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Opal · B2C · screen time app

Split users with Firebase and compare groups in Amplitude

Answers in about 8 days; ran 121 tests with a team of 7

Workedexperimentationanalytics-attribution

What they did

Firebase assigns A/B variants; a user property in Amplitude marks baseline vs variant, and uplifts and statistical significance are computed from Amplitude cohorts. Mostly binary baseline-vs-variant tests because of limited volume (sometimes a few variants, e.g. paywall at screen 3 vs 5); adopt the winner and repeat. Focus on early-funnel changes because they reach significance fastest; with a 7-day trial most answers arrive within 8 days. In hindsight, should have prioritized bigger swings by estimating likely uplift first.

What happened

121 A/B tests run by a seven-person team.
31:38
“we use Firebase for A/B testing, but we run our tests through Amplitude, the data, cohorts, data analysis through Amplitude, basically with a user parameter on Amplitude for baseline versus variants, and then we look at uplifts and figure out statistical significance.”
Kenneth Schlenker
32:23
“The earliest in the funnel, the easiest it is to get statistical significance quite fast.”
Kenneth Schlenker
37:29
“Mostly just like a binary test, because we didn't have a ton of volume.”
Kenneth Schlenker

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