# Marketing Attribution Audit Checklist: 20 Things to Verify Before Trusting Your Data

> A practical checklist for auditing your marketing attribution setup. Covers tracking integrity, UTM hygiene, model configuration, and CRM integration quality.

- **Published:** 2025-09-21
- **Author:** Clixo
- **Reading time:** 6 min read
- **Tags:** attribution, marketing-analytics, audit, tracking
- **Canonical URL:** https://clixo.sh/blog/marketing-attribution-audit-checklist

Attribution data is only as trustworthy as the system that produced it. Before making budget decisions based on your channel reports, there is a set of structural questions worth asking — most of which are rarely answered formally.

This checklist covers the tracking, configuration, and integration checks that separate attribution data you can act on from data that feels authoritative but misleads.

```mermaid
flowchart TD
  A["Tracking Integrity\n(UTMs, GA4 tags, deduplication)"] --> B["Attribution Model Config\n(windows, model consistency)"]
  B --> C["CRM and Offline Integration\n(lead source, offline conversions)"]
  C --> D["Server-Side and Technical Checks\n(first-party cookies, consent mode)"]
  D --> E["Reporting and Dashboard Integrity\n(metric definitions, cohort windows)"]
  E --> F["Ongoing Hygiene\n(tracking spec reviews, staging tests)"]
```

## Tracking Integrity

**1. Are all major traffic sources tagged with UTM parameters?**
Pull your source/medium report and look at the percentage of sessions attributed to direct. A direct percentage significantly higher than your baseline may indicate untagged campaigns — particularly email, social shares, or paid channels that someone forgot to tag.

**2. Are UTM parameters consistent in capitalization and spelling?**
Search for near-duplicate source or medium values: `google` vs `Google`, `email` vs `Email`, `cpc` vs `CPC`. Each pair is a split in your data. Consolidate and enforce a naming convention going forward.

**3. Are internal links UTM-tagged?**
UTM parameters on internal links reset session attribution. Check your site for any internal navigation using UTM-tagged URLs. This is common in email-to-web redirects, promotional banners, and CMS-managed content.

**4. Is your GA4 configuration tag firing on all pages?**
Open GA4 DebugView and navigate through the key pages of your site. Verify the configuration tag fires and that `page_view` events appear. Check for pages where tracking is missing — checkout pages and thank-you pages are common gaps.

**5. Are conversion events firing exactly once per conversion?**
Check your purchase or lead submission events for duplicate events. A common misconfiguration fires a conversion event on page load rather than on a confirmed action, or fires it multiple times if the user refreshes the confirmation page.

## Attribution Model Configuration

**6. Do you know which attribution model your analytics platform is currently using?**
GA4 defaults to data-driven attribution if your account qualifies, and last-click otherwise. Your ad platforms each use their own model. Document what model each reporting surface uses before comparing numbers across platforms.

**7. Are your attribution windows configured to match your sales cycle?**
A 7-day attribution window for a business with a 45-day average purchase cycle will systematically undercount the channels that influence early-stage consideration. Check your attribution window settings in GA4 and in each ad platform.

**8. Is the same model used consistently across all reporting?**
If your GA4 uses data-driven attribution and your campaign report uses last-click, the numbers will disagree. This is not a data error — it is a model difference. But if your team does not know this, it leads to unproductive reconciliation exercises.

**9. Are your conversion events marked correctly in GA4?**
Only events marked as conversions in GA4's Admin panel appear in conversion reports. Check which events are marked as conversions and verify they represent actual business outcomes, not intermediate engagement events.

## CRM and Offline Integration

**10. Is lead source captured in your CRM at the time of lead creation?**
Lead source fields populated retroactively or manually are unreliable. Verify that your CRM automatically populates lead source from UTM parameters passed through your form submission handling.

**11. Do your CRM and GA4 agree on conversion volume?**
Pull conversions for the past 30 days from GA4 and from your CRM. If they disagree significantly, identify whether the gap is explained by attribution window differences, duplicate tracking, or genuinely missing events.

**12. Are offline conversions imported into your ad platforms?**
If sales happen offline — phone calls, in-person, or through a sales team — those conversions should be imported into Google Ads and Meta as offline conversion events. Without this, your paid campaigns are being judged only on the leads they generate, not on the revenue they produce.

## Server-Side and Technical Checks

**13. Is server-side tracking in place for high-value conversion events?**
Purchase events, lead form submissions, and account creations are the events that should not be lost to ad blockers. If these are only tracked client-side, verify what percentage of conversions you may be missing by comparing against CRM or transaction records.

**14. Are first-party cookies being used for session identification?**
Check your GA4 cookie configuration. If the `_ga` cookie is set from a third-party domain rather than a first-party subdomain, it will be treated as a third-party cookie by Safari and other privacy-focused browsers, limiting its lifespan to 7 days or less.

**15. Is consent mode configured correctly?**
Verify that GA4 events respect your consent management platform's decisions. In a compliant setup, analytics events should not fire for users who have declined consent, and GA4's consent mode should be configured to model behavior for non-consenting users rather than ignoring them entirely.

## Reporting and Dashboard Integrity

**16. Are all metric definitions documented?**
For each metric on your attribution dashboard, is there documentation covering the data source, calculation logic, and any filters applied? Undocumented metrics cause attribution debates that are really definitional disagreements.

**17. Are reporting cohort windows consistent with your business cycle?**
If you report on conversions that occurred in a period, make sure you are not comparing against a different period's pipeline. Define whether you are reporting on when the lead was created, when the conversion event fired, or when the deal closed — and use that definition consistently.

**18. Is your reporting layer pulling from a consistent data source?**
If your dashboard aggregates data from multiple platforms with incompatible attribution models, the totals will not reconcile. Identify your authoritative data source for each metric and enforce that it is the source used in every report.

## Ongoing Hygiene

**19. When was your tracking specification last reviewed?**
A tracking spec written at launch drifts from the actual implementation over time. Schedule a quarterly review to verify that the events and parameters currently live in production match what is documented.

**20. Is there a process for testing new tracking before it goes to production?**
New events and tags should be tested in a staging environment and verified in GA4 DebugView before they are published. Without a testing process, misconfigured tags ship silently and corrupt data for however long they run before someone notices.

Working through this checklist systematically takes a few hours — but it will surface problems that have been quietly distorting your attribution data for months.

If you want a structured attribution audit performed on your current tracking setup, with documented findings and a prioritized remediation plan, [get in touch with Clixo](https://clixo.sh/#contact).

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