If you run paid social and paid search side by side, you have almost certainly seen it: Meta claims one number of purchases, Google claims another, and Shopify shows a third that matches neither. The single biggest driver of that gap is the attribution window — the lookback period each platform uses to decide whether it gets to claim a sale. This is a comparison of how Meta and Google set those windows, why the difference matters for your budget, and how to read the two side by side without fooling yourself.
What an attribution window actually is
An attribution window is the amount of time that can pass between someone interacting with your ad and buying, for that ad to still get credit. There are two flavors of interaction:
- Click-through: the shopper clicked your ad, then converted within the window.
- View-through: the shopper only saw your ad (an impression, no click), then converted within a shorter window.
Both platforms let you compare data on different windows after the fact, but the setting on the campaign is what the algorithm optimizes toward and what shows up as the headline number. Changing a comparison window only re-slices data you already have; it does not retrain the campaign.
Meta vs Google: the default windows compared
Here is where the two platforms diverge most. The table below reflects each platform's current defaults; verify at the source before you rely on them, since privacy rules keep shifting these.
| Platform | Default window | Counts view-through? | Longest click window |
|---|---|---|---|
| Meta Ads | 7-day click + 1-day view | Yes (1-day view) | 7-day click |
| Google Ads | 30-day click | No | 90-day click |
Meta's default of 7-day click plus 1-day view is documented by Foreplay and Jon Loomer, and Google's default 30-day click window and its refusal to count view-through are laid out by Rely Digital. Read those two rows together and a pattern jumps out: Google's click window is more than four times longer than Meta's, yet Meta claims credit for sales nobody clicked on at all. They are generous in opposite directions.
Why Meta looks inflated
Meta's view-through credit is the classic inflator. With the default 1-day view setting, someone who scrolls past your ad, never taps it, and buys within a day still counts as a Meta conversion. Shopify has no concept of a "view" — it only records a completed checkout — so those sales never trace back to Facebook on the store side. Meta also models conversions it cannot directly observe (blocked pixels, iOS opt-outs) and reports the estimate as a count.
Add those up and a 20–35% gap between Meta-reported purchases and Shopify orders is normal on the default window, per Vaizle and TrackBee. One comparison of neutral tooling found Meta reporting roughly a quarter more conversions than a platform-agnostic tracker, largely from view-through plus modeling (Rely Digital).
Why Google looks different
Google's 30-day click window is long, so it can claim a sale from a click that happened weeks ago — well after Meta's 7-day window has closed. But because Google ignores view-through, it never inflates on impressions. The two platforms can therefore both credit the same order (a shopper who clicked a Search ad three weeks ago and a Facebook ad yesterday), which is exactly why summing "Meta conversions + Google conversions" routinely exceeds your real order count.
A worked example: one sale, three numbers
Say you sell a print-on-demand mug for $40 plus $5 shipping and $4 tax, so the customer pays $49. In one week you get 100 real orders. Of those buyers:
- 55 clicked a Meta ad within seven days before buying.
- 15 only saw a Meta ad within one day, no click.
- 10 clicked a Google Search ad, the last of them 22 days before checkout.
- 20 came from organic or direct.
Watch how each system reports that identical week:
- Meta counts the 55 click-throughs + 15 view-throughs = 70, then adds roughly 8 modeled conversions for buyers it could not observe: about 78 purchases. It reports revenue at the $40 subtotal (the pixel usually passes subtotal, not shipping or tax), so about 78 × $40 = $3,120.
- Google counts its 10 clicks — including the one from 22 days ago that Meta's 7-day window already dropped — for 10 conversions on last-click within its window.
- Shopify records all 100 orders on last non-direct click: about 55 to Facebook, 10 to Google, 35 to search/direct/other. The 15 view-through buyers are filed under their real last referrer, not Facebook.
Now sum the ad platforms: 78 + 10 = 88 claimed conversions against 100 real orders, with heavy double-counting on the shoppers who touched both ads. That is not fraud — it is two different windows measuring two different questions. Meta answers "did my ad plausibly influence this?" Google answers "was I the last click within a month?" Only Shopify answers "did a sale happen, and for how much?"
The profit angle every comparison skips
Most articles stop at "the numbers don't match." The part that actually costs you money is what happens when you compute return on ad spend from an inflated number. Continuing the example: if Meta claims 78 conversions worth $3,120 and you spent $1,000 on Meta, its dashboard shows a tidy 3.1x return on ad spend. But you only banked 100 orders total across all channels, and 15 of Meta's 78 were view-through sales you would likely have gotten anyway.
Reported return on ad spend is a revenue ratio, and revenue is not profit. Strip out the product cost, the Printful or Printify base cost, the Shopify processing fee of about 2.9% + 30¢ per order on the Basic plan (Webgility), and the ad spend itself, and that "3.1x" can hide a loss. This is the same trap covered in depth in why platform ROAS overstates profit — the window inflates the numerator while your true costs sit untouched underneath.
How to compare the two windows fairly
You cannot make Meta and Google agree, but you can stop comparing apples to oranges:
- Normalize the click window. Set both platforms to a 7-day click view (or set Meta's comparison window to match), so you are at least reading the same click horizon on both.
- Isolate view-through. In Meta, look at the 7-day-click-only number next to the default. The difference is your view-through inflation. Switching a campaign from the default down to 1-day click can cut reported conversions by roughly 40% on the same real sales, per TrackBee — that swing is the window, not your performance.
- Compare on trailing windows, never single days. Meta reports a conversion on the click date, not the purchase date, so a Monday click that converts Thursday desynchronizes daily charts. Use 7- or 14-day trailing totals.
- Anchor everything to Shopify. Your store's order count and total sales are the server-side source of truth. Ad platforms tell you influence; Shopify tells you reality. When they disagree, reality wins the count.
Keep in mind the tracking gaps too — ad blockers and consent declines affect roughly 10–25% of users (Audiense/Elevar), which is why platform numbers can also undercount. If your Shopify channel reports look empty, the culprit is often broken tagging; see why your Shopify UTM parameters aren't showing.
Which number should you trust?
For how many sales happened and how much revenue you earned, trust Shopify. For which channel deserves credit for influence, understand that Meta and Google each answer with their own window and neither is dishonest — they are just self-interested by design. The deeper question of splitting credit across many touches is where last-click vs data-driven attribution on Shopify and dedicated multi-touch attribution tools come in. The full framework for making all these systems tell one coherent story lives in our guide to reconciling your ecommerce data.
Where PodVector fits
The reason attribution windows matter is money, and windows do not measure money — they measure claimed influence on inflated revenue. PodVector connects Shopify, Meta Ads, Google Ads, Printify, and Printful, then computes your true per-order profit from real Shopify orders instead of any platform's window-based estimate. PodVector is not a dashboard; it is a live data warehouse with Victor, an AI employee who reads your ad and store data, tells you which of those "3.1x" campaigns are actually profitable after costs, and proposes Shopify-side moves for your approval. Victor does not touch your ad account — he reads its data and hands you the profit math the windows hide.
If you are tired of reconciling three conversion counts by hand, connect your stores and let Victor do the profit math.
FAQs
What is the default attribution window for Meta vs Google?
Meta defaults to a 7-day click plus 1-day view window, and Google Ads defaults to a 30-day click window with no view-through credit, according to Foreplay and Rely Digital. Google's click window is far longer, but Meta additionally claims sales that were only seen, not clicked.
Why does Meta report more conversions than Google for the same store?
Mostly view-through and modeling. Meta credits itself for impressions that led to a purchase within a day, and it estimates conversions it cannot directly observe. Google counts neither, so on identical spend Meta's number tends to run higher — a 20–35% gap over Shopify orders is considered normal on Meta's default window (Vaizle).
Can I make Meta and Google attribution match?
Not exactly, but you can compare them fairly by setting both to the same click window and reading Meta's 7-day-click-only number to strip out view-through. They will still differ because the platforms answer different questions, so aim for a stable, understood gap rather than equality.
Should I widen my Meta window to match Google's 30 days?
You can't — Meta removed the 28-day click and view windows after Apple's iOS privacy changes, capping click attribution at 7 days (Foreplay). The practical move is the reverse: narrow Google's comparison view to seven days so you're reading both platforms on the same click horizon.
Which platform's number should I use to calculate profit?
Neither directly. Use Shopify's order count and total sales as the revenue and order truth, subtract product, fulfillment, and processing costs, then attribute ad spend against that. Window-based platform revenue overstates the top line, which is why return-on-ad-spend math built on it can mask a loss.