What metric should a push A/B test optimize?

Open rate (the notification tap-through) is the usual primary metric because push copy most directly affects whether someone taps. Always pair it with a downstream conversion metric and an opt-out guardrail, since a punchy push that drives unsubscribes is a net loss.

How is the required sample size calculated?

The builder uses a two-proportion z-test sized for 95% significance and 80% power from your baseline open rate and the minimum lift you want to detect. Smaller lifts and lower baselines require larger audiences to detect reliably.

Why does audience size matter so much?

If your audience is smaller than the required sample, the test cannot detect the effect you care about and any result is noise. The tool compares your available audience to the requirement and warns you when it is too small.

When should I read the results?

Wait 24 to 48 hours after sending so late opens are counted, then compare open rates with a two-proportion z-test. Reading results too early, or peeking repeatedly and stopping on a good number, inflates false positives.

What if neither variant wins?

A null result is still informative — it tells you the change you tested does not matter, so keep the simpler control and form a new hypothesis. Avoid the temptation to ship a variant that merely looks better without statistical support.

Push Notification A/B Test Plan Builder

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Test push copy with statistics, not vibes

Push notifications are high-frequency, high-annoyance, and easy to test — but most teams ship copy on a hunch and never size the test properly. This builder turns a hypothesis and two variants into a complete experiment plan, computes how many users each variant needs to detect your target lift, and tells you whether your audience is even large enough to run it.

How it works

You state a hypothesis, then enter the control and treatment notification copy (title and body for each), the target segment, the available audience, send time, and your current baseline open rate plus the minimum lift you want to detect. The tool computes the required sample size per variant with a two-proportion z-test at 95% significance and 80% power — the standard formula n = (z_alpha + z_beta)^2 * (p1(1-p1) + p2(1-p2)) / (p2 - p1)^2 — using a 50/50 split. It then compares that requirement to your audience and flags whether you can actually reach significance, and assembles the variants, metrics, and an analysis plan into copy-ready text.

Tips and example

Change one thing at a time — test personalization OR an emoji OR send time, not all three, so a win is attributable.
Always set an opt-out rate guardrail; a higher open rate that costs you subscribers is not a win.
If the tool says your audience is too small, accept a larger minimum detectable lift or broaden the segment.
Wait a day or two before reading results and never stop early on a lucky peek — that inflates false positives.