The Metric Definition Auditor: Find Out Why Two Teams Report Different Numbers
Why this prompt matters
Without a canonical definition, every review becomes an argument about whose spreadsheet is correct, and decisions get made on whichever number arrived first. Teams that find a definition gap after a board deck has gone out spend quarters rebuilding trust in the metric.
What we use it for
Your product team reports 48,200 active customers for March and your finance team reports 31,900. The quarterly review is on Thursday, and you need to know which number is wrong, or whether both are right under different definitions, before anyone presents either one.
Prompt
Act as a senior analytics lead who audits metric definitions before numbers reach executives. Context: I am [YOUR ROLE] at [YOUR COMPANY NAME]. Two or more teams report a metric called [METRIC NAME], and their numbers do not match. Their definitions, queries or dashboard notes are below, followed by the numbers each team reported for the same period: [PASTE DEFINITIONS, QUERIES OR DASHBOARD NOTES HERE] [PASTE THE REPORTED NUMBERS HERE]. Task: (1) Restate each team's definition as an explicit rule covering five dimensions: population (who is counted), event (what counts as an action), time window, deduplication rule, and attribution rule. (2) List every point where the definitions diverge, and estimate how much of the gap between the reported numbers each divergence explains. (3) Recommend one canonical definition that serves [PRIMARY DECISION THIS METRIC DRIVES], and state directionally which team's number will change and why. Constraints: Use only information in my input. Label every estimate as an estimate and state the assumption behind it. Do not favour a team because of seniority or because its number is larger. Write the canonical definition as one sentence a non-analyst could read. If a divergence cannot be resolved from the input, put it under Open Questions instead of guessing. Output Format: (a) a table with columns Team, Population, Event, Window, Deduplication, Attribution; (b) a ranked list of divergences with estimated contribution to the gap; (c) the canonical definition as a single bold sentence; (d) Open Questions as a bullet list.
Result
Metric audit: Active Customers (March) Scenario: Product reports 48,200. Finance reports 31,900. Gap: 16,300.
Definitions | Team | Population | Event | Window | Deduplication | Attribution | |---|---|---|---|---|---| | Product | All registered users | Any login | Last 30 days | Per user | User | | Finance | Accounts with a paid subscription | Active paid status on report date | Point-in-time | Per account | Account |
Divergences, ranked by estimated contribution to the gap 1. Population (about 14,800 of the 16,300 gap). Product counts free-tier users who logged in during the window. Finance excludes them. Estimate assumes most free-tier logins in Product's count belong to non-paying accounts. 2. Deduplication and attribution (about 1,500). Product counts each login from a 12-seat account as a separate user. Finance counts the account once. Estimate assumes roughly 1,500 of the gap comes from multi-seat accounts. 3. Window (not quantified). A 30-day login window captures users who lapsed during the month. A point-in-time status does not. This pushes the two numbers in opposite directions and cannot be sized from the input.
Canonical definition **Active Customers is the number of accounts with an active paid subscription on the last day of the reporting month.**
Effect on each team Product's number falls sharply and should be reported under its own name, such as Monthly Active Users. Finance's number does not change.
Open Questions - Does Finance count trial accounts as paying? The input does not say. - Does Product's login count include internal staff or support logins? - Does the 30-day window end at UTC midnight or at each user's local time?
Two teams report the same metric and get different numbers. This prompt makes each team's definition explicit, finds the divergence that explains the gap, and proposes one canonical definition you can take into a review.
When to use it
Your product team reports 48,200 active customers for March and your finance team reports 31,900. The quarterly review is on Thursday. You need to know which number is wrong, or whether both are right under different definitions, before anyone presents either one.
Why it matters
Without a canonical definition, every review becomes an argument about whose spreadsheet is correct, and decisions get made on whichever number arrived first. Teams that find a definition gap after a board deck has gone out spend quarters rebuilding trust in the metric.
Why the prompt is built this way
The role sets an analyst who audits definitions rather than defending a team. The context requires your actual definitions and numbers, so the model works from evidence rather than from general knowledge of what 'active' might mean. The five dimensions (population, event, window, deduplication, attribution) are where metric definitions almost always diverge, and naming them keeps the analysis from collapsing into a vague disagreement. The constraints block guards against the most common failure in this kind of task, which is confident numbers that were never in the input. The Open Questions section gives the model an honest place to put what it cannot resolve.
How to adapt it
- Replace the bracketed fields with the real definitions from each team's dashboard or query, not summaries of them.
- Add the reported numbers for at least two periods. A gap that stays constant points to a different cause than one that moves.
- After the first run, ask a follow-up: re-rank the divergences assuming trial accounts are included in Finance's count. This shows how sensitive the conclusion is to one assumption.
A good sign the audit worked is that the canonical definition fits on one line and the team whose number changes can still accept it. If it does not fit, the definition is probably still ambiguous.