Marketing Measurement Plan Template (Attribution, MMM, Incrementality)

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.

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Contents

1. Decisions this plan supports
2. KPI tree
3. Metric definitions
4. Data sources and ownership
5. Measurement methods and how they fit
6. Incrementality experiments
7. Marketing mix modelling
8. Attribution and UTM conventions
9. Privacy, consent and data minimisation
10. Reporting cadence and audiences
11. Decision rules and guardrails
12. Change control and data quality
13. Glossary and references

1. Decisions this plan supports

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.

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

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

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

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

6.2 Worked readout: EXP-01 (Example)

• 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

Channel results (Example, last 12 months):

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

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

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

Dashboard specifications follow data/dashboard-specification.

11. Decision rules and guardrails

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

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.