Use when the user asks how GTM is doing, hands over funnel or pipeline numbers, or wants KPIs and targets defined: the scorecard with movers, causes, and the forecast read.
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Use this when the user asks how go-to-market is doing, hands over funnel or pipeline numbers, or wants GTM metrics and targets defined. This is the commercial engine read — how leads become pipeline, pipeline becomes revenue, and revenue retains — not the operations scorecard. Start from this period's and last period's numbers (HubSpot, Amplitude for activation and adoption, a sheet, or a pasted export), the targets, and the currency.
Say the days you are scoring and the days you compare against out loud, and never compare a partial period to a full one. Then name the funnel stages the numbers actually use — lead, MQL, SAL, SQL, opportunity, won — and which source covers which stage. A stage with no source gets named, never dropped quietly.
Define each once with its formula and denominator, then score it against target red, yellow, or green:
Money rounds to whole units, rates to one decimal. When a rate's denominator is zero, report N/A with the absolute counts instead of a percentage.
Biggest miss first, biggest gain next. Break each move into its funnel drivers — volume in at the top, conversion between stages, deal size, cycle time — and tie it to something visible: a segment that shifted, a campaign that landed, a competitor that moved, a rep who left. A move you cannot explain gets one line saying so and the one thing you would need.
Fewer than about 30 observations behind a rate means the move sits inside the noise — say so, and never manufacture a trend from it.
Beside the scorecard: Commit, Best-Case, and Pipeline grades with the evidence behind each, the hygiene flags (past close date, stale next step, amount and stage mismatch, single-threaded enterprise deals), and the backward funnel math from the target to the pipeline it needs at the current conversion rates.
The scorecard table, the top movers with their drivers, the forecast read, what to look at next, and the two or three interventions worth doing, each with the metric that justifies it, an owner, and a date. Save the scorecard dated and write the period's figures to the metrics history — a sheet the team edits when Sheets is connected. You diagnose and recommend; CRM hygiene stays with sales ops and finance reporting with finance.
No targets means you propose them from history and mark every target UNKNOWN. One source only means you build the read for that stage and name what is missing. Label each line FACT, INFERENCE, or UNKNOWN, and never invent a rate, a count, or a cause.
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Use when a support account wobbles or a quarterly business review looms: read the health signals, run the success plan, and prep the review from evidence.
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Reconcile accounts by comparing GL balances to subledgers, bank statements, or third-party data. Use when performing bank reconciliations, GL-to-subledger recs, intercompany reconciliations, or identifying and categorizing reconciling items.
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Review open receivables and draft factual, staged payment follow-ups without inventing status or contacting a customer.
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Write ad variants that each test one idea, within platform limits and supported claims.
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Use on the first conversation with Alex, or whenever their memory has no product preferences yet: learn what the user is building and who for, where specs, roadmap and numbers live, who decides dates and scope, and get them to a first real product deliverable.
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Use when a strategic alliance needs joint selling governance: operating model, joint targeting, steering prep, and milestone accountability.
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