Apple's SKAN 4.0 framework sends you three separate postbacks with conversion values that mean nothing until you decode them correctly. Most teams waste weeks trying to map those values to revenue, only to realize their ROAS calculations have been wrong the entire time.

This guide walks you through how SKAN 4.0 actually works, from postback timing and crowd anonymity thresholds to building conversion schemas that translate Apple's privacy-safe signals into actionable campaign data. You'll see exactly how to implement SKAN decoding, optimize campaigns with limited data, and unify iOS attribution with your Android and web funnels.

What SKAN 4.0 Changes For App Marketers

SKAN 4.0 sends you three separate postbacks instead of one. The first arrives 0-2 days after install, the second between 3-7 days, and the third anywhere from 8-35 days out. Each postback tells you something different about user behavior, though Apple delays every single one by 24-48 hours to protect privacy.

The framework also tracks users who click ads in Safari, visit your App Store page, then install your app. Before SKAN 4.0, you'd lose attribution on those web-to-app journeys entirely. Now you get credit for driving installs through web campaigns.

Apple replaced the old privacy threshold with something called "crowd anonymity tiers." Instead of data showing up or not showing up, you now get different levels of detail depending on how many installs your campaign generates.

New Postback Timeline

Your first postback can contain up to 64 different conversion values, just like SKAN 3.0. You assign values 0 through 63 to specific events: registration, first purchase, tutorial completion, whatever matters to your business. The second and third postbacks only give you three options: low, medium, or high.

This setup lets you track precise actions right after install while still getting directional signals about longer-term value. A user might trigger conversion value 45 on day one (meaning they spent $20), then show up as "high" in the day 7 postback (meaning they're still engaged and spending).

LockWindow Rules

LockWindow lets you force a postback to arrive early within its measurement window. Say you want faster feedback on install quality, you could lock the first window at 24 hours instead of waiting the full 48. The trade-off is you lose any conversion data from hours 25-48.

Most teams only use lockWindow when speed matters more than complete data. If you're testing new creative and want to kill bad performers quickly, locking at 24 hours makes sense. For campaigns optimizing toward day-7 revenue, you'd skip lockWindow and wait for the full measurement window.

Web To App Support

Safari ads that send users to your App Store page now get tracked through SKAN. The attribution works the same way as direct app install ads, you receive postbacks with conversion values and campaign data. This closes a gap for teams running display or search ads on web that drive app installs.

Why Decoding Matters For Privacy Safe ROAS

Conversion values are just numbers between 0 and 63. They mean absolutely nothing until you map them to actual business events. Value 42 could represent a $50 purchase, or it could represent someone who watched three videos, it depends entirely on how you configured your schema.

Getting the mapping wrong means your ROAS calculations fall apart. You might think a campaign is profitable when it's actually burning cash, or vice versa. The decoding step is where SKAN data becomes useful or useless.

Revenue Attribution Gaps

A lot of teams map conversion values linearly. Value 10 equals $10 in revenue, value 20 equals $20, and so on. The problem is you waste most of your 64 values on users who spend very little, then lump all high spenders together. You can't tell the difference between someone who spent $100 and someone who spent $1,000.

Exponential tiers work better. Value 30 might represent $0-5, value 40 represents $5-20, value 50 represents $20-50, value 60 represents $50-100, and value 63 represents anything over $100. Now you're capturing useful signal across the full range of user value.

Privacy Threshold Trade-Offs

Apple blocks postbacks entirely when your campaign doesn't hit minimum install volume. You typically need 10-20 installs within a measurement window before any data appears, though the exact number varies. This creates a frustrating dynamic where your best-performing niche audiences might show zero attribution data while broad campaigns flood your dashboard with postbacks.

You're constantly balancing precise targeting against having enough scale to actually measure results. Narrow audiences might convert better, but if you never reach the privacy threshold, you're flying blind.

Implementation Checklist For Advertisers And Developers

Getting SKAN 4.0 running correctly involves technical setup and strategic decisions about what to measure. The choices you make here determine which campaigns you can optimize and which ones stay invisible.

1. Update SDK Or S2S Endpoints

Your MMP's SDK handles SKAN automatically once you upgrade to a version released after late 2022. If you're using server-to-server integration, postbacks arrive at a webhook endpoint and you parse them yourself.

Either way, test everything in Apple's sandbox environment first. SKAN postbacks don't arrive in real production until your app goes live with actual users and actual ad spend. Sandbox testing catches configuration errors before they cost you money.

2. Define Conversion Mapping

Start by listing your top 5-10 business events in order of value. First purchase, subscription start, high-value purchase, day-7 retention, whatever drives your unit economics. Then assign conversion values to each event, reserving your highest values (50-63) for revenue tiers that matter most.

The mapping you choose here flows directly into ad platform optimization. Meta and Google use these conversion values to find more users like the ones who convert. If your schema is wrong, the algorithms optimize toward the wrong users.

3. QA Postback Reception

Run small test campaigns with $100-200 budgets across Meta and Google. Check that postbacks arrive at your MMP or analytics system, that conversion values match your schema, and that timing aligns with Apple's documented delays.

Most implementation bugs surface during this step. Missing postbacks, incorrect value assignments, data that never reaches your BI tools, you'll catch all of it with a few days of testing.

4. Feed Data To BI And MMP

SKAN data flows from Apple to ad networks to your MMP to your data warehouse. Each hop adds latency and potential data loss. Your MMP decodes conversion values, adds campaign metadata, and surfaces aggregated reports. You want this unified with Android attribution and web analytics so you can see complete cross-channel ROAS.

Inside The Three Postback Windows

Each measurement window captures a different stage of the user journey. You configure conversion values separately for each window, which means you can optimize for day-0 actions in the first postback while tracking revenue in later ones.

1. Postback One Day 0 Engagement

Window one covers 0-2 days after install and uses fine-grained values from 0 to 63. Most teams map early engagement signals here:

• Registration completed

• First content viewed

• Tutorial finished

• Initial purchase under $10

You get the most granular data in this window because Apple expects higher install volumes. More installs means you're more likely to clear privacy thresholds and actually receive attribution data.

2. Postback Two Mid Funnel Value

Window two runs from day 3 to day 7 and switches to coarse values: low, medium, high. You'd typically track first purchase, subscription conversion, or meaningful engagement like multiple sessions coming back. The coarse values limit precision but still let you separate users who monetized from those who didn't.

3. Postback Three Long Tail Revenue

The final window (day 8 to day 35) uses coarse values to capture high-LTV signals. Repeat purchases, subscription renewals, extended retention, anything that indicates a user is truly valuable versus just trialing your app.

For subscription apps, this window often determines whether someone is worth acquiring. Gaming apps use it to identify whales who make multiple in-app purchases over weeks.

Crowd Anonymity And Data Thresholds Explained

Crowd anonymity refers to the minimum user pool size required before Apple releases attribution data. Instead of a single yes/no threshold, SKAN 4.0 uses four tiers (0, 1, 2, 3) that determine how much information you receive.

Threshold Levels

Tier 0 means your campaign didn't reach minimum volume, you get nothing. Tier 1 provides basic data like source app and coarse conversion values. Tier 2 adds campaign-level details. Tier 3 includes fine-grained conversion values and full campaign metadata.

The exact install counts for each tier aren't published by Apple. Most campaigns hit tier 2 around 50-100 installs, but this varies by market and time period.

• Low volume campaigns: Data gets suppressed completely until you cross the threshold, leaving you blind to performance

• Aggregated reporting: Apple sometimes groups multiple campaigns together, making it impossible to isolate what's working

• Geographic variations: Markets with smaller user populations have different threshold behaviors

Mitigation Tactics

Consolidating campaigns helps you reach thresholds faster. Instead of running five separate audience tests, you might combine them into two broader campaigns. You sacrifice some targeting precision but gain visibility into performance.

Broader audiences mean more installs but potentially lower conversion rates. It's a constant trade-off between measurement and optimization. Extended measurement windows also help, waiting an extra day or two sometimes lets campaigns cross the threshold that would otherwise stay suppressed.

Building A Hierarchical Conversion Value Schema

Your conversion value schema translates SKAN's numbers into business metrics. A well-designed schema balances granularity where it matters against simplicity for ongoing management.

Revenue Tiers

Exponential revenue buckets work better than linear ones. Try something like: $0, $1-5, $5-15, $15-30, $30-60, $60-100, $100+. This gives you precision in the ranges where most users fall while still flagging high-value outliers.

You're essentially creating a histogram of user value that fits into 64 possible values. The goal is capturing useful signal across the full distribution, not just measuring average revenue.

Engagement Signals

Non-revenue events get lower conversion values but still matter for optimization. Account creation might be value 10, first content engagement value 15, tutorial completion value 20.

Ad platforms use these signals to find users likely to progress through your funnel, even before they spend money. If users who complete tutorials convert at 3x the rate of those who don't, that signal helps algorithms optimize toward tutorial completers.

Predictive LTV Bands

Some teams assign conversion values based on predicted LTV rather than actual day-0 actions. A user who completes onboarding plus adds payment info might get a higher value than someone who just registers, because historical data shows that pattern predicts retention.

This approach requires robust data science but can significantly improve campaign optimization. You're essentially encoding your LTV model into the conversion value schema.

Optimising Campaigns With Coarse Values And Source Identifiers

SKAN's limitations force you to optimize differently than you did with IDFA. You're working with aggregated, delayed data and making decisions based on statistical patterns rather than individual user paths.

Bid Strategy Adjustments

When a campaign consistently delivers "high" coarse values in windows 2 and 3, you can justify higher bids because users are monetizing. Campaigns stuck at "low" across all windows signal audience or creative problems.

Most ad platforms now accept SKAN postbacks directly and adjust bids automatically. Meta and Google both use conversion values to optimize delivery, though you'll want to monitor these algorithms closely in the first few weeks.

Creative And Audience Testing

Source identifiers let you tag up to 100 different campaign variations within SKAN. Use them to test creative concepts, audience segments, or placement options. When one source ID consistently shows better conversion values than others, you've found signal worth scaling.

You won't know which specific users converted, but you'll know that "creative A with audience B" outperforms other combinations. That's often enough to make smart budget allocation decisions.

Reading SKAN Data Next To Android And Web Funnels

SKAN doesn't exist in isolation. You're running campaigns across iOS, Android, and web simultaneously, which means normalizing different attribution methods into comparable metrics.

Unified Dashboard Approach

Android still provides deterministic, user-level attribution through Google Play Install Referrer. This creates an apples-to-oranges problem: iOS gives you aggregated, delayed conversion values while Android gives you granular, real-time event data.

Your MMP translates both into common metrics like cost per install, day-7 ROAS, and LTV by cohort. Without this translation layer, you can't compare channel performance fairly.

LTV And ROAS Reporting

Building cohorted LTV models that work across iOS and Android means accepting different data latencies. iOS SKAN data arrives 2-5 days after install while Android events stream in real-time. You typically wait until day 7 or day 14 to compare cohorts fairly, giving SKAN postbacks time to arrive and stabilize.

Move From Guesswork To Clarity With Linkrunner

Linkrunner decodes SKAN 4.0 postbacks automatically and combines them with Android and web attribution in a single dashboard. You see which campaigns drive revenue, not just installs, with conversion value schemas mapped to your specific business model.

The platform flags underperforming campaigns based on coarse value patterns and suggests bid adjustments before you waste budget. Instead of waiting days to manually reconcile SKAN postbacks with ad spend, you get alerts when campaign performance shifts. Request a demo to see how your current campaigns would look with unified cross-channel measurement.

Frequently Asked Questions About SKAN 4.0 Decoding

What happens if a SKAN campaign fails crowd anonymity thresholds?

Apple suppresses the postback entirely when install volume is too low. You won't receive any attribution data for that campaign until it reaches minimum threshold requirements, leaving you completely blind to performance.

Do mobile apps still need an MMP with SKAN 4.0?

Yes, because SKAN only provides basic install attribution without user-level data. MMPs decode conversion values, unify cross-channel data, and provide the analytics layer needed for campaign optimization, turning raw postbacks into actionable ROAS metrics.

How long do SKAN postbacks actually take to arrive?

Postbacks arrive at random intervals between 24-48 hours after the conversion window closes. Apple adds this delay intentionally to prevent user identification through timing analysis, which means you're always optimizing campaigns based on 2-5 day old data.