For this hypothetical order, everyone has a plausible story. The social team introduced the shop. Search brought the customer back. An email finally prompted the purchase. Put the three reports beside one another and each team can point to the moment when its work appeared to help.
The shop, meanwhile, has one payment to collect. I find that a useful place to begin a marketing meeting, especially when the reports sound as though three different customers arrived.
This is a hypothetical journey, but the reporting problem is documented. Shopify explains that platforms can apply different attribution rules and claim credit for the same sale. A discrepancy deserves investigation; it does not, by itself, show that a platform is inventing orders. Shopify's explanation.
Start with the order
An attribution model supplies the rule for this argument. Last click rewards one point in the journey; a model distributing credit tells a different story. I can see a reason to examine both. What I cannot do is add their accounts together and expect the till to contain the result.
For a weekly review, I would start with the business record: orders, cancellations, refunds and the revenue definition being used. Then bring in the channel reports. This sequence makes it harder for an attractive platform number to become the assumed total before anyone checks it.
There are less dramatic explanations to check too. Reports may use different time zones, update at different times or treat shipping and tax differently. A purchase may be credited to an earlier ad interaction rather than the date used in another report. Aligning those details will not make every system identical, but it can remove differences that have nothing to do with marketing effectiveness.
Keep the claims visible
A deliberately plain sheet would help this review. Its job is to keep the business result visible while we investigate what the platforms believe happened:
| Record | What to write down |
|---|---|
| Business results | Orders and net sales under an agreed definition |
| Channel claims | Conversions and revenue reported by each platform |
| Reporting rules | Attribution model, window, time zone and date basis |
| Adjustments | Refunds, cancellations, tax, shipping and reporting delays |
| Remaining differences | What is still unexplained and who will investigate |
Keep platform claims in separate columns. Their sum should not become a second revenue total.
This also makes the conversation with an agency more useful. Instead of asking why its number is wrong, ask what the number includes and which decision it is intended to support.
Suppose a hypothetical shop records ten completed orders worth $1,000 under its agreed revenue definition. A search platform claims $700, a social platform claims $600 and an email report claims $400. The shop should not announce $1,700 in sales. The $1,000 business total and the overlapping channel claims belong in different parts of the report.
Where reliable identifiers and permissions allow, inspect a few recorded journeys. Perhaps an order appears in both email and search reporting. That would explain part of the overlap, but the team should not assume the rest has the same explanation. Keep a note of what has actually been reconciled.
The review could end without a channel winning the argument. Perhaps business revenue has risen, while the channel responsible remains uncertain. That conclusion is awkward to put on a celebratory slide, but it is more useful than moving budget because one team found the most generous reporting rule. Campaign changes and customer journeys are still worth examining; the overlapping columns cannot decide causality for us.
Agree on this reporting convention before a campaign performs badly. Otherwise, every disappointing month becomes an argument over which dashboard deserves to be believed.
Credit leaves another question open
Even a perfectly reconciled report cannot tell us how many customers would have purchased without a campaign. That is a question about additional impact. Google's Conversion Lift documentation describes comparing groups exposed to advertising with groups withheld from it to estimate that difference. Such studies require an appropriate design and sufficient data; they are not available or practical for every account. How Conversion Lift works.
A smaller business may have to make a budget decision without a convincing causal test. I would keep the uncertainty attached to that decision, rather than bury it beneath an impressive return figure. The order total is something the team can reconcile today. The question of what would have happened without the campaign may remain open after the meeting ends.