growth."
Scott PlewesWhy Isn't Your UX Approach Going Viral?: A Mathematical Model
March 28, 2023
"Research waste turns into research wealth when insights are properly activated inside organizations."
Kate Towsey Jake BurghardtResearchOps AMA with Kate Towsey & Jake Burghardt
October 16, 2025
"Research does this and design does that creates an us versus them strategy that leads to needless friction."
Alastair SimpsonDebunking the Myths of Cross-Disciplinary Collaboration
October 24, 2019
"Monoculture in stories is like having just one narrative that doesn’t capture the full reality."
Kwabena Opoku Leonie Annor-Owiredu Sam LadnerMethodological toolkit for unique research impact
March 11, 2026
"Large language models are far better at figuring out what you mean than simply giving an answer."
Josh Clark Veronika KindredSentient Scenes and Radically Adaptive Experiences
June 11, 2025
"The most important thing to remember is to be flexible in your concept of a deliverable."
Tiffany ChengDesigning in a Pandemic: Integrating Speed and Rigor
June 9, 2022
