Experimentation center of excellence: Best practices

If you've ever watched a promising A/B test fail because teams weren't aligned on metrics, or seen duplicate experiments waste months of engineering time, you know the pain of uncoordinated experimentation. The solution isn't more tools or processes - it's building an experimentation center of excellence (CoE).

Here's the thing: most companies think they're data-driven because they run tests. But without a CoE to orchestrate efforts, you end up with chaos: marketing testing one thing, product testing another, and nobody sharing what they learned. Let's fix that.

Understanding the importance of an experimentation center of excellence

An experimentation center of excellence is basically your company's testing brain trust. It's where expertise lives, standards get set, and teams actually talk to each other about what they're testing.

The real magic happens when you centralize the messy parts of experimentation - like deciding which statistical methods to use, or figuring out how to test across different platforms without breaking everything. Instead of every team reinventing the wheel (badly), you get consistent processes that actually work.

But here's what most guides won't tell you: the biggest win isn't efficiency - it's cultural. When you have a CoE, experimentation stops being something only the data team does. Suddenly, everyone from customer service to engineering starts thinking "what if we tested that?" And when tests fail (they will), people learn instead of pointing fingers.

The challenge? Setting one up requires serious organizational change. As folks in this Reddit thread discovered, you need three things right off the bat: clear objectives that aren't just "test more stuff," actual executive buy-in (not just head nods), and some early wins to prove this isn't just another corporate initiative that'll die in six months.

Core components of a successful experimentation center of excellence

Let's get practical. A CoE isn't built on good intentions - it needs specific components to actually function.

Leadership sets the tone, but not in the way you think. The best CoE leaders I've seen don't just align experiments with business goals - they actively protect teams from HiPPO-driven tests (you know, when the Highest Paid Person's Opinion overrides data). They prioritize based on potential impact, not who's asking loudest.

Cross-functional collaboration sounds like corporate buzzword bingo, but it's actually critical. When your payments team discovers that changing button colors increased conversions by 12%, your onboarding team needs to know. The teams at CRO Metrics found that departments working in silos missed 40% of optimization opportunities because they couldn't see the full customer journey.

Here's what your CoE actually needs to standardize:

• Experiment design templates (so people stop forgetting control groups)

• Metric definitions (is a "user" someone who signed up or someone who's active?)

• Statistical significance thresholds (please, no more p-hacking)

• Results documentation (including failures - especially failures)

Communication can't be an afterthought. Regular experiment reviews where teams share results - good and bad - create that learning culture everyone talks about but few achieve. One company I worked with started "Failure Fridays" where teams presented experiments that didn't work. Participation was voluntary at first, but once people saw failed tests weren't career-limiting, it became their most attended meeting.

Best practices for running effective experiments within a CoE

Writing good hypotheses is like writing good code - it takes practice, but once you get it, everything else falls into place. Skip the vague "this will improve user experience" nonsense. Get specific: "Adding progress indicators to our checkout flow will reduce cart abandonment by 15% because users won't think the page froze."

The technical stuff matters more than you'd think. Randomization isn't just statistical best practice - it's what keeps your results from being garbage. I've seen too many "experiments" where someone just compared this month to last month and called it a test. That's not experimentation; that's wishful thinking.

Sample size calculations make people's eyes glaze over, but here's the reality: underpowered experiments are worse than no experiments. You waste time, get inconclusive results, and lose credibility. Use a sample size calculator (Statsig has a good one), add 20% buffer for real-world messiness, and stick to it.

Knowledge sharing is where most CoEs fall apart. It's not enough to have a shared Google Doc nobody reads. You need:

• Experiment repositories that are actually searchable

• Regular learning sessions (keep them under 30 minutes)

• Post-mortems for big wins AND big failures

• A culture where "we tried that already" comes with "here's what we learned"

Overcoming challenges and sustaining an experimentation coe

Getting buy-in is where reality hits. Executives say they want experimentation until they see it might delay their pet feature by two weeks. The trick? Start with problems they already care about. If the CEO is obsessed with retention, your first experiments should target retention metrics.

As teams at Globant discovered, resource allocation is make-or-break. A CoE without dedicated budget and people is just a fancy name for "extra work nobody has time for." You need at least one full-time person who owns this, plus budget for tools and training.

Measuring impact gets tricky because not everything valuable is immediately measurable. Sure, track the obvious stuff:

• Number of experiments run

• Win rate (though 10-20% is normal, not failure)

• Revenue/efficiency gains from winning tests

But also track the squishy stuff. Are more teams running experiments? Are people making decisions based on test results instead of opinions? The team at Netflix measures "decision velocity" - how quickly teams can validate ideas and move forward.

Here's the uncomfortable truth: most CoEs fail because they try to do too much too fast. Start small. Pick one team or product area, get them running clean experiments, show results, then expand. The Harvard Business Review's research on data-driven decision making shows companies that scale gradually see 3x better adoption than those that go company-wide immediately.

Closing thoughts

Building an experimentation CoE isn't about creating another layer of bureaucracy - it's about making testing so smooth that teams can't imagine working without it. When done right, you'll stop hearing "I think this will work" and start hearing "let's test it."

Want to dig deeper? Check out Statsig's guide on experimentation best practices, or if you're ready to get your hands dirty, CRO Metrics has a solid implementation playbook.

Start small, fail fast, learn faster. Hope you find this useful!