Marketing Measurement Plan Template (Attribution, MMM, Incrementality)

Decisions, KPI tree, metric definitions, data sources, attribution, incrementality experiments with worked readout, MMM, UTM convention, privacy, decision rules

MarketingVollständigCommon practice; privacy checks cite ePrivacy Art. 5(3), UK PECR, CCPA (GPC)measurement planAktualisiert 29.10.2026

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Anleitung: Use it when marketing and finance need one agreed way to judge spend. Fill the decisions and the KPI tree first, then plan experiments before trusting platform conversions. The main pitfall is treating platform-reported conversions as incremental.

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Marketing Measurement Plan: [Organisation or product], [Period]

The agreed way a marketing team measures what its spend and work achieve: the KPI tree from business outcome down to channel metrics, metric definitions, the data sources, how credit is assigned (attribution), how true causal lift is tested (incrementality experiments), how budgets are modelled across channels (marketing mix modelling), the UTM naming convention, privacy and consent rules, reporting and the decisions each number drives. Based on common marketing analytics practice, with privacy checks that cite EU, UK and US rules as dated examples. Replace every [bracketed] value and every row marked Example. This is a documentation format, not legal advice: check the current official text and ask a qualified professional.

Document control

Field	Value
Document ID	[MMP-YYYY-01]
Organisation or product	[Name]
Planning period	[YYYY or YYYY-H1]
Version	[0.1]
Status	[Draft / Approved / Superseded]
Owner	[@marketing-analytics-lead]
Approvers	[@cmo], [@finance-partner], [@privacy-lead]
Reviewers	[@growth-lead], [@data-engineering]
Last updated	YYYY-MM-DD
Next review	YYYY-MM-DD
Classification	[Internal]
Related plans	`marketing/marketing-plan`, `data/analytics-tracking-plan`

Approvals

Role	Name	Decision	Date
Plan owner	[@owner]	[Approved]	YYYY-MM-DD
Finance partner (budget and ROI method)	[@finance]	[Approved / Changes]	YYYY-MM-DD
Privacy lead (section 9)	[@privacy]	[Approved / Changes]	YYYY-MM-DD

Revision history

Version	Date	Author	Change
0.1	YYYY-MM-DD	[@owner]	First draft
[0.2] (Example)	YYYY-MM-DD	[@owner]	Added geo holdout EXP-02 and changed MQL definition (see CHG-001)

Decisions this plan supports
KPI tree
Metric definitions
Data sources and ownership
Measurement methods and how they fit
Incrementality experiments
Marketing mix modelling
Attribution and UTM conventions
Privacy, consent and data minimisation
Reporting cadence and audiences
Decision rules and guardrails
Change control and data quality
Glossary and references

1. Decisions this plan supports

Decision ID	Decision	Owner	Frequency	Evidence used	Method (section 5)
DEC-01 (Example)	Split next year's paid budget across channels	[@cmo] with [@finance]	Annual, revisited each quarter	Channel ROI and saturation curves	MMM, calibrated by experiments
DEC-02 (Example)	Pause, keep or scale a campaign in flight	[@growth-lead]	Weekly	Cost per qualified pipeline, trend against target	Attribution, with incrementality factor
DEC-03 (Example)	Decide whether a channel adds sales we would not otherwise get	[@growth-lead]	Twice a year per major channel	Lift and incremental cost per acquisition	Holdout or geo experiment
DEC-04 (Example)	Choose between two landing page or email variants	[@lifecycle]	Per test	Conversion rate difference with confidence interval	A/B test (`product/ab-test-plan`)
[DEC-05]	[decision]	[@owner]	[frequency]	[evidence]	[method]

Out of scope: [Example: sales rep performance measurement, which is covered by the sales operations plan; product analytics for retention, covered by data/analytics-tracking-plan].

2. KPI tree

The top of the tree is a business outcome finance already reports. Each level below explains the level above as a product or sum, so a change at the top can be traced to a cause lower down.

KPI ID	KPI	Level	Owner	Target for period	Linked decision
KPI-01 (Example)	Marketing-sourced new ARR	Outcome	[@cmo]	$2.4M	DEC-01
KPI-02 (Example)	Sales-qualified opportunities (SQOs) from marketing	Pipeline	[@demand-gen]	480	DEC-01, DEC-02
KPI-03 (Example)	Cost per SQO, paid channels	Efficiency	[@growth-lead]	Under $1,100	DEC-02
KPI-04 (Example)	Incremental cost per acquisition, paid social	Causal efficiency	[@growth-lead]	Under $180 per incremental demo request	DEC-03
KPI-05 (Example)	Branded search volume	Brand health	[@brand]	Up 15% on prior year	DEC-01
[KPI-06]	[KPI]	[level]	[@owner]	[target]	[DEC-ID]

Keep the tree to roughly ten nodes. If the team needs more, the extra metrics are diagnostics: they live on a dashboard tab, not in targets.

3. Metric definitions

Metric	Formula	Includes	Excludes	Window	Source of truth
MQL (Example)	Count of contacts reaching lead score 60 or a hand-raise action	Demo requests, pricing contact forms, score 60 or above	Students, competitors, existing customers, personal email domains	Calendar month of qualification	CRM
SQO (Example)	Count of opportunities accepted by sales at stage 1 with a sourced-by-marketing flag	First-touch or self-reported marketing source	Partner-sourced, expansion deals	Month of acceptance	CRM
Cost per SQO (Example)	Paid media spend / SQOs from paid channels	Media spend, agency fees on media	Salaries, tooling	Month, with 60-day lag view	Finance ledger, CRM
Incremental CPA (Example)	Test spend / incremental conversions from a controlled experiment	Conversions above the control group rate	Conversions the control would also have made	Test period	Experiment readout
Pipeline velocity (Example)	(SQOs x win rate x average contract value) / sales cycle days	Marketing-sourced SQOs	Expansion	Rolling 90 days	CRM
[metric]	[formula]	[includes]	[excludes]	[window]	[source]

Lag matters in business-to-business sales. Example: Larkspan's median time from first click to closed deal is 74 days, so a month's spend is judged on SQOs at 60 days and on ARR at 120 days, never on the same month's revenue.

4. Data sources and ownership

Source ID	System	Data	Owner	Freshness	Join key	Notes
SRC-01 (Example)	CRM	Leads, opportunities, revenue	[@revops]	Hourly	Opportunity ID, account ID	Source of truth for pipeline and ARR
SRC-02 (Example)	Ad platforms (search, social)	Spend, impressions, clicks, platform conversions	[@growth]	Daily	Campaign ID via UTM	Platform conversions are not incremental
SRC-03 (Example)	Web analytics	Sessions, landing pages, form starts	[@analytics]	Daily	Session UTM	Consent-dependent coverage (section 9)
SRC-04 (Example)	Finance ledger	Invoiced media and agency costs	[@finance]	Monthly close	Cost centre, campaign code	Source of truth for spend
SRC-05 (Example)	Self-reported attribution field	"How did you hear about us?" on demo form	[@demand-gen]	Real time	Lead ID	Captures podcasts, word of mouth, communities
[SRC-06]	[system]	[data]	[@owner]	[freshness]	[key]	[notes]

Event naming, properties and consent flags for the website follow data/analytics-tracking-plan. Shared business terms (MQL, SQO, ARR) are recorded once in the data team's data/business-glossary.

5. Measurement methods and how they fit

Method	Question it answers	Speed	Strength	Weakness	Used for
Attribution (rules-based or data-driven)	Which touchpoints were on the path to conversion?	Daily	Granular, fast, campaign-level	Correlational; misses offline and non-clickable media; reduced by consent gaps	DEC-02
Incrementality experiment (holdout, geo, A/B)	How many conversions did this cause?	Weeks per test	Causal; the ground truth for calibration	One channel at a time; needs volume; costs some sales in the holdout	DEC-03, DEC-04
Marketing mix modelling (MMM)	How should the total budget be split across channels?	Quarterly refresh	Covers all channels and offline factors; no user-level data needed	Needs two or more years of weekly data; wide uncertainty; correlational unless calibrated	DEC-01
Self-reported attribution	Where do buyers say they heard of us?	Real time	Captures dark social and word of mouth	Recall bias; free text needs coding	Sense-check for all three

How they fit, in one loop: experiments measure true lift for a channel, the lift calibrates both the attribution model (as an incrementality factor) and the MMM (as a prior or a constraint), and the calibrated MMM sets next quarter's budget, which attribution then steers week by week.

6. Incrementality experiments

6.1 Experiment register

Exp ID	Channel	Hypothesis	Design	Primary metric	Duration	Decision rule	Status
EXP-01 (Example)	Paid social prospecting	Paid social causes demo requests beyond those that would happen without it	Audience holdout: 10% of the eligible audience randomly excluded from ads	Demo request rate	6 weeks	Keep spend if incremental CPA is under $180 and the 95% interval excludes zero lift	Complete
EXP-02 (Example)	Branded search	Branded search ads add clicks beyond organic results	Geo test: ads off in matched regions	Demo requests per region	4 weeks	Cut branded bids by half if incremental demos are under 10% of paid clicks	Planned
[EXP-03]	[channel]	[hypothesis]	[design]	[metric]	[weeks]	[rule]	[status]

6.2 Worked readout: EXP-01 (Example)

Group	Audience	Demo requests	Rate
Exposed (treatment)	180,000	2,160	1.20%
Holdout (control)	20,000	200	1.00%

Absolute lift: 1.20% minus 1.00% = 0.20 percentage points.
Relative lift: 0.20 / 1.00 = 20%.
Incremental demo requests: 0.20% x 180,000 = 360.
Uncertainty: the 95% confidence interval for the absolute lift is about 0.05 to 0.35 percentage points (unpooled standard error, z about 2.67), so incremental demos lie between about 96 and 624.
Spend in the test window: $54,000, so incremental CPA is$ 54,000 / 360 = ** $150**, inside the$ 180 rule. Decision: keep spend.
Platform comparison: the ad platform reported 900 conversions, a platform CPA of $60. The incrementality factor is 360 / 900 = 0.40: for steering in section 8, multiply platform-reported paid social conversions by 0.40 until the next test.

Design checks before launch:

Randomisation unit fixed (user, account or region) and the holdout cannot see the ads through other campaigns.
Minimum detectable effect and sample size computed from the baseline rate.
No other major change (pricing, launch) planned for the test window, or it affects both groups equally.
Primary metric and decision rule frozen in this register before launch.
Cost of the holdout (lost sales) agreed with the budget owner.

7. Marketing mix modelling

Item	Choice
Outcome variable	[Example: weekly marketing-sourced SQOs, also run on new ARR]
Granularity and history	[Example: weekly, 156 weeks (three years)]
Channels modelled	[Example: paid search, paid social, display, email, events, organic as baseline]
Control variables	[Example: seasonality, price change in week 88, competitor launch, sales headcount]
Carry-over and saturation	[Example: geometric adstock per channel; Hill saturation curve]
Calibration	[Example: EXP-01 lift used as a prior for paid social]
Tool	[Example: open-source Bayesian MMM library, version pinned in the repository]
Refresh	[Example: quarterly, before DEC-01 review]
Validation	[Example: holdout of the last 12 weeks, error reported; coefficients sense-checked against experiments]

Channel results (Example, last 12 months):

Channel	Spend	Modelled incremental revenue	ROI (revenue / spend)	Read
Paid search	$120,000	$300,000	2.5	Near saturation above $12,000 per week
Paid social	$80,000	$136,000	1.7	Room to grow; consistent with EXP-01
Display	$40,000	$36,000	0.9	Returns less than spend; test before cutting
Email	$10,000	$65,000	6.5	Limited by list size, not budget
Total	$250,000	$537,000	2.1

Report each ROI with its interval from the model, and act on a difference only when intervals do not overlap or an experiment confirms it. Marginal ROI (the return on the next dollar) guides reallocation, not average ROI: paid search has the higher average but is near saturation, so the next dollar may earn more in paid social.

8. Attribution and UTM conventions

8.1 Attribution model

Model used for steering: [Example: position-based (40% first touch, 40% lead creation touch, 20% spread across the middle) for pipeline, last non-direct click for web conversion rate tests].
Lookback window: [Example: 90 days before lead creation].
Adjustments: [Example: paid social conversions multiplied by the EXP-01 incrementality factor of 0.40 in the weekly report].
Known gaps: [Example: users who decline analytics cookies are not in path data; podcast and community influence is only visible in self-reported attribution].

8.2 UTM convention

Parameter	Rule	Allowed values (Example)
`utm_source`	Platform or publisher, lower case	`google`, `linkedin`, `newsletter`, `partner-name`
`utm_medium`	Channel type from a fixed list	`cpc`, `paid-social`, `email`, `display`, `referral`, `event`
`utm_campaign`	Campaign code from the campaign register: year, quarter, short name	`2026q4-ap-automation-launch`
`utm_content`	Creative or link variant	`video-a`, `cta-footer`
`utm_term`	Paid search keyword group only	`ap-automation`

Rules: lower case only, hyphens not spaces, no personal data (never an email address or user ID in a URL), one campaign code per campaign brief (marketing/campaign-brief), and a link builder or spreadsheet that validates values against the lists above.

Example link (Example): https://example.com/demo?utm_source=linkedin&utm_medium=paid-social&utm_campaign=2026q4-ap-automation-launch&utm_content=video-a

9. Privacy, consent and data minimisation

Question	Example rules to check (as of October 2026)	Answer
Do analytics or ad tags store or read information on the device before the visitor consents?	EU Directive 2002/58/EC Article 5(3); UK PECR regulation 6 (ICO guidance; the Data (Use and Access) Act 2025 amends PECR, so check which changes are in force)	[answer]
Do we honour browser opt-out preference signals such as Global Privacy Control for sale or sharing of personal information?	California CCPA and its regulations (the California Attorney General states GPC must be honoured as a valid opt-out request)	[answer]
Is the lawful basis for each processing purpose recorded, including uploading customer lists to ad platforms?	EU and UK GDPR Article 6; record in `compliance/gdpr-ropa`	[answer]
Do measurement reports use aggregated data wherever user-level data is not needed?	Data minimisation, GDPR Article 5(1)(c)	[answer]
How long are raw event and click-level data kept?	Retention schedule in `data/data-retention-deletion-standard`	[answer]

Design for consent gaps: [Example: report the consent rate weekly, present web conversion metrics as "consented sessions only", and rely on CRM outcomes, experiments and MMM, which do not need cookie-level data, for budget decisions].

Last checked: [YYYY-MM-DD], by [@privacy-lead].

10. Reporting cadence and audiences

Report	Audience	Cadence	Contents	Feeds decision	Owner
Weekly channel pulse (Example)	Growth team	Weekly	Spend, cost per SQO, factor-adjusted conversions, anomalies	DEC-02	[@growth-analyst]
Monthly marketing review (Example)	CMO, sales leadership	Monthly	KPI tree status, pipeline at 60 days, experiment results	DEC-02, DEC-03	[@analytics-lead]
Quarterly budget review (Example)	CMO, finance	Quarterly	MMM refresh, ROI intervals, reallocation proposal	DEC-01	[@analytics-lead]
[report]	[audience]	[cadence]	[contents]	[DEC-ID]	[@owner]

Dashboard specifications follow data/dashboard-specification.

11. Decision rules and guardrails

Rule ID	Trigger	Action	Approver
RULE-01 (Example)	Cost per SQO above target for 3 consecutive weeks on a channel	Cut that channel's weekly budget by 20% and review creative	[@growth-lead]
RULE-02 (Example)	Experiment shows incremental CPA above the threshold with interval excluding the threshold	Reduce channel to maintenance spend; retest in 6 months	[@cmo]
RULE-03 (Example)	MMM marginal ROI for a channel exceeds another by more than its interval width	Move up to 10% of budget per quarter toward it	[@cmo], [@finance]
RULE-04 (Example)	Consent rate drops more than 10 points week on week	Check the consent banner and tags before reading web metrics	[@analytics-lead]
[RULE-05]	[trigger]	[action]	[@approver]

Guardrail metrics that must not get worse while optimising for the target: [Example: SQO-to-win rate, unsubscribe rate, brand search volume].

12. Change control and data quality

Change ID	Date	Change	Reason	Effect on history	Approved by
CHG-001 (Example)	YYYY-MM-DD	MQL score threshold changed from 50 to 60	Sales rejected 38% of MQLs at 50	Prior months restated at 60 for trend charts	[@cmo], [@sales-lead]
[CHG-002]	YYYY-MM-DD	[change]	[reason]	[effect]	[@approver]

Data quality checks before publication:

Spend in ad platforms reconciles with the finance ledger within [Example: 2%].
Share of sessions and leads with a valid UTM campaign code above [Example: 95%].
No duplicate leads per contact within the window.
Opportunity source field populated for all new SQOs.

13. Glossary and references

Adstock: the carry-over effect of advertising into later weeks.
Attribution: assigning credit for a conversion to touchpoints on the path to it.
Incrementality: the conversions caused by marketing that would not have happened without it.
Incrementality factor: incremental conversions divided by platform-reported conversions, used to adjust attribution.
MMM: marketing mix modelling, a regression of outcomes on spend and other factors over time.
MQL / SQO: marketing-qualified lead / sales-qualified opportunity, as defined in section 3.
Saturation: diminishing returns as spend on a channel rises.

References (checked 2026-10-30):

Directive 2002/58/EC (ePrivacy Directive), consolidated text: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32002L0058
ICO, Cookies and similar technologies (PECR guidance): https://ico.org.uk/for-organisations/direct-marketing-and-privacy-and-electronic-communications/guide-to-pecr/cookies-and-similar-technologies/
California Attorney General, California Consumer Privacy Act (CCPA), including Global Privacy Control: https://oag.ca.gov/privacy/ccpa
Related templates: data/analytics-tracking-plan, data/metric-definition, data/dashboard-specification, product/ab-test-plan, mba/mba-marketing-mix-modeling-card, mba/mba-marketing-experiments-card, mba/mba-marketing-analytics-guide, marketing/marketing-plan, marketing/campaign-brief.

Standard and licence note

Based on common marketing analytics practice (KPI trees, attribution, holdout and geo experiments, marketing mix modelling with adstock and saturation, UTM tagging), with privacy questions that cite public law and regulator guidance as dated examples (EU Directive 2002/58/EC, UK PECR and ICO guidance, EU and UK GDPR, California CCPA). The text, structure and the fictional Larkspan figures are original. Check the current official version of each source before relying on it; this is not legal or financial advice. Template licence: MIT.