What a 7-day click, 1-day view attribution window actually means

What does a 7-day click, 1-day view attribution window actually change, and which one should I use?

The attribution setting is a dropdown most advertisers change once, if ever, and it silently decides more of your reported performance than almost anything else in the account. It is worth ten minutes of understanding, because two of the most common reporting disputes turn out to be arguments about it in disguise.

A window is two decisions, not one

The phrase 7-day click, 1-day view names two independent rules.

The click half says: if someone clicked this ad and bought within seven days, credit the ad. That is the intuitive one, and it is the claim most people think attribution is making.

The view half says: if someone was shown this ad, did not click it, and bought within one day, credit the ad anyway. That is a real phenomenon. An ad someone scrolled past can genuinely influence a purchase they make later that evening. It is also a claim you cannot check anywhere in your own data, because nobody arrived from it, so there is no visit, no referrer and no session. Your analytics will never contain a trace of it and that absence is not a tracking failure.

The window decides which day, not just which sales

This is the part that causes real confusion, and it is the mechanism behind most day-level disagreements between a platform and a store.

Ad platforms report a conversion on the date of the ad interaction that earned the credit, not the date of the purchase. Your store records the order on the day the order was placed. So a sale placed on Monday, from a click the previous Thursday, appears on Monday in your store and on Thursday in the platform.

Widen the window and you do not just count more sales. You move them further back in the platform's report, away from the day they actually happened. Over a seven-day comparison those edge effects are a large share of the total, and they grow with your buying cycle. This is why the first move in any reconciliation should be comparing whole windows rather than individual days: a per-day gap is usually a difference of basis, not a difference of measurement.

It also means a recent window is always incomplete on the platform's side and keeps filling in for as long as the window is open. Judging yesterday's performance yesterday reads a number that is still moving.

The defaults are different on every platform, and they have changed

Meta's has moved twice, always toward shorter. It was 28-day click before Apple's App Tracking Transparency changes, then 7-day click with 1-day view. On 12 January 2026 the 7-day view and 28-day view windows were removed entirely, and a newly created ad set now defaults to 7-day click alone; 1-day view survives as an option and an ad set that already existed keeps whatever it was set to. So your account can hold several different windows at once, and a comparison that spans any of those dates compares two definitions rather than two periods.

Google's defaults vary by campaign type and are generally longer: Search conversions commonly use a 30-day click window. Video campaigns can record engaged-view conversions, where someone watched a meaningful portion without clicking and converted within a few days. Display impressions can receive credit under data-driven attribution.

The practical consequence is blunt. Two platforms reporting on different windows are not comparable, and putting their conversion counts in adjacent columns invites a conclusion neither number supports.

Changing the setting changes the history

If you widen or narrow a window and then compare the following month to the previous one, the comparison contains the settings change. Performance may have moved, the definition definitely did, and the report cannot separate them.

This is worth planning around rather than discovering. When you change a window, note the date, and treat any trend line that crosses it as two series rather than one.

How to choose one, from your own data

The right window is a property of your business, not a best practice. It is also measurable without any special tooling.

For the orders in your store, look at the time between the first touch you recorded and the purchase. Plot that distribution and find where it flattens. If most orders close within two days, a 28-day window is mostly collecting coincidences: people who saw an ad three weeks ago and would have bought anyway. If you sell something people research for a month, a 7-day window is discarding influence that genuinely happened.

Then set the window to cover the bulk of the real distribution and stop moving it, because the stability is worth more than the precision.

The comparison that survives all of this

Every problem above comes from comparing two numbers built on different rules. There is one number in the picture with no window inside it at all: the orders your store actually settled. It does not depend on a click window, a view window, a model or a reporting basis. It is simply the list of sales that happened.

Anchoring there does not make the platform reports wrong or unnecessary. It gives them something to be checked against. And it makes one test available that no window setting can distort: a platform can legitimately resolve a buyer you never saw, but no platform can resolve an order that does not exist, so the total of all platform claims for a window has a ceiling, and claims above it are provably counting the same sale more than once.