Why Ad Platforms All Claim the Same Sale
Ad platforms all claim the same sale because each one counts any conversion it touched, using its own attribution window and its own rules. Add up Google, Meta, TikTok and LinkedIn and the total is almost always higher than the orders in Shopify or the leads in your CRM.
None of the platforms is lying, exactly. They are each answering a different question from the one you are asking. Here is how it happens, and how I reconcile it.
How does over-attribution happen?
Picture one customer. They see a Meta video on Monday, search your brand on Google on Wednesday, click a search ad and buy.
- Meta counts a conversion, because the customer saw its ad within its attribution window.
- Google counts a conversion, because the customer clicked its ad before buying.
- Shopify records one order.
Two platforms, one sale. Repeat that across thousands of customers and platform totals can sit well above reality. The more channels you run, the bigger the gap tends to get.
What is the difference between click-through and view-through?
Click-through conversions
The customer clicked an ad and later converted within the click window. This is the more reliable signal, although it still does not prove the ad caused the sale.
View-through conversions
The customer saw an ad, did not click, and later converted within the view window. Platforms that run video and feed ads, such as Meta and TikTok, can include these in their reported conversions depending on your settings.
View-through is not worthless. Video genuinely does influence people who never click. But a view-through conversion can also be someone who scrolled past an ad on their way to buying something they were already going to buy. Treat it as a signal of influence, not proof.
Why do attribution windows make it worse?
Every platform lets you choose, or sets by default, how long after a click or view a conversion still counts. A longer window captures more conversions. Different windows on different platforms mean each one is drawing its circle around a different set of customers.
Common problems I see:
- one platform on a long click window and another on a short one, then compared side by side
- view-through windows switched on without anyone realising
- windows changed mid-period, so month-on-month comparisons are not like for like
Check the attribution settings on every account before comparing anything.
What are modelled conversions?
Since browser and device privacy changes reduced how much can be tracked directly, platforms fill gaps with modelled conversions. They estimate conversions they believe happened but could not observe, based on patterns in the data they can see.
Modelling is reasonable and often helps optimisation. But it means part of the number in the platform is an estimate made by the party selling you the ads. It is one more reason the platform total should not be your source of truth.
How do you reconcile platform numbers with reality?
The principle is simple: your CRM or your shop decides how many sales or leads happened. The platforms help explain why.
Start with the source of truth
For ecommerce, that is Shopify orders and revenue. For B2B, that is HubSpot or Salesforce leads, opportunities and pipeline. Pull the real totals for the period first.
Compare total spend against total outcomes
Before looking at channels, look at the blended picture: total ad spend against total new revenue, or total spend against opportunities created. If the blended number is healthy, the mix is working, even if individual platforms disagree about who deserves credit.
Line up channel data against real data
For each platform, put its reported conversions next to what your CRM or Shopify attributes to that channel through UTMs or first-party source data. The gap between the two shows you how much each platform is over-claiming.
Look at trends, not single weeks
A weekly reconciled view, where spend by platform sits next to real orders or pipeline, shows relationships you cannot see in any single platform. When one channel goes up and another channel’s results go up with it, that tells you something.
Watch for cross-channel effects
Some channels create demand that another channel collects. Video on Meta or TikTok often shows up later as more branded search on Google. If you only look at last-click, you will over-credit search and under-credit the channel that created the interest.
What did this look like for Soto Gardens?
With Soto Gardens, a garden retailer, I built a weekly reconciled view of spend against real revenue. It showed that Google Search improved when Meta video was running. Meta’s own numbers did not tell the full story, and Google was taking credit for demand Meta had helped create.
That gave the brand a reason to keep Meta budget in place through softer months, rather than cutting it because platform ROAS looked weak. More on that work is in the Soto Gardens case study.
When are incrementality and holdout tests worth it?
Reconciliation tells you what happened. An incrementality test tells you what a channel caused. The usual approach is to stop or reduce a channel for a defined group, such as a set of regions or an audience held out from ads, and compare results against a group that kept seeing ads.
They are worth running when:
- a channel takes a large share of budget and you need to justify it
- reconciled data is ambiguous, for example retargeting or branded search that might be capturing sales that would happen anyway
- you are about to make a big budget decision and want evidence beyond correlation
They are less useful when:
- spend is small, because you will not get a clear result
- you cannot hold the test long enough for your buying cycle
- the business cannot tolerate a temporary dip in a channel during the test
Some platforms offer their own lift studies. These can be useful, but they are run by the platform being measured, so I treat them as one input rather than the final answer.
How should you report this?
I report against CRM pipeline in HubSpot or Salesforce, and Shopify revenue, not platform numbers. Platform data stays in the report as diagnostic detail: which campaigns, audiences and creatives are pulling their weight. You can read more on my ecommerce paid media page.
Building the weekly reconciled view by hand is tedious. If you want it automated, my sister business Ad Ops Autopilot builds reconciled reporting dashboards that join platform spend to Shopify or CRM data.
Not sure what your real numbers are?
If your platforms add up to more sales than you made, I offer a free account audit. I will check your attribution settings and tracking, compare platform claims against your real data, and show you where the gaps are.
Questions people ask
Why do my ad platforms report more conversions than I actually had?
Each platform counts any conversion it touched within its own attribution window and model. One customer who saw a Meta ad and clicked a Google ad can be counted as a conversion by both, so the platform totals add up to more than your real sales or leads.
What is the difference between click-through and view-through conversions?
A click-through conversion happens after someone clicked an ad. A view-through conversion happens after someone saw an ad without clicking, which platforms may still count, so view-through numbers need more scepticism.
Which number should I trust, the platform or my CRM?
Use your CRM or Shopify as the source of truth for how many sales or leads happened, and use platform data to understand which campaigns and creatives are contributing.
When is an incrementality or holdout test worth it?
When a channel takes a meaningful share of budget and you cannot tell from reconciled data whether it is driving extra sales or just being credited with sales that would have happened anyway.