The growth experimentation terms that matter

Plain-English definitions, each with an original take and links to go deeper.

• ICE Scoring: ICE scoring is a prioritization method that ranks growth experiments by three factors: Impact (how much it could move the metric), Confidence (how sure you are it will work), and Ease (how little effort it takes). You score each from 1 to 10, combine them, and run the highest-scoring tests first.
• ROTI (Return on Time Invested): ROTI, or Return on Time Invested, is a review lens that weighs the learning and results an experiment produced against the time it consumed. Where ICE estimates value before a test, ROTI grades it afterward to decide whether that type of experiment is worth repeating.
• Growth Loop: A growth loop is a closed system where the output of one cycle becomes the input of the next, so growth compounds instead of relying on one-time pushes. A new user produces something, such as content, a referral, or data, that brings in the next user.
• North Star Metric: A North Star Metric is the single measure that best captures the core value your product delivers to customers, used to align an entire team on one outcome. The best ones track value delivered, like time spent listening or nights booked, not vanity counts like total signups.
• A/B Test: An A/B test is a controlled experiment that splits traffic between two versions, a control (A) and a variant (B), to measure which performs better on a chosen metric. Randomly assigning users isolates the change as the cause of any difference, so the result can be trusted.
• Experiment Hypothesis: An experiment hypothesis is a testable, falsifiable statement that predicts how a specific change will affect a specific metric, and why. A strong format reads: because we observed X, we believe change Y will cause metric move Z for audience A, and we will know we are right when result R.
• RICE Scoring: RICE scoring is a prioritization method that ranks ideas by four factors: Reach, Impact, Confidence, and Effort. You multiply Reach, Impact, and Confidence, then divide by Effort, so the score reflects total value per unit of work. The highest scores move to the top of the backlog.
• Cohort Analysis: Cohort analysis groups users by a shared starting point, usually the week or month they signed up, then tracks how each group behaves over time. It separates how new users behave from how older ones do, so you can see whether retention is genuinely improving rather than being masked by overall growth.