Naming conventions: the most boring thing that makes experimentation programs scale

It blows my mind how many companies run experimentation without a naming convention.

Not an imperfect one. None. Tests called "homepage revamp" or "Q2 pricing," somewhere in a spreadsheet or just inside Intelligems, with no structure, no ID, no way to find them six months later without scrolling through 40 rows trying to remember what was actually being tested.

And I think it's a hard way to scale an experimentation program.

The fix is simple: give every test a unique ID and a structured name before you do anything else.

The ID part is what I'm most emphatic about. Something as simple as #1, #2, #3. Or more structured: EXP-23. Looks bureaucratic. Feels unnecessary on your first few tests.

But here's what I noticed when I was on the agency side: stakeholders started referencing the IDs in their own conversations. Instead of "that price test from March," they'd say "test 47." That moment, when a client uses a test ID unprompted in a meeting, that's when you know the program has actually landed. They're thinking in experiments now, not in one-off projects.

It just makes everyone's lives easier. Stakeholder updates, Slack threads, QA handoffs, everything gets cleaner when there's a shared shorthand. And when you need to iterate on a test, same hypothesis, new variant, you just add a suffix: Ex47a, Ex47b. No confusion, no "wait, which version of that test are we talking about?"

Beyond the ID, a naming structure worth trying:

'Ex[#] | Surface | Short Name | Date'

Example: 'Ex47 | PDP | Anchor Price Removal | May 2026'

The best part: this is trivial to auto-generate with a formula in whatever tool you're using, Notion, Airtable, Google Sheets. One formula column that builds the name from your existing fields. You fill in the surface and a short description, the ID increments automatically, and the full experiment name writes itself.

One line. You know what number it is, where it ran, and what changed. A year from now you can look back across your test library and actually see patterns, which surfaces you've tested most, which hypotheses keep winning, where you've put a lot of effort with not much to show for it.

That's the difference between running tests and building an experimentation program. Subtle change. The payoff compounds.

Curious what systems you're using. Drop your naming convention below!

0 comments