Framework · Measurement

Using benchmarks honestly

Benchmarks orient; they are not targets. They appear only as bands (below median, median, top quartile, top decile) with source, date, confidence and a definition-match flag. When definitions don't match, the product is compared with its own best cohort instead. Practitioner "good / great" labels map to median / top quartile.

ValueDiscoverStartReturnStayPaySpread

Plain label: benchmark bands with definitionsAlso known as: benchmarks, bands, definition match, reachable band

The best baseline is the product's own history; outside numbers count only when the metric definition matches.
Benchmark confidence: A primary source, B reputable dataset with a stated sample, C secondary or anecdotal.
Definitions cause most disagreements: Amplitude's day-7 rate counts every tracked visitor and sits far below sign-up-based rates.
The reachable band, in order: the product's own best cohort in the last 12 weeks (n of at least 30), then a matching benchmark median, then an archetype default labeled provisional.
Benchmarks are refreshed yearly; an older one only warns.

Keep reading

Framework · Measurement
Activation moments and the aha threshold
setup, aha and habit moments
Work backward from the habit moment to the aha moment (first core value) to the setup moment (what must happen first), per account for team products. Find the product's own threshold by comparing retained and churned users across early actions, weighing coverage and precision, then confirm it with a randomized onboarding change.
Framework · MeasurementEvidence grade A. Robust evidence: replicated, peer-reviewed research.
NPS and better-targeted survey metrics
satisfaction and effort surveys
NPS is qualified, not discredited: it predicts growth about as well as other satisfaction measures, but it throws away information, surveys only current customers and is not diagnostic. Pair any attitude score with behavior, such as actual referrals, and use targeted instruments: effort score, SUS, single-ease and worth-your-time questions.
Framework · MeasurementEvidence grade A. Robust evidence: replicated, peer-reviewed research.
Experiment hygiene: trust checks before reading results
trustworthy A/B testing checklist
Before trusting a result: check the sample ratio, run A/A tests, treat surprising wins as likely bugs, never stop a fixed-horizon test at the first p < 0.05, and run at least one full week (ideally two) to cover weekly cycles and novelty or change-aversion effects. Pre-register the metric, the horizon and the decision rule.
Framework · Measurement
Experiment win rates and the winner's curse
how often tests really win
In mature organizations about 1 in 10 experiments wins, and at that base rate a significant result has roughly a 22% chance of being a false positive. At low power any significant result overstates the true effect. Replicate surprising wins, report intervals and show a shrunk estimate next to the raw one.
Framework · Measurement
Growth accounting and the user state model
where active users come from and go
Split active users into new, retained, resurrected and churned each period; the user quick ratio, (new + resurrected) / churned, must exceed 1 to grow. Duolingo's state model classifies every user daily and showed that lifting current-user retention (CURR) moved daily actives most. It is the tool for splitting a decline by phase.
Framework · MeasurementEvidence grade A. Robust evidence: replicated, peer-reviewed research.
Measuring habit strength (SRHI and SRBAI)
self-reported automaticity
Frequency is not habit. The four-item SRBAI measures automaticity directly (done automatically, without having to remember, without thinking, started before realizing) and correlates about .92 with the full 12-item SRHI. Survey users who did the behavior at days 7, 30 and 90, and pair it with unprompted share.

Sources

The 7% retention rule (2025 Product Benchmark Report) · Amplitude, 2025
Benchmark confidence B. A reputable dataset.
What is good retention · Lenny's Newsletter (Lenny Rachitsky and Casey Winters), 2020
Benchmark confidence B. A reputable dataset.

Book content is paraphrased, never quoted at length. Last verified October 24, 2026.

Your product

Find the stage your product is stuck in

Gamify It reads your product, picks the one move that matters at its stage, and sizes the test to your traffic. Connecting is free; a Pro subscription starts it.

Add Gamify It to your assistant Take the 2-minute check-up

Related

Activation moments and the aha threshold

NPS and better-targeted survey metrics

Experiment hygiene: trust checks before reading results

Experiment win rates and the winner's curse

Growth accounting and the user state model

Measuring habit strength (SRHI and SRBAI)

Sources

Find the stage your product is stuck in