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The four questions Early Warning asks before any A/B test
How Supercell A/B tests 300 million players without breaking trust
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
Top takeaways from our favorite conversations
Close every losing test with two questions: did it work for a granular segment, and is the idea worth further investment?
Shift from MVP to MVT: list leap-of-faith assumptions and design minimum viable tests before you build.
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Friction can increase revenue. Blocking the "view all" grid and forcing a style choice sent shoppers deeper and lifted conversion and revenue, because the extra click added value.
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.
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.
Separate your two experimentation modes: high-volume CRO chases many small wins, while big uncertain bets deserve multiple shots to de-risk.
Build composite metrics (e.g., CPQI) to align finance, engineering, and data science around shared outcomes.
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.
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A failed test can hold the real winner; contextual onboarding matched to user intent roughly doubled activation and became the default variant after the bundling experiment was rolled back.
Simplification has a limit. Removing too much can strip away the cues and context buyers actually need to decide.
Test metrics before you test features — usage time could signal engagement or just mean your product takes too long to do its job.
When you struggle to land a result, lead with the story of what the customer did, then bring the numbers.
Unblock teams: create a center of excellence for data science and enable rapid variants with AI-powered tooling.
Persistence pays: four months and three to four rounds of trial-model testing at Codecademy produced a 35% conversion increase.
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.
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.
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."
Share losses as openly as wins. Wins build credibility, and losses build the psychological safety a testing culture runs on.
