Attribution Windows Guide: How Click and View Windows Affect Your Ad Data

An attribution window is the time period during which a conversion gets credited to an ad. If someone clicks your ad on Monday and purchases on Thursday, whether that purchase counts as an ad-driven conversion depends on how long your attribution window is. A 7-day click window would count it. A 1-day click window would not.

This sounds like a technicality, but it shapes everything you think you know about ad performance. Change the window and your reported ROAS changes, even though actual sales did not. Compare platforms with different default windows and you get an apples-to-oranges comparison that makes some channels look better or worse than they are.

This guide covers how attribution windows work, why they differ across platforms, how window choice affects reported performance, how to compare channels fairly, and how to set windows that match your business reality.

What attribution windows measure

Attribution answers the question: which ad caused this conversion? A window defines how far back in time you are willing to look.

Click-through attribution. The conversion is credited to an ad if the user clicked it within the window. A 7-day click window means clicks from the last 7 days can receive credit.

View-through attribution. The conversion is credited to an ad if the user saw it (but did not click) within the window. A 1-day view window means impressions from the last 24 hours can receive credit.

Most platforms report both, but the defaults and options vary. View-through attribution is more controversial because the user never actively engaged. They may have scrolled past the ad without noticing it, then purchased for unrelated reasons.

Default windows by platform

Each ad platform has its own default attribution settings.

Meta (Facebook and Instagram). Default is 7-day click, 1-day view. This means a conversion is credited to Meta if the user clicked an ad within the last 7 days OR viewed an ad within the last 1 day (without clicking). You can change this to 1-day click, 7-day click, or combinations with or without view-through.

Google Ads. Defaults vary by campaign type. Search campaigns often use 30-day click. Performance Max uses data-driven attribution with a 30-day window. YouTube has its own view-through settings.

ChatGPT Ads. Currently uses click-based attribution with a window that may vary. Check the OpenAI Ads Manager for current settings.

Pinterest. Default is 30-day click, 1-day view. Longer click windows mean more conversions get credited.

TikTok. Default is 7-day click, 1-day view, similar to Meta.

Snapchat. Default is 28-day click, 1-day view.

These differences matter when comparing platforms. A channel with a 30-day click window will naturally claim more conversions than one with a 7-day window, even if actual influence is similar.

Why window length matters

Longer windows credit more conversions to ads. Shorter windows credit fewer. Neither is "right" in absolute terms, but each tells a different story.

Longer windows capture delayed purchases. Some products have long consideration cycles. A buyer might click your ad, research for two weeks, and then purchase. A 7-day window misses that conversion. A 30-day window catches it.

Shorter windows are more conservative. They credit conversions only when the ad interaction was recent, making it more likely the ad actually influenced the decision.

View-through is the most generous. Crediting conversions to ad impressions (without clicks) inflates numbers because many of those impressions may not have been noticed or influential.

Hypothetical example: A furniture brand runs Meta ads. With 7-day click, 1-day view, they report 100 purchases attributed to ads. Switch to 1-day click only, and the number drops to 40. The same actual purchases happened, but the reported ad performance looks very different.

This is why you cannot take reported ROAS at face value. The window setting is part of the equation.

Comparing channels with different windows

When platforms have different default windows, direct comparison is misleading.

Standardize windows where possible. If you are comparing Meta and TikTok, set both to 7-day click, 1-day view (or 1-day click only if you prefer conservative measurement). This gives you an apples-to-apples comparison.

Document your settings. Know what window each platform is using when you report performance. "Meta ROAS 3.2x (7dc/1dv)" is more useful than "Meta ROAS 3.2x" because it specifies the attribution context.

Beware platform incentives. Platforms benefit from claiming more conversions, so defaults tend toward longer, more generous windows. Understand that Meta's default settings are designed to make Meta look good.

Use blended metrics as a check. Marketing Efficiency Ratio (MER), which is total revenue divided by total ad spend, does not depend on any single platform's attribution. If your platforms collectively report great ROAS but MER is declining, the platform numbers may be overcounting. See our MER guide for how to calculate this.

View-through attribution: handle with care

View-through conversions credit ads for purchases where the user saw but did not click the ad. This is useful for understanding awareness impact, but it can also inflate performance numbers.

Why it exists. Display and video ads are often seen without being clicked. Someone sees your ad, remembers your brand, and later types your URL directly or searches your brand name. View-through attribution tries to capture this influence.

Why it is risky. Not all impressions are equal. A user might scroll past your ad in 0.3 seconds without processing it. Crediting that impression with a conversion overstates ad influence.

When to include it. For awareness campaigns (video views, reach), view-through helps you understand whether impressions are correlated with purchases. For conversion campaigns, it can inflate numbers and mislead optimization.

When to exclude it. If you want conservative measurement that credits only active engagement, use click-only attribution. This is especially useful for comparing channels or making budget decisions.

Many advertisers report both: "7-day click ROAS of 2.5x, plus an additional 0.8x from view-through." This separates the more certain click attribution from the softer view attribution.

Setting windows that match your business

The right attribution window depends on how your customers buy.

Short consideration cycles. For impulse purchases, consumables, and low-priced items, a 1-day or 7-day click window often captures most genuine ad influence. Buyers decide quickly, so distant clicks are less likely to matter.

Long consideration cycles. For furniture, mattresses, high-end electronics, and B2B software, buyers research for weeks. A 7-day window may miss conversions that a 14-day or 30-day window would capture. Match the window to your average purchase timeline.

Check your data. Look at the time between first click and purchase in your analytics. If most purchases happen within 3 days of clicking an ad, a 7-day window is sufficient. If many happen between 10 and 20 days, consider a longer window.

Consider customer journey complexity. If buyers typically interact with multiple channels before purchasing, shorter windows may undercount each channel's contribution because credit goes to whoever touched last within the window.

Our guide to attribution for AI channels covers how these considerations apply to newer platforms like ChatGPT.

Multi-touch attribution versus last-click

Most platform-reported attribution is last-click (or last-touch) within the window: the final ad interaction gets all the credit. But buyers often see multiple ads before purchasing.

Last-click overstates closers. The ad that happened to be last gets 100% credit, even if earlier ads did most of the persuading.

Multi-touch attribution (MTA) distributes credit across touchpoints. A buyer who saw a Facebook video ad, then clicked a Google search ad, then purchased might have credit split 40/60 or 30/30/40 if a retargeting ad also contributed.

MTA is harder to implement. It requires data from all platforms, a system to connect user identity across them, and a model to decide how to allocate credit. Most small brands do not have this infrastructure.

Practical compromise. Accept that platform-reported numbers are directional, not precise. Use blended metrics (MER, overall CAC) as the source of truth, and use platform attribution to understand relative performance within each channel.

Our marketing mix modeling guide covers alternatives to click-based attribution for measuring channel contribution.

The iOS 14+ complication

Apple's App Tracking Transparency (ATT) reduced the data available for attribution on Meta and other platforms. Many users opt out of tracking, which means their conversions cannot be matched back to ad clicks.

Modeled conversions. Meta now reports modeled conversions to fill gaps, using statistical methods to estimate what would have happened if full tracking existed. This adds uncertainty to reported numbers.

Shorter windows became more common. With less tracking data, long attribution windows have more gaps. Some advertisers shifted to shorter windows where tracking is more reliable.

Aggregated measurement. Meta's Aggregated Event Measurement limits the events you can optimize for and report on, further affecting how attribution works.

The practical impact: platform-reported attribution is less complete than it was before iOS 14. This makes blended metrics and incrementality testing more important as crosschecks. Our incrementality testing guide explains how to measure true ad impact beyond attribution.

Using UTM parameters for independent tracking

You can track ad performance independently of platform attribution using UTM parameters in your landing page URLs.

UTM basics. Add parameters like utm_source=meta&utm_medium=paid&utm_campaign=spring-promo to your ad URLs. Your analytics tool (Google Analytics, for example) will record these and attribute conversions to the tagged source.

Benefits. UTM tracking is platform-agnostic. It uses your own data, not the ad platform's reported data. You can compare channels on the same footing.

Limitations. UTM tracking is also last-click. It credits the final clicked link before purchase, which may not capture view-through or multi-touch influence. Users who clear cookies or switch devices break the trail.

Our UTM parameters guide covers how to set up and use UTMs effectively.

Post-purchase surveys as an attribution supplement

Another way to understand attribution is to ask customers directly: "How did you hear about us?"

Simple to implement. Add a question to your checkout confirmation or post-purchase email with options like "Facebook/Instagram ad," "Google search," "Friend recommendation," "Podcast," "ChatGPT," etc.

Captures influence, not just clicks. A customer might have seen your podcast ad, searched your brand name, and clicked a Google ad. Platform attribution credits Google; the survey might credit the podcast.

Limitations. Customers do not always remember accurately. Some never respond. Results are directional, not precise.

Use as a supplement. Compare survey data to platform-reported attribution. If Meta claims 60% of conversions but surveys show 30% of customers remember seeing a Meta ad, there may be over-attribution happening.

Our post-purchase survey attribution guide covers implementation details.

How SecondWin approaches measurement

SecondWin writes and runs ChatGPT ads. It tracks performance using OpenAI's reporting, which includes click-based attribution. For a complete picture, SecondWin recommends using UTM parameters to track ChatGPT traffic in your own analytics and adding post-purchase survey options that include ChatGPT.

Because ChatGPT is a newer channel, you may not have historical data to compare. SecondWin suggests watching blended MER as you add ChatGPT spend: if total efficiency holds or improves while you scale, the channel is contributing even if last-click attribution looks modest.

Run a free analysis of your store or see plans and pricing. SecondWin is independent and not affiliated with OpenAI.

FAQ

What attribution window should I use on Meta?

Start with the default 7-day click, 1-day view if you want to see what Meta is reporting by default. For more conservative measurement, switch to 7-day click only (no view-through) or 1-day click only. Match the window to your product's typical consideration time: shorter for impulse buys, longer for considered purchases.

Why does my ROAS look different when I change the attribution window?

Because the window determines which conversions get credited. A longer window includes more conversions (clicks from further back in time still count), so ROAS goes up. A shorter window excludes those delayed conversions, so ROAS goes down. The actual purchases are the same; only the reported attribution changes.

Should I include view-through conversions when measuring performance?

It depends on your goals. View-through shows the correlation between ad impressions and purchases, which is useful for understanding awareness impact. But it can inflate numbers because not all impressions are genuinely influential. For conservative budgeting decisions, consider reporting click-only attribution separately.

How do I compare performance across channels with different attribution windows?

Standardize windows where possible. If Meta is 7-day click and Pinterest is 30-day click by default, set Pinterest to 7-day click before comparing. If you cannot standardize, use blended metrics (MER, total CAC) as the common baseline and treat platform-specific ROAS as directional, not definitive.

Why is attribution harder after iOS 14?

Apple's App Tracking Transparency lets users opt out of cross-app tracking. When users opt out, ad platforms cannot match their conversions back to ad clicks. Meta and others now model some conversions statistically, adding uncertainty. This makes blended measurement and incrementality testing more important as crosschecks against platform-reported data.