Experimentation best practices: Leading companies

Here's a funny thing about experimentation culture - everyone wants it, but most companies get stuck at the starting line. They'll nod along when you talk about data-driven decisions and testing hypotheses, but when it comes time to actually run experiments, suddenly there are a million reasons why they can't.

The truth is, building a real experimentation culture isn't about buying fancy tools or hiring data scientists (though those help). It's about changing how your entire organization thinks about failure, learning, and decision-making. Let me walk you through what actually works, based on what I've seen at companies that do this well.

Embracing a culture of experimentation

The companies that get experimentation right all share one trait: they've made it okay to be wrong. Amazon runs thousands of experiments every year, and most of them fail. That's not a bug - it's the whole point. Each failed experiment teaches them something valuable about their customers.

But here's where most companies mess up. They think experimentation culture happens by decree. The CEO sends out an email saying "we're data-driven now!" and expects magic to happen. It doesn't work that way. Real experimentation culture grows from the bottom up when you give teams three things:

• Permission to test bold ideas (even the weird ones)

• Tools that make experimentation easy, not painful

• Protection when experiments don't pan out

I've seen this play out at Microsoft, where teams run experiments on everything from button colors to entire product features. The key insight from their approach? They treat every experiment as a learning opportunity, win or lose. Failed experiments aren't career-limiting moves - they're contributions to organizational knowledge.

The hardest part isn't the technical setup. It's getting middle managers on board. They're often the ones who kill experimentation culture because they're worried about hitting quarterly targets. Smart companies solve this by changing how they measure manager performance, rewarding learning and iteration over just hitting predetermined metrics.

Implementing standardized processes for scalable experimentation

Once you've got buy-in, you need to make experimentation repeatable. Otherwise, you'll end up with chaos - every team running experiments differently, no way to compare results, and a lot of wasted effort.

Start with templates. I know, templates sound boring, but they're your secret weapon for scaling. Good experiment templates force teams to think through the important stuff upfront:

• What are we testing and why?

• How will we measure success?

• What's our hypothesis?

• When do we pull the plug if things go sideways?

The team at Atlassian learned this the hard way when they tried to scale experimentation across multiple services. Without standardized processes, their experiments became impossible to monitor and debug. Now they use consistent templates and tooling across teams, which means anyone can understand and learn from any experiment in the company.

Here's a practical tip: don't go overboard with process. I've seen companies create 20-page experiment design documents that nobody reads. Keep your templates simple - one page max. The goal is to lower friction, not create bureaucracy. Statsig's approach of using Teams to enforce best practices at the team level strikes a nice balance. Each team gets to maintain some autonomy while still following organizational standards.

The real magic happens when you make experimentation self-service. Engineers shouldn't need to file a ticket to run an A/B test. Product managers shouldn't need a data scientist to analyze results. When teams can move fast and experiment independently, that's when you see innovation accelerate.

Leveraging technology to enhance experimentation efforts

Let's talk tools. You can build a culture of experimentation with spreadsheets and manual processes, but you'll hit a wall pretty quickly. Modern experimentation platforms do three things that transform how teams work.

First, they automate the boring stuff. Statistical significance calculations, sample size estimates, experiment monitoring - all the things that used to require a statistics degree now happen automatically. This democratizes experimentation. Suddenly, any product manager can run a valid A/B test without worrying about p-values.

Second, they provide real-time insights. The data science teams at leading tech companies don't wait weeks for experiment results. They're watching dashboards that update continuously, letting them spot problems early and double down on winners fast.

Third, and this is crucial - good platforms prevent you from shooting yourself in the foot. They'll warn you if your sample size is too small, alert you to potential statistical errors, and even run automated A/A tests to ensure your system is working correctly. This builds trust across the organization. When executives know the numbers are reliable, they're more likely to act on them.

But here's a warning: don't get seduced by fancy features you won't use. I've seen companies buy enterprise experimentation platforms with every bell and whistle, then use 10% of the functionality. Start simple. Pick a platform that your teams will actually use, not the one with the prettiest slides in the sales deck.

Overcoming challenges and fostering continuous improvement

Now for the reality check. Even with the right culture, processes, and tools, things will go wrong. The question isn't whether you'll face challenges - it's how you'll handle them.

The most common pitfall? People gaming the metrics. You set up an experiment to increase engagement, and someone figures out that annoying pop-ups technically count as engagement. Congratulations, you've just made your product worse while declaring victory. The fix is peer review - have someone outside the team sanity-check experiment designs before they launch.

Another killer of experimentation programs is what I call "success theater." This is when teams only share positive results and bury the failures. You need to celebrate the valuable failures as much as the wins. One company I worked with started a "Failure Friday" where teams shared their most instructive failed experiments. It completely changed how people thought about negative results.

Here are the practices that separate companies that sustain experimentation culture from those that don't:

• Regular retrospectives on both successful and failed experiments

• A centralized repository of learnings (not just results)

• Cross-team sharing sessions

• Investment in ongoing education and training

The companies that excel at experimentation, like those discussed in machine learning circles, treat it as a core competency that requires constant refinement. They don't just run experiments - they experiment with how they experiment.

Closing thoughts

Building a culture of experimentation isn't a project with an end date. It's an ongoing journey that requires commitment, patience, and a willingness to challenge how things have always been done. The good news? You don't need to transform your entire company overnight.

Start small. Pick one team, give them the tools and support they need, and let them show what's possible. Success breeds success, and soon other teams will want in on the action. Before you know it, you'll have an organization where "let's test that" becomes the default response to every idea.

Want to dive deeper? Check out Statsig's guide to experimentation best practices or browse the discussion on becoming more experimentation-driven from the product management community.

Hope you find this useful! Remember, the best experiment is the one you actually run. So stop planning and start testing.