Template

Measurement framework template (OKRs, KPIs)

An interactive worksheet to design outcome-focused metrics, capture precise definitions, link metrics to experiments, and surface diagnostic measures and risks.

Interactive Tool

Measurement framework worksheet

Use this worksheet to turn a good intention into a usable metric. Capture a clear objective, define an unambiguous outcome metric and target, name supporting diagnostics, identify ownership and cadence, and link experiments or initiatives that will move the measure. Good metrics are precise, actionable, and tied to decisions — not vanity numbers.

Quick example: Objective: Improve customer retention. Outcome metric: Monthly active customers retention rate (rolling 30 days). Baseline: 72% (2026-06-01). Target: 78% by 2026-12-31. Supporting KPIs: 30-day churn by cohort, NPS, product engagement depth. Associated experiments: onboarding flow A/B test, targeted win-back campaign.

A brief, outcome-focused statement describing what you want to achieve. Keep it clear and specific (one sentence).
The primary metric that indicates success (e.g., 'Monthly Active Customers' or 'Net Revenue Retention').
Define numerator, denominator, filters, cohorts, and units. Be precise so the metric is unambiguous for any analyst or system pulling the data.
Choose the most appropriate data type for aggregation and interpretation.
Record the current measurement and the date (e.g., '12% as of 2026-06-01').
Specific target and deadline (e.g., '18% by 2026-12-31'). Targets should be realistic and time-bound.
Does higher mean better, lower mean better, or is there a target range?
Reporting window for the metric (e.g., 'monthly', 'rolling 30 days', 'quarterly average').
Diagnostic indicators that help explain movement in the outcome metric. Limit to the most useful 3-6 measures.
Short-term signals that typically predict the outcome (useful for early action).
Who is responsible for monitoring this metric, investigating changes, and driving experiments?
How often the metric is reviewed and decisions are made based on it.
Precise formula, any transformations, time windows, cohorting rules, and aggregation steps so the number can be reproduced reliably.
Systems, tables, or owners where the data lives and who maintains it.
How often the underlying data is refreshed and available for reporting.
Current or planned tests, projects, or changes intended to move the metric. Link experiments and expected impact.
Questions to ask when the metric moves unexpectedly (what to check first).
How the metric could be gamed or induce unwanted behavior and what controls or alternate measures you'll use.
Rate how confident you are that the metric is correctly specified, available, and useful for decision-making.
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