Filter results
How Clover experiments when billions of dollars flow through daily
Why JobLeads says one test won't move you, but 100 will
Inside Aspen Dental's 100-test-a-year experimentation program
Synthetic audiences meet real A/B tests at Principal Financial Group
Why US Bank considers missing even 1% of customers unacceptable
Why Farfetch manages by learning rate, not win rate
How Cogniteer Built an Experimentation Engine From Scratch
How Fin does 1,000,000 A/B Tests in 24 Hours
How Kargo turns losing experiments into competitive edges
The 'wine effect' and other surprises that reshaped how Box runs e-commerce experiments
Diligent explains why moving on from an experiment might cost you
The metric Stitch Fix says every experimenter should chase
What the Expedia Group cannot measure, it cannot ship
Top takeaways from our favorite conversations
Scale experimentation with AI: use Cursor desktop/cloud agents for parallel builds and visual QA; orchestrate docs/analysis via Claude; automate cleanups and reporting.
The biggest thing that gets a team testing is to just do it. Stop designing the perfect experiment and get something simple live to take away the mystery.
Separate your two experimentation modes: high-volume CRO chases many small wins, while big uncertain bets deserve multiple shots to de-risk.
Shift from MVP to MVT: list leap-of-faith assumptions and design minimum viable tests before you build.
False negatives are more dangerous than false positives — they get institutionalized as "we tried that, it didn't work" and quietly kill good ideas for years.
Twitch used geo-fenced experiments with matched markets and causal inference to measure true price elasticity, turning a feared pricing decision into a measured, accretive one.
Massey's first test removed navigation from UPS's shipping checkout flow and delivered $35 million in incremental revenue—proving e-commerce best practices apply even when customers think "this is just a tool, not e-commerce."
Prioritize like a pyramid: fix the widest-impact experiences first, then optimize down into smaller cohorts.
Win rate matters less than learnings per test — DoorDash ships company-wide experiment summaries (win or lose) that the CEO actively reads and responds to, creating cultural accountability around testing rigor.
Top-down buy-in shifts the conversation from "why test?" to "how do we test?": When leadership treats data as the tiebreaker, teams stop defending opinions and start building better experiments.
Democratize experimentation with a centralized platform and self-serve tooling; reset baselines regularly.
A losing test is a finding, not a failure. If every experiment wins, you're not taking enough risk to learn anything new.
Close every losing test with two questions: did it work for a granular segment, and is the idea worth further investment?
Build the triad: pair an easy-to-use platform with training, top-down sponsorship, and clear launch processes.
One centralized team of about 40 people tests every major change to Home Depot's $25B online business, serving 40–50 business teams with consistent hypothesis and analysis standards.
Persistence pays: four months and three to four rounds of trial-model testing at Codecademy produced a 35% conversion increase.
Documenting experiments in a centralized Wiki creates a growth flywheel: Fanatics' Wiki feeds their roadmap with iterations on already-built features, reducing tech dependency and accelerating velocity.
DoorDash's price experiment proved price by itself doesn't predict orders. Different customers want different things at different times, which pushed the team toward personalization.
When you struggle to land a result, lead with the story of what the customer did, then bring the numbers.
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