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Most attribution projects start with a request for a new tool. This one started with a direct-to-consumer brand that sold through its own Shopify store and on Amazon, and whose channel numbers did not agree with each other. The scope below is anonymized and shows the plan, not a case study with results.
The starting point
- Attribution was last-touch only, so upstream channels were undervalued.
- Shopify carried a high share of unknown or unattributed traffic.
- Amazon performance was not connected to marketing activity, and the journey from the website to Amazon was not visible.
- No single source of truth existed for channel performance.
The four workstreams
The engagement had four workstreams. The first two find and fix what is broken. The third decides how multi-touch attribution should work. The fourth turns findings into decisions that someone owns.
| Workstream | What it covers | Why it comes here |
|---|---|---|
| Foundation audit (Shopify and Amazon) | UTM governance, Shopify unknown traffic root causes, GA4 configuration, a source of truth per metric, and what Amazon data is available | Every later decision depends on tags and definitions being trustworthy |
| Channel audit | Paid account structure and conversion setup, affiliate tracking and payouts, email attribution, and social tagging | Channel findings are only comparable once the foundation is in place |
| Multi-touch evaluation | What GA4 can deliver, plus a comparison of Northbeam and Triple Whale on cost, effort, accuracy, and Amazon coverage | The choice needs clean inputs to evaluate fairly |
| Insight and operating rhythm | Decisions on spend and fixes, defined workflows and owners, and a reporting cadence built around questions | Without owners and a cadence, the audit findings fade |
Days 1–30: audit and fix
| Week | Focus |
|---|---|
| Week 1 | Kickoff, access, stakeholder alignment, data inventory, and the key business questions |
| Week 2 | UTM audit, Shopify unknown traffic diagnosis, and GA4 configuration review |
| Week 3 | Channel audits for paid, affiliate, and email, with spend and tracking findings |
| Week 4 | Prioritized issues list, quick fixes live, and a readout of findings and recommendations |
By the end of day 30, the engagement should produce:
- Audit findings with severity and impact ranking.
- A UTM standard and naming taxonomy, with a QA checklist the team can run before launch.
- Quick-win fixes implemented, covering tagging, Shopify unknown traffic reduction, and conversion setup.
- Baseline metrics: unknown traffic share, UTM coverage, and the GA4-to-Shopify reconciliation variance.
- Initial spend efficiency observations by channel.
- A recommendation on the multi-touch path: GA4, Northbeam, or Triple Whale.
Days 31–60: build and operationalize
| Week | Focus |
|---|---|
| Week 5 | Decision on the multi-touch approach, then implementation or vendor selection begins |
| Week 6 | Single source of truth in place, with the reconciliation process live |
| Week 7 | Amazon and website journey linkage, with multi-touch views in early use |
| Week 8 | Insight readout, budget and channel recommendations, and handoff of workflows and owners |
By the end of day 60, the engagement should produce:
- A multi-touch model running, with a comparison against last-touch results.
- A documented source of truth and metric definitions.
- An Amazon and direct-to-consumer journey view, with its known limitations stated.
- Spend reallocation recommendations backed by multi-touch data.
- Standing workflows for UTM governance, monthly QA, the reporting cadence, and ownership.
- A roadmap for the next phase, such as incrementality testing and expanded coverage.
How success is measured
The measures below are the kind the engagement tracks. The thresholds are agreed with the client for their own baseline, so they are not universal numbers.
- Unknown Shopify traffic is reduced against the baseline.
- UTM compliance is above an agreed threshold.
- GA4-to-Shopify revenue variance is within an agreed range.
- The multi-touch view is used for at least one budget decision by day 60.
- Every recurring attribution process has a named owner.
Why the tool decision comes last
The brand was evaluating Northbeam and Triple Whale, and the scope treats that as a real option. It is still deliberately sequenced after the audit. Buying a platform on top of unknown traffic and broken UTMs imports the same gaps into a more expensive system. GA4 may be enough for some of the questions, and Amazon coverage is often the deciding factor, since a platform that cannot see Amazon leaves the marketplace side of the business out of the model. Tool budget and appetite for a third-party platform were left open on purpose, to be decided once the findings are in.
Adapt this scope for your business
You do not need an engagement this size to apply the same logic. The steps below work for most teams running paid media across a storefront and a marketplace.
- Define the business questions first, before choosing any tool or report.
- Baseline unknown traffic share and the reconciliation variance against orders.
- Name a system of record for each metric, such as revenue, orders, and channel spend.
- Decide the multi-touch tool after the audit, not before it.
- Put a named owner on each recurring process, including UTM governance and monthly QA.
For the foundation work, start with the Shopify vs GA4 revenue guide and the UTM standard. To compare the multi-touch options, read the multi-touch attribution guide, and see the attribution modeling service. If you want to scope an audit for your own store and marketplace, book a tracking audit and bring the channel reports that disagree.
Frequently asked questions
What should an attribution audit include?
A useful audit covers UTM governance, unknown traffic root causes, GA4 configuration, reconciliation against Shopify orders, channel-level tracking for paid, affiliate, email, and social, and a clear review of what Amazon data can and cannot show. It ends with a prioritized issues list and named owners.
How long does an attribution audit take?
In the scope described here, the first 30 days cover audit and quick fixes, and days 31 to 60 cover the decision on a multi-touch approach, a reconciliation process, and standing workflows. The timing depends on how many channels, storefronts, and data sources are in play.
Should we buy Northbeam or Triple Whale before fixing tracking?
Usually not. A third-party attribution platform reads the same tags, UTMs, and order data as everything else. If unknown traffic and broken UTMs are not fixed first, the platform imports those gaps. The tool choice is easier to make once the audit findings are in.
Can you connect Amazon sales to marketing activity?
Partly. The audit assesses which Amazon signals are available, such as Amazon Attribution, Brand Analytics, and Amazon Ads reporting, and how site-to-Amazon behavior can be linked. Some journeys will stay invisible, and the limitations should be stated in the readout rather than hidden in a dashboard.
What does MetricMaven deliver in an attribution engagement?
The deliverables are audit findings ranked by severity and impact, a UTM standard with a QA checklist, quick-win fixes, baseline metrics, channel spend observations, a recommendation on the multi-touch path, and standing workflows with owners. The goal is insight and workflow change, not more dashboards.