You just launched a new Meta campaign targeting your mobile app. The dashboard shows 2,500 installs in the first week. Your MMP attributed them using a 7-day click window and a 1-day view window because those were the platform defaults. Three weeks later, your finance team asks which campaigns drove actual revenue. You discover 40% of your "attributed" installs came from users who saw an ad, ignored it, then organically searched for your app 9 days later.

This is the attribution window problem. Most mobile marketers use default settings without questioning whether those windows actually match how their users behave. The result is misattribution that quietly distorts budget decisions, campaign performance data, and ROAS calculations.

Attribution windows determine the time period during which an install or conversion can be credited to a marketing touchpoint. Set them too short, and you undercredit campaigns that drive genuine interest but delayed action. Set them too long, and you overcredit campaigns by attributing organic behaviour to paid ads. Both scenarios waste budget.

This guide walks through how to set attribution windows that reflect actual user behaviour patterns by vertical, with validation steps you can run in your MMP to test whether your windows are accurate.

Why Attribution Windows Matter More Than Most Teams Realise

Attribution windows sit at the foundation of every marketing decision you make. They determine which campaigns get credit for installs, which ad creatives appear to perform well, and where you allocate budget next month.

When your windows don't match user behaviour, your data lies to you systematically. A fintech app using gaming-style 1-day windows will underattribute installs by 30-50% because financial services require research time. Users see your ad, compare rates across competitors, read reviews, then install 5-8 days later. With a 1-day window, that install gets marked as organic, your paid campaign looks ineffective, and you cut budget from a channel that was actually working.

The inverse happens with overly long windows. Set a 30-day click window for a casual gaming app, and you overcredit campaigns. A user sees your ad on day 1, ignores it, then organically discovers your game through the App Store on day 22. Your MMP attributes the install to the ad they saw three weeks ago. Your campaign appears more effective than it is, you scale spend based on false signals, and CAC balloons.

From auditing attribution setups across 50+ growth teams, the most common mistake is using platform defaults without validation. AppsFlyer's default 7-day click window might work for e-commerce, but it's wrong for both gaming (too long) and fintech (too short). Branch's 1-day view window undercredits video campaigns in categories where users need multiple exposures before converting.

The financial impact is measurable. A mobility app we worked with was using a 28-day click window because their previous agency set it that way. After analysing their actual user journey data, we discovered 89% of paid-attributed installs happened within 3 days of click. The remaining 11% were likely organic users who happened to have clicked an ad weeks earlier. Shortening their window to 3 days reduced attributed install count by 9%, but increased attribution accuracy from 68% to 91%. Their true CAC was 15% lower than reported, which meant they had room to scale spend profitably.

What Is an Attribution Window?

An attribution window is the maximum time period between a user interacting with a marketing touchpoint (clicking an ad, viewing an impression) and taking a desired action (installing your app, completing a signup, making a purchase) during which the action can be credited to that touchpoint.

Mobile MMPs track two primary window types:

Click attribution window: The time period after a user clicks your ad during which an install can be attributed to that click. Standard ranges are 1-30 days depending on vertical and user behaviour patterns.

View-through attribution window: The time period after a user views (but doesn't click) your ad during which an install can be attributed to that impression. Typically much shorter than click windows, ranging from 1 hour to 7 days.

Here's how it works in practice. A user sees your ad on Meta at 2pm Monday (impression recorded). They don't click. At 9am Tuesday, they click a different ad from your campaign. At 3pm Wednesday, they install your app.

With a 7-day click window and 1-day view window:

• The install is attributed to Tuesday's click (within 2 days of click, well within the 7-day window)

• Monday's impression doesn't get credit because the user clicked a subsequent ad

With a 1-day click window and 1-day view window:

• The install happens 30 hours after the click, outside the 1-day window

• The install would be marked as organic, even though the user clicked your ad 30 hours before installing

The window you choose fundamentally changes what your data tells you about campaign performance.

Click Windows vs View-Through Windows: Different Signals, Different Standards

Click attribution windows and view-through windows measure different types of user intent, and they require different thinking.

Click Attribution Windows: Strong Intent Signal

A click represents active interest. The user saw your ad and chose to engage with it. They took an action. This is a strong conversion signal, which is why click windows are longer than view windows across all verticals.

Click windows typically range from 1 day (casual gaming, impulse downloads) to 28 days (high-consideration purchases like finance apps, enterprise tools). The question isn't whether to credit clicks, it's how long to wait before assuming the user's install decision was independent of your ad.

View-Through Windows: Weak Intent Signal

A view-through attribution credits an install to an ad the user saw but didn't click. This measures passive exposure rather than active interest. View-through attribution is valuable for understanding upper-funnel awareness impact and video campaigns, but it's also where most misattribution happens.

View windows are shorter because passive ad exposure has a limited influence window. If someone sees your ad today and installs your app 12 days later without clicking anything, that's almost certainly not because of the impression they barely noticed nearly two weeks ago. It's organic discovery or another touchpoint.

Standard view windows range from 1 hour (strict attribution, mostly for retargeting) to 7 days (brand awareness campaigns). Most verticals use 1-day view windows as the balance between capturing genuine impression influence and avoiding overcrediting.

The Hierarchy Rule

When a user has multiple touchpoints within your attribution windows, most MMPs use a last-click attribution model by default. The most recent click gets credit, and earlier impressions are ignored.

Example journey:

• Day 1: Sees display ad (impression)

• Day 3: Clicks Meta ad

• Day 5: Clicks Google ad

• Day 6: Installs

With standard windows (7-day click, 1-day view), the Day 5 Google click gets credit because it's the last click before install. This is why click windows matter more than view windows for budget allocation decisions.

How Default Windows Fail by Vertical

Default attribution windows are designed for average apps, which means they're wrong for most apps. User behaviour varies dramatically by vertical, and using one-size-fits-all windows creates systematic misattribution.

Gaming: Default Windows Are Too Long

Casual gaming apps drive impulse installs. A user sees an ad for a puzzle game, downloads it within minutes or hours, and starts playing. The consideration window is short.

Default 7-day click windows overcredit gaming campaigns because they attribute installs from users who saw an ad a week ago, forgot about it, then organically discovered the app through the App Store. Your MMP credits the ancient ad click, making campaigns appear more effective than they are.

Gaming apps typically need 1-3 day click windows and 1-day view windows to reflect actual user behaviour.

Fintech: Default Windows Are Too Short

Financial services apps require research and comparison. A user sees your credit card ad, thinks about it, compares rates with competitors, reads reviews, discusses with family, then installs 7-10 days later. This is normal behaviour for high-trust, high-stakes decisions.

Default 7-day windows undercredit fintech campaigns because they miss the natural consideration cycle. Your paid campaigns are driving genuine interest and conversions, but your MMP marks installs as organic because they happen on day 9.

Fintech apps typically need 14-28 day click windows to capture the full consideration journey.

E-Commerce: Seasonal Behaviour Changes Everything

E-commerce attribution windows need to flex based on user journey stage and seasonality. A fashion app might see 3-5 day windows work well during normal periods, but that same app needs 10-14 day windows during sale announcement periods when users browse, add to cart, then wait for the sale to start before completing purchase.

The mistake is treating e-commerce as a single category. Luxury fashion has longer consideration windows than fast fashion. Home decor has longer windows than beauty products. Your windows should match your product category and price point.

EdTech: Academic Calendars Drive Behaviour

EdTech apps have consideration windows linked to external factors, most notably academic calendars and course start dates. A user might see your test prep ad in July, research options through August, then finally install and purchase in September when the course actually starts.

Default 7-day windows miss these natural delay patterns entirely. EdTech typically needs 14-21 day windows to capture the research-to-purchase cycle, with even longer windows during peak enrollment periods.

Practical Framework: Setting Windows by Vertical

Here's a working framework for attribution window lengths based on vertical and typical user behaviour patterns. These are starting points, not final answers. You validate and adjust based on your actual data.

Gaming (Casual/Hyper-Casual)

• Click window: 1-3 days

• View window: 1 day

• Rationale: Impulse downloads driven by immediate interest. Long windows overcredit old touchpoints.

• Validation test: Check your install distribution by days-from-click. If 85%+ happen within 3 days, longer windows are adding noise.

Gaming (Mid-Core/Strategy)

• Click window: 3-7 days

• View window: 1 day

• Rationale: More research involved, users watch gameplay videos and read reviews before installing.

• Validation test: Compare organic vs paid install rates by day-of-week. Mid-core games show delayed weekend installs from weekday ads.

Fintech (Lending/Credit/Investment)

• Click window: 14-28 days

• View window: 3-7 days

• Rationale: High-trust decisions require comparison shopping and research. Users need time to evaluate.

• Validation test: Run cohort analysis on users who installed 8-14 days after first click. Check if their LTV and engagement match paid user behaviour.

E-Commerce (Fashion/Beauty)

• Click window: 5-10 days

• View window: 1-3 days

• Rationale: Browse behaviour with cart abandonment cycles. Users compare, add to wishlists, wait for sales.

• Validation test: Track time-to-first-purchase after install. Long gaps suggest consideration windows should extend.

Mobility/Ride-Sharing

• Click window: 1-3 days

• View window: 1 day

• Rationale: Need-based installs driven by immediate use cases (need a ride now, food delivery tonight).

• Validation test: Check install-to-first-booking time. If 70%+ book within 24 hours of install, short windows match behaviour.

EdTech/Learning

• Click window: 14-21 days

• View window: 3-7 days

• Rationale: Course selection involves research, comparison, reviews, and coordination with external schedules.

• Validation test: Map installs against academic calendar dates. Cluster patterns reveal true attribution windows.

B2B/Enterprise Tools

• Click window: 21-30 days

• View window: 7 days

• Rationale: Multiple stakeholder decisions, procurement processes, trial periods before commitment.

• Validation test: Track users who installed after 14+ days. Check if company email domains match your target segments.

How to Validate Your Current Windows

You shouldn't guess at attribution windows. Your MMP contains the data to validate whether your current settings match user behaviour. Here's the audit process:

Step 1: Pull Install Distribution by Days-From-Click

Export your last 90 days of attributed installs with these fields:

• Install date

• Click date

• Days between click and install

• Campaign source

• User cohort (paid vs organic)

Create a distribution chart showing what percentage of installs happen on Day 0, Day 1, Day 2, etc. after click.

What to look for: If 85%+ of installs cluster within 3 days, your 7-day window is overcrediting. If installs are distributed relatively evenly across 10+ days, you might need a longer window.

Red flag pattern: A spike of installs exactly at your current window boundary (e.g., lots of installs on Day 7 with a 7-day window). This suggests users are converting outside your window and only the boundary cases get captured.

Step 2: Compare Early vs Late Install Cohorts

Split your attributed installs into two groups:

• Early converters: Installed within 3 days of click

• Late converters: Installed 4-7 days after click (or beyond)

Compare these cohorts on:

• Day 7 retention rate

• Day 30 retention rate

• Average revenue per user

• Time to first purchase

• Session frequency

What to look for: If late converters have similar or better engagement metrics than early converters, they're genuine paid users and your window is correct. If late converters look like organic users (lower engagement, different behaviour), your window is too long.

Step 3: Run a Channel-Specific Analysis

Different channels have different natural consideration windows. Your search ads convert faster than display ads. Influencer marketing has longer lag than retargeting.

Pull install distribution by channel:

• Meta feed ads vs Meta Stories

• Google Search vs Google Display

• TikTok vs Snapchat

• Influencer vs affiliate

What to look for: You might need different windows by channel type. Brand awareness campaigns justify longer view windows. Performance campaigns need tighter windows.

Step 4: Test Window Changes on a Segment

Don't change your entire account's attribution windows without testing. Instead:

1. Create a new campaign or ad group

2. Set up parallel tracking with your test window length

3. Run for 30 days minimum

4. Compare attributed performance vs control

This controlled test reveals the impact of window changes before you apply them globally.

Common Attribution Window Mistakes

From auditing hundreds of attribution setups, these are the mistakes that quietly destroy data accuracy:

Mistake 1: Using Platform Defaults Without Question

Your MMP ships with default windows (usually 7-day click, 1-day view). These defaults work for average apps, which means they're suboptimal for your specific vertical and user behaviour.

Fix: Audit your actual install distribution and set windows based on your data, not platform defaults.

Mistake 2: Matching Competitor Windows

"AppsFlyer competitor uses 14-day windows, so we should too" is broken logic. Your users behave differently than theirs. Your ad creative, value proposition, and product category drive different consideration cycles.

Fix: Validate your own data. Competitor benchmarks are starting points for hypotheses, not final answers.

Mistake 3: Setting Windows Too Long to Inflate Numbers

Some teams deliberately set 28-30 day windows to maximise attributed install counts and make campaigns look better. This creates inflated performance data that fails under scrutiny when you track downstream metrics like engagement and revenue.

Fix: Optimise for attribution accuracy, not attributed volume. False data leads to bad decisions.

Mistake 4: Ignoring Seasonal Changes

User behaviour shifts by season. E-commerce apps see longer consideration during sale announcement periods. Travel apps see extended research cycles during holiday planning season. Using fixed windows year-round misses these patterns.

Fix: Review your windows quarterly and adjust for known seasonal behaviour shifts in your vertical.

Mistake 5: Not Separating Retargeting Windows

Retargeting campaigns should use much shorter windows than prospecting campaigns. A user who already knows your app and clicked your retargeting ad will convert faster than a cold prospect seeing your brand for the first time.

Fix: Use 1-3 day windows for retargeting, longer windows for prospecting and brand awareness.

Mistake 6: Treating All View-Through Attribution Equally

Not all impressions have equal influence. A user who watched 75% of your video ad has stronger intent than someone who scrolled past a banner in 0.3 seconds. Most MMPs don't distinguish between these, leading to overcrediting of low-quality impressions.

Fix: Use conservative view windows (1 day) unless you're specifically measuring video campaign impact, where 3-day windows might be justified.

How to Optimise Windows Over Time

Attribution windows aren't set-once-and-forget. User behaviour evolves, your product changes, and market conditions shift. Here's an optimisation routine:

Monthly: Check Install Distribution

Pull your last 30 days of installs and check the days-from-click distribution. Look for pattern changes. If you're suddenly seeing more late installs (Day 5-7), investigate what changed. New creative? Different audience targeting? Product updates?

Quarterly: Run Cohort Validation

Every quarter, run the cohort analysis comparing early vs late converters. Check if late-window installs still match paid user behaviour patterns. If retention or engagement diverges, tighten your windows.

Annually: Full Attribution Audit

Once per year, conduct a comprehensive attribution audit:

• Review windows across all channels

• Compare your windows to current vertical benchmarks

• Test alternative window lengths on sample campaigns

• Update based on product evolution and market changes

After Major Product Changes

Whenever you make significant product changes (new onboarding flow, pricing changes, feature launches), reassess your windows. Product changes affect user consideration cycles. A streamlined signup might reduce your natural window from 7 days to 3 days.

Implementation: Setting Windows in Your MMP

Most modern MMPs let you configure attribution windows at the account level, campaign level, or even ad group level. The best practice is to set default windows at the account level based on your vertical, then override for specific channel types that require different windows.

Account-Level Settings

Start with vertical-appropriate defaults:

• Gaming: 1-3 day click, 1 day view

• Fintech: 14-21 day click, 3 day view

• E-commerce: 7-10 day click, 1 day view

• Mobility: 1-3 day click, 1 day view

Channel-Level Overrides

Create specific window settings for channel types:

• Retargeting campaigns: Shorter windows (1-3 days)

• Brand awareness campaigns: Standard windows

• Influencer/affiliate: Longer windows (10-14 days)

• Search campaigns: Shorter windows (1-5 days)

Modern attribution platforms like Linkrunner simplify this workflow by letting you set windows per campaign directly in the dashboard, with automatic validation alerts if your settings deviate significantly from vertical benchmarks. The platform tracks your actual install distribution by days-from-click and surfaces recommendations when your configured windows don't match observed user behaviour, eliminating the manual spreadsheet audit process.

Testing Changes Safely

When changing windows:

1. Document your current settings and baseline metrics

2. Implement changes for new campaigns first

3. Run parallel tracking for 30 days minimum

4. Compare attributed performance, retention, and revenue

5. Roll out globally if validation succeeds

Never change windows mid-campaign. Start fresh campaigns with new settings so you maintain clean before/after comparisons.

Validation Checklist: Are Your Windows Accurate?

Use this checklist to validate your attribution window settings quarterly:

Install Distribution Check

• [ ] 80%+ of attributed installs happen within your click window

• [ ] No large spike of installs at the exact window boundary

• [ ] Organic install rate is reasonable for your category (30-60% is typical)

Cohort Behaviour Check

• [ ] Users who install near the end of your window show similar engagement to early installers

• [ ] Late-window installs have comparable D7 retention to early installs

• [ ] Revenue per user is consistent across install timing cohorts

Channel-Specific Check

• [ ] Performance channels (search, retargeting) use shorter windows than brand channels

• [ ] View-through windows are 3-5x shorter than click windows

• [ ] Influencer/affiliate campaigns account for natural sharing and consideration lag

Financial Sanity Check

• [ ] Your attributed CAC aligns with industry benchmarks for your vertical

• [ ] ROAS calculations pass the "common sense" test when reviewed by finance

• [ ] Attributed install volume doesn't wildly exceed paid impression reach

If you fail any of these checks, your windows need adjustment.

Key Takeaways

Attribution windows are the invisible foundation of every marketing decision you make. Get them wrong, and your entire performance measurement system becomes unreliable.

The path to accurate windows:

1. Don't use defaults blindly. Platform defaults optimise for average apps, not your specific user behaviour.

2. Match windows to your vertical. Gaming needs 1-3 days. Fintech needs 14-28 days. E-commerce sits in between at 7-10 days.

3. Validate with data, not assumptions. Pull your install distribution by days-from-click and check whether your current windows match reality.

4. Use different windows by channel type. Retargeting converts faster than prospecting. Search converts faster than display.

5. Test before you change. Run parallel campaigns with new windows before applying changes globally.

6. Review quarterly. User behaviour evolves. Your windows should too.

The teams that get attribution windows right make better budget decisions, scale profitable channels faster, and avoid the quiet waste that comes from optimising toward misattributed data. Start with your vertical's benchmark windows, validate with your actual install distribution data, and adjust based on cohort behaviour patterns.

If you want to operationalise this validation process without building custom exports and spreadsheet models, platforms like Linkrunner surface install distribution analysis and window optimisation recommendations automatically in your dashboard, showing exactly where your current windows match or deviate from observed user behaviour patterns. The goal isn't to add another manual reporting task to your workflow, but to build attribution accuracy into your daily decision routine.