Measurement

Marketing metric definition sheet

Marketing metric definition sheet is licensed CC BY 4.0. Attribution: Katafacts (katafacts.com).

1 · What it is

What it is

A marketing metric definition sheet is a real, checkable answer to 'what does this number actually mean, and who says so' for commercial metrics — one formula, one data source, one owner per metric name, the same discipline as a shop-floor metric definition sheet pointed at SQLs, cost per SQL, or channel attribution instead of production metrics. A dashboard built on an unvalidated default (like first-touch attribution nobody deliberately chose) looks just as authoritative as one built on a real, agreed formula — this sheet is where that difference actually gets named.

2 · When to use it

When to use it — and when not to

Use it when

Marketing and Sales have started reporting different numbers for a metric with the same name — an SQL count, a conversion rate — and nobody has actually reconciled why.
A visibility board or marketing KPI tree is live and you want every metric on it traceable to a real formula and a real owner, not an assumption inherited from the CRM's defaults.
You want to know honestly which commercial metrics — including hard ones like channel attribution — still have no agreed definition.

Not when

You don't actually know how a metric is computed today — go find out from RevOps or whoever owns the CRM report first; this sheet records real facts, it doesn't invent a formula that sounds plausible.
The metric is genuinely one-off or exploratory, not something two or more people will reference by name going forward — the overhead isn't worth it yet.

3 · How to fill it in

How to fill it in

Scope area: What part of the commercial function do these metric definitions cover?
Metrics: For each metric: the formula, data source, owner, refresh cadence, and unit you actually use today — your own facts, never guessed. Leave any of these blank if it's genuinely not agreed yet, including a model (like attribution) that's really just an unvalidated default.
Narrative: Drafted from the metrics actually defined, naming which ones still have a real gap.

4 · What good looks like

What good looks like

The example below continues the Beacon Analytics storyline — SQL and cost per SQL are both checked and confirmed against the marketing KPI tree's own assumptions, and channel attribution honestly names the CRM's first-touch default as unvalidated rather than letting it pass as a real model.

Same example, as a downloadable xlsx workbook.

Download .xlsx

Metric definition sheet

Beacon Analytics — marketing metric definitions

Marketing and RevOps — pipeline generation metrics

Priya Anand, RevOps · 2026-05-01

Scope area

Marketing and RevOps — pipeline generation metrics

Metrics

No agreed formula: Channel attribution (which channel sourced a given SQL) · No owner: Channel attribution (which channel sourced a given SQL)

Sales Qualified Lead (SQL)

A lead Sales has formally accepted as ready to work — cleared qualification standard work's bar (firmographic fit, a real trigger, anti-ICP disqualifiers cleared, a named path to budget authority) and been assigned to a rep.

Formula: Count of leads with crm_status = 'SQL' as of the stage-entry timestamp, not leads a rep has merely contacted

Source: CRM stage field — the same source the marketing KPI tree's MQL-to-SQL conversion rate already pulls from · Owner: Renee Okafor, VP Sales

Known limitations: A lead can bounce back from SQL to MQL if a rep disqualifies it after acceptance — this is a snapshot count, not a one-way funnel stage, so summing daily counts over a period will overstate true unique SQLs.

Cost per SQL

Blended marketing spend divided by the number of SQLs it's credited with producing in the same period.

Formula: (Total paid + organic marketing spend for the period) ÷ (SQL count for the same period)

Source: Ad-platform spend reports reconciled against the CRM's SQL count · Owner: Priya Anand, RevOps

Known limitations: Blended across every channel — this number alone can't say which specific channel is actually efficient, only that overall spend-to-SQL ratio. See channel attribution below for why that split isn't reliable yet.

Channel attribution (which channel sourced a given SQL)

No formula · no owner

Which marketing channel gets credit for sourcing a given SQL. Currently assigned by whichever channel's UTM tag is present on the lead's very first form fill — a first-touch model nobody at Beacon Analytics deliberately chose; it's just what the CRM defaults to.

Formula: Not yet agreed

Source: CRM first-touch UTM field (the default, not a validated model) · Owner: Unowned

Known limitations: First-touch attribution is almost certainly wrong for deals with a real sales cycle — a buyer's first form fill is rarely the channel that actually convinced them. No one owns validating or replacing this model yet. This is exactly the gap the marketing KPI tree's own third goal and the marketing go-see protocol's fourth step both already flagged without a driver metric or key point — and exactly what the discipline's still-unbuilt attribution reality check exists to close.

Narrative

SQL and cost per SQL are both fully specified — the same two numbers the marketing KPI tree already assumed were solid, now checked and confirmed with a real formula, source, and owner rather than left as an assumption. Channel attribution is the honest exception: it has a data source (the CRM's first-touch UTM field) but no agreed formula and no owner, because nobody at Beacon Analytics actually chose first-touch as the right model — it's just the CRM's default. This is the same gap the marketing KPI tree's third goal and the marketing go-see protocol's fourth step both already surfaced without a driver metric or key point to fill it. Naming it formally here doesn't resolve it — that's what a real attribution model would do — but it does stop the current first-touch number from quietly being treated as validated when it isn't.

5 · Common mistakes

Common mistakes

Treating a CRM's default field (like first-touch attribution) as though it were a deliberately chosen model.
A default is not a decision — naming it as unvalidated is what stops a budget conversation from quietly trusting a number nobody actually chose.
Leaving a metric unowned because 'marketing and sales both kind of own it.'
A definition nobody owns is a definition everyone quietly forks — a real owner is what keeps the formula from silently drifting as campaigns and systems change.
Resolving a genuine Marketing-vs-Sales conflict unilaterally just to make the sheet look complete.
Picking a winner without the other team's buy-in doesn't resolve the conflict, it just hides it — naming the conflict plainly is what actually gets it fixed.

6 · What it connects to

What it connects to

upstream

Marketing KPI tree
A driver metric named there — like channel attribution, left without one — is exactly what this sheet formally names as a gap, even when it can't resolve it.
Marketing go-see protocol
A go-see walk verifies a metric is holding at the source system; this sheet is what defines what 'holding' actually means for that metric in the first place.

downstream

Marketing attribution reality check
This sheet names channel attribution as an unowned, unvalidated default; the reality check is where that default actually gets pressure-tested against two other honest models.

7 · Where AI helps

Where AI helps

Judgement — stays yours

Deciding whether a metric's formula is actually well-specified or just sounds precise
Deciding which unresolved gap — including a hard one like attribution — to prioritize closing first

Analysis — AI helps

Tightening raw definition and known-limitations notes into specific, scannable language
Drafting the narrative naming which metrics are fully specified and which still have a real gap

Drudgery — automated

Identifying which metrics are missing a formula or an owner
Exporting to xlsx in the house format

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