Experiment documentation: Creating knowledge bases

You know that sinking feeling when someone asks about an experiment you ran six months ago, and you can't remember if the 15% lift was statistically significant or just noise? Yeah, we've all been there.

The reality is, most teams are sitting on a goldmine of experimental learnings that nobody can actually find. It's scattered across Slack threads, buried in spreadsheets, and locked away in the heads of people who might not even work there anymore. Here's how a well-built knowledge base can fix that - and actually make your experimentation program stronger in the process.

The role of knowledge bases in experiment documentation

Let's be honest: documenting experiments feels like homework nobody wants to do. But here's the thing - without proper documentation, you're basically throwing away all those insights you worked so hard to uncover. A centralized knowledge base changes the game entirely.

Think of it as your team's collective memory. Instead of asking "didn't we test something like this before?" in every planning meeting, you can actually pull up past experiments and see exactly what worked (and what didn't). This is especially clutch when onboarding new team members. Rather than spending weeks trying to download years of context from different people's brains, they can self-serve and get up to speed fast.

But the real magic happens when documentation becomes part of your workflow, not an afterthought. Teams that nail this create a searchable repository of wins, failures, and "huh, that's weird" moments that spark new test ideas. It's like having a conversation with your past self - except your past self actually took good notes.

The transparency piece matters too. When everyone can see what's been tested, you avoid that awkward moment where two teams run basically the same experiment three months apart. Plus, it keeps people accountable. If your test bombed, that's valuable data - but only if someone can actually find it later.

Here's where tools like Statsig's Experiment Knowledge Base come in handy. Instead of manually copying results into some dusty wiki page, the platform automatically captures experiment details. You just add the context and insights while they're still fresh in your mind. No more "I'll document this later" (spoiler: later never comes).

Best practices for creating experiment knowledge bases

Alright, so you're sold on the idea. But building a knowledge base that people actually use? That's where things get tricky.

Start with the basics: what are you actually trying to accomplish here? If your goal is just to check a compliance box, you'll end up with a graveyard of half-filled templates. But if you're building something that helps teams ship better features faster, that changes everything. Get clear on the purpose before you write a single doc.

Organization is make-or-break. Nobody's going to dig through 500 untagged experiments to find that one test about checkout flow from 2021. You need:

• Consistent naming conventions (yes, boring but crucial)

• Smart categorization that matches how your team thinks

• Keywords that people will actually search for

• A structure that works for both technical and non-technical users

Here's a pro tip: add charts, screenshots, and visuals wherever possible. A picture really is worth a thousand words when you're trying to explain why that button color test had such weird results. The data team might love your statistical analysis, but the design team just wants to see what the variants looked like.

The maintenance part is where most knowledge bases go to die. Set up a simple review process - maybe quarterly check-ins where teams update their sections. Better yet, make contributing part of the experiment wrap-up process. As the knowledge base best practices community will tell you, stale documentation is worse than no documentation.

Building and maintaining an effective experiment knowledge base

Picking the right tools and platforms feels overwhelming, but it doesn't have to be. The best knowledge base is the one your team will actually use. Fancy features don't matter if everyone ignores them.

Start by looking at your existing workflow. If your team lives in Slack, a tool that integrates there will get more traction than something that requires switching contexts. Same goes for your experimentation platform - the less copying and pasting required, the better.

Once you've got your tool sorted, you need guidelines. Not a 50-page manual nobody will read, but clear basics like:

• What counts as an experiment worth documenting?

• Who's responsible for adding the documentation?

• What's the minimum viable documentation (hint: hypothesis, what you did, what happened, what you learned)

• How quickly after an experiment should docs be added?

Get your stakeholders involved early. Product managers, engineers, designers, data scientists - they all have different needs from a knowledge base. The PM wants business impact, the engineer wants implementation details, the designer wants visual examples. Build something that serves everyone, or watch it become another ghost town.

Regular updates keep things fresh. Assign ownership - maybe rotate it quarterly so it doesn't become one person's permanent burden. Use templates to make documentation consistent and faster. And invest in search that actually works. If people can't find experiments in under 30 seconds, they'll stop looking.

Leveraging knowledge bases to foster an experimentation culture

Here's where it gets interesting. A good knowledge base doesn't just store information - it changes how your team thinks about experimentation.

When past experiments are easy to find, something shifts. That random idea in a brainstorming session? Someone can quickly check if you've tested something similar. The heated debate about user preferences? Pull up the actual data from previous tests. Teams at companies like Airbnb and Booking.com credit their experimentation success partly to making past learnings accessible to everyone.

The meta-analysis opportunities are huge too. Once you've got dozens (or hundreds) of experiments documented, patterns emerge. Maybe you notice that tests on mobile consistently show different results than desktop. Or that certain types of copy changes never seem to move the needle. These insights only surface when you can look across experiments, not just at individual tests.

Statsig's Knowledge Base takes this further by making experiment learnings searchable across teams. So the growth team can learn from the platform team's infrastructure tests, and vice versa. It breaks down the silos that usually keep valuable insights trapped in team bubbles.

Want to really level up your experimentation culture? Try these moves:

• Make checking the knowledge base the first step in any experiment planning

• Share "experiment of the month" highlights in all-hands meetings

• Create learning sessions where teams walk through their most surprising results

• Reward people who surface insights from old experiments that inform new ones

The goal isn't just to document for documentation's sake. It's to create this virtuous cycle where every experiment makes the next one better. Where failures become as valuable as successes because someone else can learn from them. Where your tenth experiment on a feature is informed by the previous nine, not starting from scratch.

Closing thoughts

Building an experiment knowledge base isn't sexy work. It's not going to get you a conference talk or a promotion next week. But it might be the single highest-leverage thing you can do to improve your team's experimentation game.

Start small. Pick your last five experiments and document them properly. See what format works, what information actually proves useful later. Then build from there. Before you know it, you'll have this incredible resource that makes everyone's job easier and your experiments more impactful.

Want to dive deeper? Check out how teams at Google, Microsoft, and Netflix approach experiment documentation. Or just start with your next experiment and document it like you're explaining it to yourself six months from now.

Hope you find this useful!