A marketing attribution audit that leaves every dashboard number alive has found nothing useful. An account can legitimately keep every number, but only when each survives reconciliation rather than mere tag inspection. A useful audit is a numbered checklist with a pass/fail on every line, and its main output is a list of numbers you are no longer allowed to report.
The objective is not to crown an attribution model. A Google Ads measurement audit must determine which figures can safely inform spend, bidding and hiring decisions. That evidence problem sits behind why headline Google Ads ROAS can be misleading.
What a marketing attribution audit must deliver
The board needs three deliverables:
- A stop-reporting register: every failed metric, its location, owner, failed check and affected decision.
- An evidence ledger: the identifiers, timestamps, definitions and commercial records supporting every surviving number.
- A prioritised repair queue: each fault, its decision risk, accountable owner and retest condition.
“Needs investigation” is not an acceptable status. A figure either passes, fails or remains explicitly unavailable.
The audit must also separate four monitoring jobs:
- What moved: a genuine change in demand, cost, pipeline or revenue.
- What broke: a collection, transmission or storage failure.
- What the bid strategy did: changes in Google Ads targeting, conversion priorities, budgets or automated bidding.
- What is contaminating the data: duplicates, spam, internal activity, returning customers or misclassified sources.
Combining those jobs creates persuasive nonsense. In an illustrative comparison, a dashboard might report 24 marketing-qualified leads versus 17 CRM-accepted leads. Until that 24-versus-17 gap is explained, nobody knows whether demand moved or measurement broke.
A live signal-monitoring setup can expose sudden faults quickly. It cannot decide whether the commercial definition was valid in the first place.
Attribution monitoring fails when four different jobs share one status light.
The 16-check marketing attribution audit
The count below is a navigation device, not evidence of rigour. Evidence comes from the pass conditions.
Collection layer
| No. | Check | Pass condition |
|---|---|---|
| 1 | Priority form completions | Exactly 10 controlled completions create 10 unique lead identifiers; 9 or 11 fails. |
| 2 | Bookings and calls | Each completed Cal.com booking or qualified call creates one record; cancellations and repeat notifications create none. |
| 3 | Consent state | Accepted and denied journeys record the state specified by the organisation’s approved GDPR/PECR policy. |
| 4 | Acquisition context | Landing page, referrer, campaign parameters and available Google click identifiers remain attached to the lead identifier. |
Transmission layer
| No. | Check | Pass condition |
|---|---|---|
| 5 | Event reconciliation | Source records, Google Tag Manager transmissions and GA4 receipts reconcile by identifier after documented processing time. |
| 6 | Google Ads conversion mapping | Only the intended primary actions inform bidding, with matching value and currency. |
| 7 | Cross-domain continuity | A test journey across the website and Cal.com retains its identifier and creates no self-referral. |
| 8 | Import failures and delays | Imported records arrive within the documented service level; missing records produce an exception rather than a silent zero. |
Storage layer
| No. | Check | Pass condition |
|---|---|---|
| 9 | CRM identity | Every attributed conversion resolves to exactly one person, company or opportunity record. |
| 10 | Original-source retention | Original source remains immutable while later touches are stored separately. |
| 11 | Stage history | Every material stage entry has a definition, timestamp and responsible owner. |
| 12 | Commercial value | Closed-won value and currency match the approved CRM or finance record; amendments remain visible. |
Reporting layer
| No. | Check | Pass condition |
|---|---|---|
| 13 | Cost scope | Spend currency, VAT treatment and included fees are documented and reconcile with the chosen cost source. |
| 14 | Model and window labels | Every attributed figure states its model and lookback window; unlike windows are never presented as equivalent. |
| 15 | Cohort basis | Click-date, lead-date and close-date reports are labelled, with no silent mixing of denominators. |
| 16 | Metric lineage | Every published number traces to a numerator, denominator, source record and named owner. |
Three immediate thresholds convert failures into decisions:
- Controlled tests: 10 expected versus 10 observed passes; 9 or 11 triggers a reporting hold and retest.
- Value reconciliation: a dashboard-to-CRM difference above 5% removes that value metric from budget decisions.
- Missing identity: more than 2% unmatched records creates a separate “unknown” cohort and blocks campaign credit.
The 5% rule is falsifiable: if the next complete sales-cycle reconciliation lands within 5% without repairs, our instruction to withdraw the metric was wrong.
What this cannot tell you is how much demand would have existed without advertising. Attribution reconstructs observed journeys; it does not create a counterfactual.
The honest limit here is sales-cycle maturation. If won revenue from the same cohort changes by more than 20% between day 7 and day 90, use the 90-day figure for budget decisions and label the 7-day figure directional.
Every permitted number needs an evidence chain that survives reconciliation.
Turn failed checks into a £ consequence
A failed line must alter reporting, otherwise failure becomes a decorative note.
Take an illustrative UK consultancy with five labelled inputs:
- Monthly Google Ads spend: £8,000
- Dashboard-attributed pipeline: £72,000
- Duplicated opportunity value: £12,000
- Existing-customer renewal misclassified as acquisition: £20,000
- Pipeline lacking a joinable paid-media identifier: £10,000
Four calculations follow:
Auditable paid-media pipeline = £72,000 − £12,000 − £20,000 − £10,000 = £30,000
Reported pipeline ROAS = £72,000 ÷ £8,000 = 9.00x
Permitted pipeline ROAS = £30,000 ÷ £8,000 = 3.75x
Pipeline removed from paid attribution = £72,000 − £30,000 = £42,000
The immediate consequence is not that £42,000 of pipeline never existed. It is that paid media may no longer claim it. Total company pipeline and auditable paid-media pipeline answer different questions.
The reported 9.00x versus permitted 3.75x comparison should then feed the agreed commercial ROAS calculation method, not an improvised dashboard formula.
Commercial decisions belong to auditable value, not credited value.
If the path from ad click to signed revenue still has gaps, Actualyse will trace each hand-off with you — book a call
Common findings and the decisions they trigger
Four findings dominate the version of this problem that reaches us.
1. One outcome has several conversion identities
A form event, thank-you page view, imported CRM stage and sales notification can all describe one opportunity. Illustratively, two conversion rows versus one CRM opportunity is inflation, not growth.
If the conversion-to-opportunity relationship is not 1:1, suspend summed conversion reporting and nominate one commercial identity.
2. Google Ads is optimising a convenient proxy
The primary conversion is often the fastest event to collect rather than the deepest reliable outcome. Automated bidding then does exactly what it was instructed to do.
If a primary Google Ads action cannot join to an agreed CRM stage, make it secondary until repaired. If fewer than 30 qualified outcomes exist in the evaluation window, aggregate the analysis rather than ranking thin campaign samples.
Where the failure extends into conversion priorities, queries and bidding, specialist Google Ads account expertise should own the remediation.
3. The CRM overwrites acquisition history
Latest-touch fields frequently replace original source. A returning customer clicking an advert can then make paid search appear to acquire revenue that already existed.
If more than 2% of original-source values change without a timestamped touch record, freeze the field and withdraw source-level revenue reporting.
4. Measurement ignores the site’s buying routes
Page-type reporting is rarely enough for a complex offer. In our Lanteria work, broad capabilities needed routes for several stakeholder audiences. AfriCap Hub required a catalogue, filtering and registration journey. Savgen needed a technical multi-industry structure, while Lake Erie Shores separated stay and ownership audiences under one site.
Those were architectural decisions, not performance claims. Measurement should preserve the same distinctions.
Two routes with different buyers or conversion definitions require two cohorts. Paid-search intent should follow the same separation used in the B2B query and keyword structure.
Common failures matter only when each failure changes a decision.
What does not work in an attribution audit
Step counts are marketing. Four familiar moves fail to improve the decision:
- A 70-point checklist without pass conditions measures auditor activity, not data validity. A checklist producing no decommissioned metrics found nothing.
- Google Tag Manager and GA4 screenshots prove that interfaces displayed events. They do not prove identity, commercial value or CRM agreement.
- Switching last-click to data-driven attribution redistributes credit across the same contaminated records. A different model cannot repair a duplicate opportunity.
- Installing another dashboard accelerates the presentation of unresolved definitions. Faster refreshes make wrong numbers arrive sooner.
If a line has no pass condition, it is not a check. Our 16-line count is not evidence of rigour; the reconciliation and reporting consequences are.
Remediation should be scoped from failed lines. An ongoing Google Ads programme is relevant only when those failures reach account operation and landing-page intent.
Audit theatre preserves numbers that evidence should remove.
FAQ
How long should a marketing attribution audit take?
For one website, one Google Ads account and one CRM, set a five-working-day evidence window. More than three sales pipelines should trigger phased sign-off because definitions and owners usually differ.
Which records should be sampled?
Review the 100 most recent conversions plus every closed-won opportunity above £10,000 in the chosen period. If fewer than 100 conversions exist, inspect the complete population.
Who approves the definitions?
Finance approves recognised revenue and currency, sales operations approves stage entry, and marketing approves source mapping. Any definition missing its named approval contributes £0 to attributable board reporting.
When should the audit be repeated?
Run a focused audit within five working days after any of six trigger changes: domain, consent platform, CRM, form handling, offline import or primary Google Ads conversion. Without a trigger, re-audit every six months above £20,000 monthly media spend versus annually at £20,000 or below.
Audit scope should follow decision risk, not platform count.
Summary
Use these five operating rules:
- Ban any metric that lacks a pass condition, evidence chain or named owner.
- Require exactly 10 observed records from 10 controlled conversion tests.
- Remove value metrics from budget decisions when CRM variance exceeds 5%.
- Place unmatched records in an “unknown” cohort when missing identity exceeds 2%.
- Separate what moved, what broke, bidding behaviour and contamination into four monitoring jobs.
Only reconciled numbers earn a place in the board pack.
Actualyse builds measurement and attribution setups that tie B2B ad spend to real revenue. Book a call to talk through where yours stands.
