Attribution Windows: 1-Day Click, 7-Day View, and Why Your Numbers Moved
The 60-second version
Every ad platform ships a different default attribution window. Here is what each window setting actually changes, with worked examples.
- Click windows vs. view-through windows
- Platform defaults compared
- What changes when you shorten a window
Nobody changed spend. Nobody changed creative. Reported conversions fell 28% overnight — and the only thing that happened was someone clicked "7-day click" instead of "7-day click + 1-day view" in the ad account settings.
No customer behaviour changed. The attribution window changed: the rulebook that decides how long after an ad interaction a purchase still counts as "caused by the ad." Every platform ships a different default, every window quietly rewrites history when it moves, and most teams discover this the week they try to compare two channels — or two months — measured under different rules.
Click windows vs view-through windows
Two kinds of credit exist, and they differ wildly in strength.
The Two Kinds of Attribution Credit
Data JourneyClick-through
Customer clicked the ad, then bought within N days. Intent is demonstrated — they interrupted what they were doing to look at your product.
View-through
Customer merely saw the ad (it rendered on screen), did not click, then bought within N days/hours. Correlation is real; causation is often imaginary.
The asymmetry
Widening a click window adds real-but-delayed buyers. Adding a view window adds mostly people who would have bought anyway — and claims them.
A concrete illustration. A footwear brand spends ₹5,00,000 on Meta in a fortnight and the warehouse records 1,000 orders from customers exposed to those ads. Under a strict 1-day-click rule, 210 orders count — customers who clicked and bought within a day. Widen to 7-day click and 340 count, because considered purchases take days. Add 1-day view and the number jumps to 520: 180 more people who saw an impression, never clicked, and bought within 24 hours — many of them brand searchers and past visitors who would have converted through some other touchpoint anyway.
Same spend, same customers, three conversion counts (210 / 340 / 520) and three reported CACs (₹2,381 / ₹1,471 / ₹962). The attribution window did not measure differently — it defined differently. A view-through conversion is not a weaker measurement of the same thing; it is a claim about a different, far looser causal relationship.
View-through credit is where double-counting lives. A customer who saw a Meta impression, ignored it, then clicked a Google brand ad and bought is a click conversion for Google and a view-through for Meta. Both platforms report a conversion. Your warehouse sees one order. Every cross-platform ROAS comparison that ignores this is counting the same revenue twice.
Platform defaults compared
Each platform chose the default that makes its own inventory look best — which is precisely why defaults differ.
| Platform | Default click window | Default view window | What it favours |
|---|---|---|---|
| Meta Ads | 7-day click | 1-day view | Discovery inventory; claims delayed + passive buyers |
| Google Ads (Search) | 30-day click | None by default | High-intent capture; long consideration looks brilliant |
| Google (YouTube / Demand Gen) | 30-day click | View-through optional | Video views claimed as conversions when enabled |
| TikTok Ads | 7-day click | 1-day view | Same playbook as Meta — view credit inflates heavily |
| Snap / programmatic display | 28-day click | Up to 28-day view | Longest, loosest; numbers rarely comparable to anything |
Read the incentives. Search gets a 30-day click window because someone who clicked once and bought three weeks later was clearly high-intent — and a long window captures all of it. Social gets a 1-day view bolted on because its inventory is mostly scrolled past, not clicked; without view credit, social's reported conversions would collapse. Neither default is dishonest. Both are self-serving, and comparing a 30-day-click Search ROAS against a 7-day-click-plus-view Meta ROAS is comparing a month of harvesting against a week of prospecting plus a day of bystanders.
For Indian D2C there is an extra wrinkle: conversion lag interacts with COD. A customer clicks on Monday, places a COD order on Wednesday, and it RTOs the following week. Under a 7-day-click window the platform keeps the conversion; your books never see revenue. The longer the window, the more unconfirmed COD intent it sweeps in — another reason to reconcile platform conversions against delivered orders, not placed ones.
What changes when you shorten a window
Shortening a window does three things at once, and only the first is intended.
Footwear brand cuts Meta from 7-day-click + 1-day-view to 7-day-click only
The intended effect is honesty: the remaining 340 conversions are demonstrably stronger-causal than the 180 that left. The unintended effects are operational. Smart bidding and Advantage+ treat attributed conversions as ground truth, so narrowing the window starves the optimiser — expect a 7–14 day re-learning wobble with higher CPA before it stabilises. Reporting continuity breaks: every historical comparison across the change is invalid unless you restate old periods under the new rule (export daily click-only and view-through splits for at least 60 days before switching). And team psychology takes a hit — a media buyer whose ROAS "fell" 35% overnight will claw the old window back unless leadership pre-agrees that the drop is definitional.
Always restate before you switch. Pull 60–90 days of daily conversions split by click vs view, recompute every headline under the proposed window, and socialise the "new history" first. The conversation "our ROAS was always 2.3x on this definition" is survivable; "ROAS collapsed this week" is not.
Picking a window for your business
Matching the Window to the Buying Cycle
Reporting HierarchyShort cycle (replenishment, low AOV)
Most genuine clicks convert within 1–3 days. A 7-day click captures nearly everything real; anything beyond is noise. Disable view-through for decisions.
Considered cycle (fashion, electronics, high AOV)
Comparison shopping takes a week or more. 7-day click minimum, and test 28-day click on search only. Keep view-through visible but out of targets.
Portfolio truth (finance, board)
No window at all. Warehouse new customers over total spend — blended CAC and MER — which are invariant to every platform setting.
Four practical rules. First, align the click window to your measured lag, not the default: pull the click-to-purchase distribution from your analytics (median and 90th percentile lag in days) and set the window just past the 90th percentile. A brand whose 90th percentile lag is 4 days gains nothing from 28-day click except stray credit. Second, keep view-through out of optimisation targets — visible as a diagnostic column, excluded from tROAS/tCPA signals and from any number that leaves the marketing team. Third, freeze windows during tests: any creative, audience or budget experiment run across a window change is uninterpretable, because the ruler moved mid-race. Fourth, document the windows next to every number the way you would a currency — "Meta 3.1x (7d-click)" is a measurement; "Meta 3.1x" is a rumour.
Show query
If cumulative coverage hits 90% by day 5, a 7-day click window captures your reality and anything longer only imports other channels' customers into this one's report.
Frequently asked questions
Should I just turn off view-through everywhere?
For optimisation and targets, yes — optimise bids and judge creatives on click-through only. Keep view-through visible as a diagnostic, because a sudden surge in view claims often means frequency is too high (everyone "saw" the ad) rather than performance improving. Turning it off entirely blinds you to that signal.
Why do platforms default to windows that flatter them?
Because the default is the number every casual advertiser sees, and flattering numbers retain spend. This is not a conspiracy — a 1-day view genuinely captures some real influence — but the burden is on you to choose the ruler, not to inherit it. Change the default deliberately in week one of any new account.
Can I compare ROAS across platforms with different windows?
Not directly. Either restate both to a common click-only basis (7-day click is the usual lingua franca) or stop comparing platforms on platform numbers entirely and compare them on warehouse-attributed new-customer CAC instead. The second option is more work and more honest.
How do attribution windows affect smart bidding?
Directly: the optimiser maximises whatever conversion definition you feed it. Feed it view-throughs and it will buy impressions that generate views, not buyers — frequency climbs, click quality falls, and reported CPA improves while warehouse CAC worsens. Feed it a tight click window and it buys fewer, better events. The algorithm is obedient; make sure the instruction is right.
Do iOS / privacy changes make windows less reliable?
Yes — modelled conversions now fill gaps where device-level tracking fails, and modelled volume concentrates in exactly the loosest buckets (view-through, long-click). Treat any window's modelled share as disclosed uncertainty: ask each platform what proportion of reported conversions is modelled, and discount long-window, view-heavy numbers accordingly.
The summary
- An attribution window defines conversions; it does not merely measure them. Same spend, three windows: 210 / 340 / 520 conversions and three different CACs.
- Click credit is demonstrated intent; view-through credit is mostly correlation — and the home of cross-platform double-counting.
- Platform defaults serve platform incentives: 30-day click for search, 7-day-click-plus-view for social. Never compare across them raw.
- Shortening a window buys honesty but costs signal volume, reporting continuity and a 1–2 week bidding re-learn — restate 60–90 days of history first.
- Set the click window from your measured lag distribution, keep view-through out of targets, and let blended CAC and MER carry the portfolio truth.
MER Calculator
Total revenue divided by total marketing spend — the attribution-agnostic efficiency number, plus its contribution-margin-adjusted variant.
Chinmay Raibagkar
About author →Founder of DataLens AI. He helps non-technical teams read their ad and database numbers with confidence — which number to trust, what to do next, and what to ignore.