Attribution Windows on Meta and Google Explained for Stores

Attribution windows are the main reason the sales Meta and Google claim add up to more than your store actually sold. A window is the period in which a platform may credit a purchase to its ad after a click or a view: 7-day click and 1-day view on Meta by default, 30 days after a click for Google Ads conversions by default, and a data-driven model in GA4 that distributes credit across channels. Understanding these windows does not change the numbers, but it changes how you read them and how you decide. This article explains each window, why platforms over-claim in good faith, and how to read overlap with practical rules.

What an attribution window is, in plain words

When a customer buys from your store, each platform asks one question: did this person interact with my ad recently? The window defines recently. If a customer clicked an Instagram ad on Saturday and bought on Thursday, the purchase sits inside the 7-day window and is credited to Meta. If the same customer searched on Google on Wednesday and clicked a search ad, the purchase also sits inside Google’s window and is credited to Google. One order, counted by both.

No platform is lying; each answers its own question honestly from its own data. The problem starts when the merchant adds the answers together.

Attribution windows on Meta

Setting	Meaning	When it fits
7-day click, 1-day view (default)	Any purchase within 7 days of a click or within 24 hours of a view with no click	Most retail stores
7-day click only	Excludes views	A more conservative campaign read
1-day click only	The most conservative	Impulse, low-price products
28-day click (reporting only, some events)	The widest window	Products with a long decision cycle

View-through attribution is the most over-claimed: a customer who scrolled past your ad in the feed without stopping, then bought within 24 hours for an unrelated reason, is counted for Meta. In broad campaigns and retargeting, the view share can be large. The rule: read view-through conversions as an awareness and frequency signal, not as sales.

A tip: in Ads Manager you can change the attribution setting in the reporting columns (compare attribution settings) to see the same campaign under different windows without changing the actual campaign setting. It is the fastest way to learn how much of your ROAS really comes from clicks. All of this assumes correct tracking, as covered in our Meta Pixel and Conversions API Shopify setup guide.

Conversion windows in Google Ads

Google Ads calls it the conversion window, set per conversion action. The default for clicks is 30 days, shorter for engaged-view and view-through. Two points matter:

1. The conversion is recorded on the click date, not the purchase date. A customer who clicked on the 1st and bought on the 12th shows a conversion in the report for the 1st. Past days keep changing for weeks, so judging the last 24 hours, or even the last week for long-decision products, is unreliable.
2. The attribution model inside Google: the default today is data-driven, distributing credit across multiple Google clicks (Search, Shopping, YouTube), but it does not see Meta or WhatsApp. Even Google’s internal attribution cannot be compared with another platform.

Setting up conversions correctly with the right window is covered in Google Ads conversion tracking on Shopify.

GA4 attribution: the wider picture and smaller numbers

GA4 is fundamentally different: it sees every channel the customer passed through (an Instagram ad, organic search, a direct visit, a WhatsApp link, a Google ad) and distributes credit between them with a data-driven model. As a result each channel’s share in GA4 is smaller than the platform’s own number, which is not an error but the nature of distribution. GA4 also loses some sessions to cookies and consent and does not see view-through conversions, so it is best for comparing channels relatively, not for counting sales.

Source	What it sees	Nature of the number
Meta Ads Manager	Meta interactions only, clicks and views	Usually the highest
Google Ads	Google clicks only	High on search and brand terms
GA4	All channels, last click or data-driven	Smaller per channel
Shopify	Every real order	The truth

Why platforms over-claim (in good faith)

• Overlap: one customer touches several platforms and each counts them.
• Views without clicks: counted on Meta within 24 hours.
• Returning customers: a fifth-time buyer who saw a retargeting ad is credited to the ad, although they would most likely have bought anyway.
• Brand search: a search ad on your store name captures customers who came because of Meta or WhatsApp.
• Cancelled and rejected orders: platforms count the purchase event at the moment of checkout and never see the later cancellation or cash-on-delivery rejection.

Practical rules for reading overlap

1. Never add platforms together. Calculate MER from store sales divided by total spend, as explained in MER vs ROAS.
2. Compare inside the platform only. The Meta window is fine for ranking Meta campaigns against each other, not for comparing Meta with Google.
3. Fix the window when comparing over time. Do not compare a month under a 7-day window with a month under a 1-day window.
4. Read click and view separately. A low click-to-view ratio in a broad campaign signals reliance on impressions alone.
5. Wait for numbers to settle. On Google, do not judge a period before its conversion window has passed.
6. Test with gradual reductions, not full pauses. If you suspect a channel is over-claiming, reduce its budget gradually and watch store sales, not platform conversions. This is an unbreakable rule in our management: no winning campaign is paused without evidence from the store.
7. Put everything in one report. The monthly eCommerce performance report shows platform numbers as direction and settles profitability with store numbers.

From our experience

At Xmart, the electronics store, attributed Meta ROAS reached 11x in Q2 2025, an excellent figure inside Meta’s window. But the decision to scale was not built on it alone; it was built on blended MER from store sales across Meta and Google together, which exceeded 10x in September 2026 with hundreds of orders a month. The gap between the two numbers is exactly the overlap this article describes: when blended MER stays close to platform ROAS, overlap is limited and the channels genuinely add to each other. Details are in the Xmart case study.

E-SAFQA’s tracking and analytics service starts by comparing each platform’s numbers with store orders, so you know the size of overlap in your specific account instead of relying on general rules.

Conclusion

Attribution windows explain why every platform looks better than it is: each counts the order it touched within its window without seeing the others. Read Meta within its window to compare its campaigns, Google within its window to compare its campaigns, GA4 to compare channels relatively, and judge profitability by MER from store sales alone.

Send us last month’s numbers from Meta, Google and Shopify on WhatsApp and we will calculate the overlap in your account and your real MER.