GTM Sauce

Life360 · B2C · family location and safety app

Track test speed, win rate and average win, and count inconclusive tests as failures

Unknownexperimentation

What they did

Treat experiments as a learning portfolio with three levers: velocity (experiments shipped), win rate, and average win size. Current push: raise velocity with AI while holding win rate and average win stable. Measure the number of inconclusive tests and work to reduce it; losses are fine if they produce learnings. Predict experiment impact up front, then segment results after (e.g. lost overall but worked for suburban users with a dog and 1+ month tenure) to generate follow-up experiments. People who know the users interpret the data, not only automated systems.
Stage
scale
“Velocity, how many experiments do you ship? We rate what percentage of those are successful, but there is a caveat there that we go through now. And the average win, right?”
Giordano Contestabile
“So what we do is we measure the amount of inconclusive tests and we think about how we can reduce that?”
Giordano Contestabile

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