Marketing reporting data dictionary

Fill in each section below. Every section has a “Concept review” dropdown explaining what the section is for and why it exists; you can complete the whole page without opening one, or expand any of them for the reasoning first. Examples throughout use Doughnut Labs, an invented company that sells Doughnut Technology, so you can see one filled-in dictionary rather than scattered fragments. Replace the greyed E.g. lines with your own answers.

Fill in Setup, North star metric, and KPI tiers first, ideally with the people who tend to argue about numbers in the same room for 90 minutes. Attribution, source of truth, and ownership come next. Metric formulas can be added gradually, starting with your north star and tier 1 metrics. Everything is a living document: expect to update it as questions come up, and review it on a schedule rather than only when something breaks.

Settle what you measure

Fill in Setup, the north star metric, and the KPI tiers. This is the part worth doing live, with whoever ends up arguing about numbers later, rather than as individual homework.

Settle how you measure it

Attribution, source of truth, and ownership. These sections are what keep two people from getting two different answers to the same question.

Write the formulas

One block per metric, starting with the north star and tier 1. The worked example with real numbers is where a broken definition reveals itself, so don’t skip it.

Link your flat file, dashboards, and reports so this page becomes the one place anyone can find any of them.

Keep it current

Log decisions and changes as they happen, and review the whole dictionary on a set schedule.

Setup

The basics that identify this dictionary and who’s accountable for it:

Field	Fill in
Organization or brand	E.g. Doughnut Labs
Business model	E.g. Ecommerce
Completed by	E.g. Priya Desai, Head of Growth
Date completed	E.g. 2026-02-03
Last reviewed	E.g. 2026-05-01
Next review	E.g. 2026-08-01

What this business gets paid for, in one sentence: E.g. Doughnut Labs sells a direct-to-consumer subscription box of small-batch doughnut kits.

Concept review: Setup

These fields exist so anyone opening the dictionary can place it in seconds: whose numbers these are, what kind of business they run, and when the definitions were last checked. The one-sentence business description matters more than it looks. A metric like “conversion” or “customer” means something different for a business selling one-time purchases than one selling subscriptions, and writing the model down up front keeps that distinction visible everywhere else in the document.

North star metric

The tiebreaker metric: the one you optimize for when two reasonable options conflict.

Field	Fill in
North star metric	E.g. Contribution margin after marketing
Plain-language definition	E.g. What’s left from sales after every cost that scales with an order, including advertising.
Why this one	E.g. Revenue alone can be bought at a loss; this can’t be improved by spending recklessly.
Counter-metric	E.g. New customer count
Who owns it	E.g. Finance lead

Common starting points, if you want a reference rather than a blank page:

Business type	Common north star	Counter-metric
Ecommerce	Contribution margin after marketing	New customer count
Subscription	Net new recurring revenue	Churn rate
Lead generation	Cost per closed-won customer	Pipeline volume
Marketplace	Completed transactions	Take rate

Concept review: North star metric

The north star is the tiebreaker, not the most important KPI on the list. A team can have several well-defined KPIs and still disagree constantly, because KPIs conflict with each other in ordinary ways: volume up and margin down, lead count up and lead quality down. The north star is the one number that settles which side wins. There can be many KPIs. There’s exactly one north star.A counter-metric belongs beside it always, in the same report, because any single metric can be improved by doing something that quietly damages the business elsewhere. The counter-metric doesn’t need its own target; it needs to be visible every time the north star is, so the tradeoff shows up in the routine report rather than in a postmortem months later. See reporting framework concepts for the four tests a good north star has to pass.

KPI tiers

Keep the total list under 15. A metric earns a place here only if a 20% move, in either direction, would change what you do.

Tier 1, outcomes

Three to five metrics the business is directly accountable for.

Metric	Definition in one line	Target	Floor	Owner
E.g. Contribution margin	E.g. Net revenue minus every cost that scales with an order	E.g. 24% of net revenue	E.g. 18%	E.g. Finance lead
E.g. New customer count	E.g. First-ever purchasers in the period	E.g. 2,400/month	E.g. 1,800/month	E.g. Growth lead
[add tier 1 metric]

Floor is the level that triggers an actual phone call rather than a note in a report.

Tier 2, drivers

Five to ten metrics: the levers that move tier 1.

Metric	Which tier 1 metric it moves	Owner
E.g. Marketing efficiency ratio	E.g. Contribution margin	E.g. Paid media lead
E.g. Blended customer acquisition cost	E.g. New customer count	E.g. Paid media lead
[add tier 2 metric]

Tier 3, diagnostics

Listed, not tabled. These explain why a tier 2 driver moved: cost per click by channel, add-to-cart rate, checkout abandonment, whatever your team checks first when something looks off. E.g. Cost per click by channel, add-to-cart rate, checkout abandonment rate [add diagnostic]

Deliberately not tracking

Write these down. It saves the same conversation from repeating every quarter.

Not tracking	Why not
E.g. Social media follower count	E.g. Doesn’t move with revenue or margin; vanity metric for this business
[add item]

Concept review: KPI tiers

A KPI is a metric that changes what you do. If it moved 20% and nobody would act differently, it’s context, not a KPI, and it belongs in tier 3 or off the list. Starting from decisions rather than from whatever a dashboard happens to surface is what keeps this list short and useful rather than long and decorative.The three tiers exist so a report can explain, not just state. When a tier 1 outcome moves, tier 2 says why; when a tier 2 driver moves, tier 3 says why that happened. A report built on this chain reads as cause and effect instead of a list of unrelated facts. See reporting framework concepts for how this plays out differently in a business with a long sales cycle, where tier 1 outcomes lag the activity that produced them.

Attribution

The rule for deciding which touch gets credit for a result. Every model is wrong differently; pick one, write down where it’s wrong, and apply it consistently.

Field	Fill in
Primary model	E.g. 7-day click, multi-touch
Applied to	E.g. All paid channels
Click window	E.g. 7 days
View window	E.g. Excluded
Lookback window	E.g. 7 days
Secondary model, if any	E.g. Marketing efficiency ratio (blended, unattributed)
Used for	E.g. Sanity check against the primary model
Do platform-reported numbers appear in reports?	E.g. No
If yes, how labeled	E.g. N/A

Where the model is known to be wrong, written down honestly:

Question	Answer
This model overcredits	E.g. Retargeting and branded search, since they’re often the last click before a purchase someone had already decided on
This model undercredits	E.g. Top-of-funnel video, which rarely gets the last click
Not measured at all	E.g. Podcast sponsorships and word of mouth

How the model gets checked:

Field	Fill in
Validation method	E.g. Quarterly geo holdout test on paid social
How often	E.g. Quarterly
Last validated	E.g. 2026-04-15
What we found	E.g. Model undercredited paid social by roughly 12% against the holdout

Concept review: Attribution

Add up what every ad platform reports and the total will exceed actual orders, because each platform claims every conversion it touched under its own window. This is normal, not a tracking bug, and it’s exactly why platform numbers can’t be summed and why the business needs one model of its own, applied the same way every time.Which model fits depends on how long the path to purchase runs, how much spend goes to channels that can’t be clicked (audio, connected TV, sponsorships), and how fast the team needs to act on the answer. See reporting framework concepts for how those three questions point toward different models, and why validating the model at least once a year, rather than just trusting it, is what keeps an attribution setup from quietly drifting out of line with reality.

Source of truth

One approved system per metric. When two systems disagree, the approved one wins.

Metric	Approved source	Exact field or report	Refresh	Who owns the pipeline
E.g. Total revenue	E.g. Shopify	E.g. Net sales	E.g. Daily	E.g. Data lead
E.g. Ad spend	E.g. Each ad platform	E.g. Amount spent	E.g. Daily	E.g. Paid media lead
E.g. Cost of goods	E.g. Finance export	E.g. Standard cost per SKU	E.g. Monthly	E.g. Finance lead
[add metric]

Name the exact field, not just the system. “Revenue from the ecommerce platform” is ambiguous; gross sales, net sales, and total sales are three different numbers in the same admin panel.

Expected discrepancies

Systems will disagree. Recording the normal gap turns a change in that gap into a signal instead of a monthly crisis.

Comparison	Normal gap	Why	Investigate when
E.g. Analytics revenue vs. platform revenue	E.g. 3 to 6%	E.g. Ad blockers, consent rejection	E.g. Gap exceeds 10%
Sum of platform conversions vs. actual orders	(usually higher, always)	Platforms claim shared conversions	Never; this gap is expected

Data lag

Field	Fill in
Earliest date a weekly report can cover through	E.g. 2 days before send
Earliest business day a monthly report can send	E.g. 4th business day
When finance closes	E.g. 10th business day
Are monthly reports sent before close?	E.g. Yes
If yes, how estimates are labeled	E.g. Cost of goods marked “estimated” until reconciled

Concept review: Source of truth

When two systems produce different numbers for the same metric, the useful question is “which one is approved,” not “which one seems more trustworthy right now.” Naming the source ahead of time turns that moment from a debate into a lookup.The most common mistake is treating an analytics platform as the source for revenue. Analytics tools miss sales lost to ad blockers or consent rejection and don’t know about refunds after the fact; revenue belongs to whichever system actually took the money. See reporting framework concepts for how to assign a source by which system owns the underlying event, and how a metric that spans several systems, like contribution margin, needs a join rule recorded alongside its source.

Ownership

No role can be blank. One person can hold several roles.

Role	Name	What they decide
Metric owner	E.g. Priya Desai	E.g. What a metric should measure
Data owner	E.g. Sam Okafor	E.g. Where the number comes from; fixes pipelines
Report owner, weekly	E.g. Marco Silva	E.g. Writes and sends the weekly report
Report owner, monthly	E.g. Priya Desai	E.g. Writes and sends the monthly report
Decision owner	E.g. Head of Growth	E.g. Acts on what the report says

Concept review: Ownership

Every metric needs one name attached, not a team. A metric with shared ownership tends to drift silently, since someone adjusts a filter, someone else changes a date range, and eventually two views of the same number disagree with no record of why. One owner doesn’t mean one person does all the work; it means there’s one person to ask when the definition needs a decision.A definition should only change when its owner approves the change, and the change gets logged with an effective date below in Part 4, so old reports don’t quietly stop matching new ones. See reporting framework concepts for what breaks when a definition changes silently instead.

Metric formulas

One block per tier 1 and tier 2 metric. Copy the block as many times as you need. Start with your north star and tier 1 metrics; you don’t need every metric defined before you start reporting.

METRIC: _________________________________________
TIER: ______  OWNER: ____________________________

PLAIN DEFINITION (one sentence, no jargon):
_________________________________________________

FORMULA:
_________________________________________________

INPUTS:
  Input              Source system        Exact field
  ________________   __________________   ____________
  ________________   __________________   ____________

INCLUDES: _______________________________________
EXCLUDES: _______________________________________

GRAIN (what one row represents): ________________
DATE ATTRIBUTED TO: _____________________________
ATTRIBUTION APPLIED: ____________________________

TARGET: __________  FLOOR: __________
NORMAL RANGE: ___________________________________

WORKED EXAMPLE (real numbers, one recent period):
_________________________________________________

MISLEADS WHEN: __________________________________
READ ALONGSIDE: _________________________________

A block filled in, for reference:

METRIC: Contribution margin after marketing
TIER: 1    OWNER: Finance lead

PLAIN DEFINITION:
What's left from sales after every cost that scales with an
order, including advertising.

FORMULA:
Net revenue − cost of goods − shipping − payment fees
− discounts − ad spend

INPUTS:
  Input           Source              Exact field
  Net revenue     Shopify             Net sales
  Cost of goods   Finance export      Standard cost per SKU
  Shipping        Fulfilment provider Actual cost per shipment
  Payment fees    Payment processor   Fee per transaction
  Discounts       Shopify             Discount applied
  Ad spend        Ad platforms        Amount spent

INCLUDES: all direct-to-consumer orders, all paid channel spend
EXCLUDES: wholesale orders, flagged test orders, salaries, software

GRAIN: one order, rolled up to week and channel
DATE ATTRIBUTED TO: order date, account time zone
ATTRIBUTION APPLIED: ad spend allocated by 7-day click, multi-touch

TARGET: 24% of net revenue    FLOOR: 18%
NORMAL RANGE: 21 to 27%

WORKED EXAMPLE (week of June 8, 2026):
  Net revenue      $103,000
  Cost of goods    $ 37,100
  Shipping         $  9,950
  Payment fees     $  2,975
  Discounts        $  5,600
  Ad spend         $ 23,150
  103,000 − 37,100 − 9,950 − 2,975 − 5,600 − 23,150 = $24,225
  Margin % = 24,225 / 103,000 = 23.5%

MISLEADS WHEN: standard rather than actual cost of goods delays
the effect of a supplier price change by a month. A shift in
product mix can move the percentage with nothing else changing.

READ ALONGSIDE: new customer count, which catches margin
improving because the business is quietly shrinking.

Calls worth making explicit, since this is where two people’s numbers usually diverge:

Question	Your answer
Test and internal orders	E.g. Excluded
Refunds	E.g. Deducted when refunded
Cancelled orders	E.g. Excluded
Tax	E.g. Excluded
Shipping revenue charged to customer	E.g. Included
Discounts	E.g. Net
Time zone	E.g. Account local time

Concept review: Metric formulas

The worked example is the part most templates skip and the part that matters most. Calculate it with real numbers before adopting a metric; if the answer doesn’t roughly match what people already expect, the definition has a problem, and it’s far cheaper to find that now than in a meeting where two people are holding different numbers.This block is also where the ambiguity in a metric name actually gets resolved. “Revenue” sounds like one number until you write down whether it includes tax, whether a refund gets deducted the day it happens or the day the original order shipped, and which time zone a date belongs to. None of those calls has a universally right answer; what matters is making the call once and writing it down, so it doesn’t get made differently by whoever happens to be pulling the number that week.

Where everything lives

Update this whenever you build something. This section is what makes the dictionary a portal instead of just a form.

The flat file

Field	Fill in
Location	E.g. Google Sheet: “Doughnut Labs Reporting Flat File”
Owner	E.g. Sam Okafor
Channels included	E.g. Meta, Google Search, Email, blended
Refresh schedule	E.g. Every Monday for the prior week
Restatement window	E.g. 14 days
Last validated	E.g. 2026-06-09

Metric names in the flat file must match the metric formulas above. Channel and source names must match Source of truth. See the flat file template for the schema.

Dashboards

Name	What question it answers	Audience	Link	Owner	Refresh
E.g. Live pacing dashboard	E.g. Are we on track to hit this month’s target?	E.g. Growth team	E.g. [link]	E.g. Sam Okafor	E.g. Daily
[add dashboard]

Reports

Report	Send day	Period covered	Audience	Owner	Template
Weekly	E.g. Every Tuesday	E.g. Prior Mon to Sun	E.g. Growth team	E.g. Marco Silva	Weekly report template
Monthly	E.g. 4th business day	E.g. Prior calendar month	E.g. Leadership	E.g. Priya Desai	Monthly report template
Quarterly	E.g. 2 weeks after quarter end	E.g. Prior quarter	E.g. Leadership, board	E.g. Priya Desai	Quarterly report template
Annual	E.g. Before next year’s planning	E.g. Prior year	E.g. Leadership, board	E.g. Priya Desai	Annual report template

Leave a row’s fields blank for a cadence you don’t yet produce. Most brands start with a dashboard plus a monthly report and add weekly once spend justifies it.

Report archive

Every sent report gets logged, by cadence, in the weekly, monthly, quarterly, and annual report archives.

Concept review: Where everything lives

A dictionary that only defines metrics, without saying where the flat file, dashboards, and reports actually live, leaves a reader with the definitions but not the destination. This section is what turns the dictionary into the front door for the whole reporting system: anyone with a question about a number can start here and reach the actual file, dashboard, or report without asking around for a link.Metric and channel names have to match exactly between this section, the formulas above, and the flat file itself. A metric that’s spelled one way in the dictionary and another way in the flat file is the same ambiguity the dictionary exists to prevent, just moved one level down.

Decisions and changes

Why we decided what we decided

One entry per significant choice. The “rejected because” field is what stops the same debate from reopening every few months.

DATE: ___________________________________________
DECISION: _______________________________________
CONTEXT: ________________________________________
OPTIONS CONSIDERED: _____________________________
WHY WE CHOSE THIS: ______________________________
REJECTED BECAUSE: _______________________________
DECIDED BY: _____________________________________
EFFECTIVE DATE: _________________________________
AFFECTS: ________________________________________

Filled in, for reference:

DATE: 2026-02-10
DECISION: Use a 7-day click, multi-touch model for all channel
numbers.
CONTEXT: Platform-reported conversions summed to well over our
actual order count.
OPTIONS CONSIDERED: Last touch, platform-reported, 7-day
multi-touch, media mix modeling.
WHY WE CHOSE THIS: Median first-touch-to-purchase is 4 days, so
a 7-day window covers most real paths.
REJECTED BECAUSE: Last touch overcredited branded search by
roughly 30% against a January holdout test. Mix modeling needs
more history than we have.
DECIDED BY: Head of Growth, with finance present.
EFFECTIVE DATE: 2026-03-01
AFFECTS: All channel-level conversion and revenue numbers.
Reports before this date used last touch.

Effective date matters. When a definition changes, every report before that date used the old one; without the date, old numbers look wrong instead of just different.

What changed and when

Anything that could change how a number should be read. Log it the day it happens, not at report time.

Date	What changed	Expected effect	Duration	Logged by
E.g. 2026-06-03	E.g. Summer sale, 20% off sitewide	E.g. Revenue up, margin down	E.g. 06-03 to 06-10	E.g. Marco Silva
[add change]

Log campaign launches, promotions, price changes, site deploys, outages, stockouts, tracking changes, definition changes, and competitor moves. Skip routine bid adjustments and normal creative rotation; log what would actually break a comparison, not everyday tuning.

Open questions

Anything unresolved. Each needs a name and a date, or it becomes permanent.

Question	Owner	Needed by	Blocking
[add question]

Concept review: Decisions and changes

The decision log exists because the same debate tends to resurface every few months if the reasoning behind a choice isn’t written down anywhere. Recording what was considered and why it was rejected is what lets a future conversation start from “we already looked at that” instead of relitigating it from zero.The change log answers a different, more immediate question: why did this number move on that date. Without it, an unusual spike or dip in a report becomes a small investigation every time, even when the cause was a known promotion or a tracking change logged weeks earlier and simply forgotten.

Sign-off

Everyone agrees these are the numbers, calculated this way, from these systems, until the decisions and changes section says otherwise.

Role	Name	Date
Marketing
Finance
Agency or analytics, if applicable

Reporting framework concepts The reasoning behind every section on this page.
Flat file template Where the raw numbers this dictionary defines actually get stored.
Ecommerce metrics starter pack and Sales-led metrics starter pack Pre-researched metric definitions if you’d rather start from a reference than a blank page.
Weekly, monthly, quarterly, and annual report templates The reports this dictionary’s definitions feed.

Brand guidelines

Naming conventions

Tracking & attribution

Creative standards

Reporting standards

Experiment standards

Customer profiles

Marketing campaigns

Marketing reporting data dictionary

Setup

North star metric

KPI tiers

Tier 1, outcomes

Tier 2, drivers

Tier 3, diagnostics

Deliberately not tracking

Attribution

Source of truth

Expected discrepancies

Data lag

Ownership

Metric formulas

Where everything lives

The flat file

Dashboards

Reports

Report archive

Decisions and changes

Why we decided what we decided

What changed and when

Open questions

Sign-off

​Setup

​North star metric

​KPI tiers

​Tier 1, outcomes

​Tier 2, drivers

​Tier 3, diagnostics

​Deliberately not tracking

​Attribution

​Source of truth

​Expected discrepancies

​Data lag

​Ownership

​Metric formulas

​Where everything lives

​The flat file

​Dashboards

​Reports

​Report archive

​Decisions and changes

​Why we decided what we decided

​What changed and when

​Open questions

​Sign-off

​Related resources

Setup

North star metric

KPI tiers

Tier 1, outcomes

Tier 2, drivers

Tier 3, diagnostics

Deliberately not tracking

Attribution

Source of truth

Expected discrepancies

Data lag

Ownership

Metric formulas

Where everything lives

The flat file

Dashboards

Reports

Report archive

Decisions and changes

Why we decided what we decided

What changed and when

Open questions

Sign-off

Related resources