Ashley Stirrup

You never know where your next home run idea will come from. That's not a platitude. It's a data problem. At Bing, an idea sat in the backlog for months. PMs had rated it: "meh." Too many higher priorities. One engineer built it anyway. In a few days. Ran an A/B test. Then the alerts fired — Bing was making too much money. +12% revenue. $120M+. The best idea in Bing's history. Almost killed by intuition. But here's the part most people miss: that was a rare gem. Most wins are small. 0.1%. 0.5%. Occasionally 2%. Bing's ads team ran hundreds of experiments per year — typical monthly lift: 0.9%. 1.4%. Some months: a rollback. But those fractions compounded. Bing ads revenue grew 55% from 2014 to 2016. Inch by inch. The teams that win at experimentation understand both truths: → You can't predict your next home run — so you have to keep swinging → Small wins stack. The compounding is where the real growth lives. The only strategy that works: test everything, constantly. Triple your experiment rate and you triple your success rate. That's only possible when running an experiment is cheap — when your infrastructure is built for volume and speed. That's what @GrowthBook is built for. How much revenue is sitting untested in your backlog right now? 👇 Hat tip to Ron Kohavi — this story is from his Maven course "Accelerating Innovation with A/B Testing." which I loved!

Only one snapshot so far — the engagement-over-time curve appears once the daily scrape has captured this post at least twice.