Graham McNicoll

If you work inside an experimentation program, you have probably noticed that leadership speaks a different language than you do. You are talking about tests, quality scores, and learnings. They are looking at company KPIs. They will nod along when you present experimentation quality scores, but listen to how they talk, and they will not use these metrics. It's really hard to express your progress with experimentation when there is this language gap, and it can put your program in jeopardy. If this sounds familiar, I suggest you also represent your progress in terms of their metrics. Take something your team shipped and tested, get the results against all the KPIs your company actually cares about, produce a nice report, and share it up the chain. Even better if you can include a nice graph. Attach learnings that can be applied to other projects. People will be impressed. The data gives the company decisions a solid basis, and a degree of certainty that is welcome with all the uncertain decisions leadership has to make. And then something else happens. Every other product your company releases, the ones without that report, start to look woefully inadequate. Someone upstairs starts asking, "Wait, why does this one team have all this data backing the decision, and all this other team is shipping based on vibes?" You did not have to make that argument. The report made it for you. I guarantee you will look like a hero if you do this. And once leadership has seen one decision with real data behind it, it gets very hard for anyone to ship on pure vibes again.