How the Fastest-Growing Companies Build a Culture of Experimentation
We tend to glorify speed in product development.
But speed alone isn’t what separates good teams from great ones.
The real differentiator?
It was a product mindset:
The ability to run trustworthy, scalable experiments — and learn from them faster than anyone else.
Companies like Meta, Airbnb, and Booking.com didn’t grow by guessing. They scaled because they learned systematically, made data-driven bets, and built an engine that remembered what worked — and what didn’t.
In this article, I’ll break down the three pillars that enable scalable experimentation:
- Trustworthy Experiments
- Institutional Memory
- A Deeply Rooted Data Culture
Let’s dive in.
Pillar 1: Trustworthy Experiments
You can’t scale what your team doesn’t trust. And too often, experiments fail to earn that trust — not because the data is bad, but because of how it’s interpreted.
Here are three common trust killers and how to avoid them.
1. Outlier Customers Skewing the Data
An enterprise client churning can look like a massive problem. But sometimes, it’s just one user — albeit a big one — throwing off your entire test result.
The risk: You optimize for edge cases instead of your core audience.
The fix: Use stratified sampling. Balance cohorts by customer size or type. That way, your conclusions aren’t hijacked by the 1%.
2. Novelty Effects That Fade Fast
Week one shows a big lift. Excitement brews. By week six, results flatline. It wasn’t a breakthrough — it was a mirage.
The risk: Wasting months chasing temporary gains.
The fix: Track metrics over weeks, not days. Include holdout groups that never get the change so you can measure lasting impact. Value sustained improvement over flashy spikes.
3. Inconsistent Methodology Across Teams
Growth runs one experiment. Product runs a similar one. Results conflict. Teams lose trust in testing altogether.
The risk: Fragmentation, confusion, and paralysis.
The fix: Standardize your testing methodology. Create shared playbooks. Make rigor a habit, not a hero move.
Pillar 2: Institutional Memory
Running experiments is easy. Remembering what you learned is hard.
Without systems to retain insights, teams unknowingly repeat the same tests — or worse, relearn old failures. Here’s how to avoid that:
1. Track Your Batting Average
You should know your hit rate. The industry average? About 1 in 3 experiments drive meaningful lift. And the average uplift? Around 8%.
It was a product mindset:
Why it matters: You can focus resources on high-confidence bets instead of wishful thinking.
2. Automate Documentation
Let’s be honest — manual documentation never scales. People skip it. Things get lost. Learnings disappear.
The fix: Automate the capture of hypotheses, setup, and results at the point of test creation. If it’s frictionless, it actually happens.
3. Share Learnings Across Teams
Your growth, marketing, and product teams are all experimenting — but the insights often stay siloed.
Solution: Build shared repositories or internal wikis. A new hire shouldn’t have to spend 6 months discovering what the last PM already tested.
Institutional memory turns short-term wins into compounding returns.
Pillar 3: Data Culture
Even the best experiment won’t help if your culture doesn’t know how to use it. These cultural foundations separate data-informed teams from data-driven disasters:
1. Standardized Metrics
Everyone should speak the same language. If “engagement” means something different to marketing than it does to product, you’re asking for chaos.
It was a product mindset:
Action: Build a shared metrics dictionary. Define KPIs clearly and ensure everyone adheres to them.
2. Celebrate Truth, Not Ego
If your team feels pressured to be “right,” you’ll get cherry-picked metrics and post-hoc rationalizations.
The fix: Normalize negative results. Reward the discovery of truth — even if it proves you wrong. This isn’t just a data practice. It’s a leadership choice.
3. Foster Statistical Literacy
You don’t need a PhD in statistics to make good decisions. But you do need the basics: confidence intervals, p-values, false positives.
Goal: Give every PM, designer, and marketer just enough training to separate signal from noise.
When your whole team understands the rules of the game, they stop playing to win arguments — and start playing to learn.
Why This Matters
If you don’t build this system, here’s what happens:
- You test the same ideas repeatedly
- You forget what worked
- You lose trust in data
- And your competition learns faster than you
In fast-moving markets, the speed of learning is your true competitive advantage.
Want to go deeper? I broke down the full system in a free deep dive (no paywall, thanks to Statsig): Read here
The Bottom Line
Velocity without validation is just noise.
It’s not enough to ship fast — you need to ship smarter, learn faster, and remember longer.
Experimentation isn’t a tool.
It’s a culture.
And the best teams treat it like a superpower.
If you found this helpful, follow me for more on product experimentation, strategy, and building high-performance teams.
