You relied on IDFA to tie app installs back to ad clicks. Once users opt out of App Tracking Transparency, deterministic IDFA-based attribution stops. Your mobile measurement partner (MMP) still ingests SKAdNetwork postbacks but loses user-level granularity, reducing it to an aggregated reporting layer.
Without deterministic attribution, you're left guessing which campaigns actually drove downloads, leading to overspending on underperforming channels. That uncertainty makes it hard to prove ROI, adjust bids, or plan scaling with confidence.
SKAdNetwork and Aggregated Event Measurement replace IDFA for iOS 14 and later. SKAdNetwork provides privacy-safe install postbacks with timer-based conversion values, while AEM enables you to report up to eight prioritized events per user within Meta’s ad ecosystem.
You need both: SKAdNetwork covers your overall funnel outside Facebook, and AEM fills in the gaps on Facebook campaigns.
This blog explores SKAdNetwork’s privacy-first tracking for iOS, Meta’s Aggregated Event Measurement, and the reasons behind their data differences, as well as two strategies to unify insights and scale growth.
Understanding SKAdNetwork (SKAN): The Privacy-First Approach
SKAdNetwork is Apple’s privacy-compliant framework for measuring app installs and select post-install events. When you weigh Meta AEM vs SKAN, remember that SKAN strips out user-level identifiers and only reports aggregated data. This makes it the default choice if you need to meet App Store privacy requirements.
How it works (briefly)
When you launch a SKAN 4.0 campaign, ad networks receive up to three postbacks over roughly 35+ days. In SKAdNetwork 4.0, each eligible app install can generate up to three postbacks, aligned with specific conversion windows and random 0- 24-hour delays for privacy:
Window 1 (0–2 days) → postback around day 2–3.
Window 2 (3–7 days) → postback around day 7–8
Window 3 (8–35 days) → postback around day 35–36, though it can arrive after day 41, depending on your conversion window settings and Apple’s crowd anonymity thresholds.
Each postback includes a conversion value and a campaign ID, but does not include a user ID. By comparison, meta AEM vs SKAN discussions often highlight that AEM can deliver faster and slightly richer data, at the cost of a higher privacy risk.
Benefit:
You get built-in compliance with Apple’s privacy stance. If you stick with SKAN rather than trying to force user-level tracking, you avoid App Store rejections and build user trust through stronger data handling.
Major Limitations (Why you might struggle)
Reporting Delays: You won’t see full data until the final postback lands. Those delays can stretch over 35 days or more, which means you can’t immediately pause underperforming ads or boost winners.
Data Sparsity: Low-volume campaigns often fall below Apple’s privacy thresholds and may yield no or very few postbacks. You may end up with sparse event data when you compare meta AEM vs SKAN results, especially on smaller budgets.
Under-reporting: Many marketers observe that SKAN shows about 5–15% fewer installs than App Store Connect, as installs under the privacy threshold are dropped. You should routinely cross-check your SKAN install numbers against App Store Connect and any AEM reporting you run.
To stay ahead, build simple cohort forecasts to gauge performance before those late SKAN postbacks arrive. Apply conversion-value locking for quicker insights and routinely compare meta AEM vs SKAN results to spot gaps and fine-tune your UA bids.
With SKAN’s privacy-first install postbacks in mind, let’s turn to Meta’s complementary solution, Aggregated Event Measurement, and see how it fills in the gaps.
Introducing Meta Aggregated Event Measurement (AEM)
When you run ads on iOS 14 and later devices, Meta Aggregated Event Measurement (AEM) is the method Meta uses to track your web and in-app events. It captures key conversion actions, such as installs and in-app purchases, without requiring any device identifiers, ensuring compliance with Apple’s privacy rules.
Core mechanics
Under the hood, AEM aggregates event data on Meta’s servers. AEM removes device identifiers, applies a randomized attribution delay and additive noise to conversion counts (not raw logs), then returns only aggregated, modeled metrics. This ensures Meta never sees or stores raw user-level logs, only the aggregated insights your campaigns need.
Key Distinction in “meta AEM vs SKAN”
While SKAdNetwork (SKAN) is Apple’s solution for post-install attribution, meta AEM vs SKAN differs fundamentally:
Independence from SKAN: AEM doesn’t ingest any SKAdNetwork postbacks or conversion values. It operates entirely within Meta’s ecosystem, utilizing its own privacy filters and modeling layers to evaluate your campaigns.
No reliance on SKAN conversion values: You won’t need to map SKAN’s coarse buckets or wait for Apple’s conversion windows. Meta AEM uses your event setup directly, then models performance in a way that aligns with Meta’s learning algorithms.
Why did AEM emerge?
You’re familiar with SKAN’s strengths, but its postback delay, which is at least 24 hours and often stretches to 48 hours or more, can leave you flying blind for a full day of optimizations. Meta built AEM so you don’t have to wait. You get modeled event data within hours, tightening your feedback loop and keeping your user acquisition (UA) campaigns agile.
Benefits of Meta AEM compared to SKAN
Near Real-time Reporting: With AEM, you start seeing modeled conversions just a few hours after they happen. In contrast, SKAN waits for a 24–48 hour random delay before sending any postback, slowing your ability to tweak bids and creatives in real-time.
No Data Tiers: AEM reports every modeled conversion regardless of your daily spend. SKAN, by design, can drop low-volume events below its privacy thresholds, creating “blackouts” in your data. AEM keeps your campaign graphs smooth.
More Consistent Volume: Because AEM uses aggregated statistical models (not strict install counts), you avoid the swings you see when SKAN thresholds kick in. Your learning phase can be completed reliably, helping you hit stable ROAS faster.
MMP Integration Since October 9, 2024: Meta began sending AEM signals to Mobile Measurement Partners on October 9, 2024, so you can pull your AEM-modeled data directly into your favorite analytics dashboard. Each MMP may handle these signals differently; double-check your integration settings to confirm you’re capturing the full AEM feed.
AEM provides you with faster, steadier, and privacy-safe insights within Meta’s own reporting system, while SKAN remains Apple’s deterministic but delayed alternative. Choose the tool that fits your UA tempo, and lean on Meta AEM when speed and consistency matter most.
You’ve seen how AEM and SKAN each report installs and events. Next, let’s explore why their figures don’t always line up.
When comparing Meta AEM to Skan, you’ll notice that the numbers rarely align perfectly. Here’s how to understand and act on each key driver of those gaps from a UA standpoint:
1. Reporting Delays
SKAN’s postbacks incur a random 24–48 hour delay before any install or conversion data is sent, whereas Meta AEM updates near real-time in Ads Manager. If you optimize solely on SKAN, you’ll be working one to two days behind current performance.
Pro tip: Use AEM for daily pacing and reserve SKAN for weekly or bi-weekly audits.
2. Attribution-Window Alignment
By default, Meta AEM attributes conversions based on a 1-day click, a 7-day click, and a 28-day view-through window. In comparison, SKAdNetwork’s three conversion-value windows (0–2, 3–7, and 8–35 days) differ from Meta’s default attribution windows of 1-day click, 7-day click, and 28-day view. Longer-tail conversions (e.g., D21 purchases) may appear exclusively in SKAN.
Pro tip: Match your reporting horizons by using SKAN’s full 30–35 day cohort for down-funnel events and AEM’s shorter windows for early-stage installs.
3. Channel Coverage
AEM only captures conversions within Meta’s ecosystem (Facebook, Instagram, Audience Network), whereas SKAN aggregates installs across all iOS ad networks. The total iOS lift (including non-Meta sources) appears in SKAN but is invisible to AEM.
Pro tip: Rely on AEM for day-to-day bidding and buying optimizations on Meta. Utilize SKAN for comprehensive iOS validation and incrementality testing across the full funnel.
4. Event Configuration Consistency
Mismatches occur when your Meta Events Manager schema (parameter names, value bins) doesn’t mirror your SKAN conversion-value mapping in the SDK or MMP. Apparent “lost” conversions in one system despite actual installs.
Pro tip: Conduct a side-by-side audit of your AEM event schema in Meta Events Manager against your SKAN conversion-value mapping in your MMP or SDK.
5. AEM Eligibility Rules
Meta limits each ad account (or domain for web) to eight prioritized conversion events; any lower-ranked events are simply not reported. SKAN, by contrast, always sends an install postback plus your chosen conversion values. Mid-funnel or secondary events may vanish from AEM if they’re outranked.
Pro tip: Before each campaign, review and reorder your top-8 event list to ensure priority aligns with your current goals.
6. Campaign Launch and Teardown Effects
AEM reflects installations immediately upon the start or stop of a campaign, whereas SKAN timers triggered at installation may not fire their delayed postbacks until days later. Numerical spikes or drops in the first or last 72 hours appear mismatched.
Pro tip: Hold scaling or pausing decisions until 72 hours post-launch or stop, when both systems have stabilized.
7. Low-Volume Variance & Privacy Thresholds
On SKAN, Apple’s privacy thresholds may suppress conversion values or source IDs when daily installs are small; AEM’s privacy thresholds and probabilistic modeling also introduce noise under low volumes. Small cohorts (<100 installs/day) can exhibit large percentage swings.
Pro tip: Focus comparative analysis on campaigns driving 200+ installs per week; treat gaps of 100 installs or less as statistical noise.
8. Integration Methodology
Rerouting SKAN postbacks through an MMP vs. direct SDK integration can introduce additional buffering, de-duplication logic, or conversion-value handling differences. MMP-mediated data may lag or differ slightly from the “raw” SDK postbacks.
Pro tip: Ensure your app runs the latest Meta SDK, enable your MMP’s AEM toggle, and verify identical conversion-value mappings in both tools.
9. Probabilistic Modeling Uncertainty
AEM augments deterministic signals with probabilistic attribution (especially on iOS when users opt out of ATT), which speeds up feedback but introduces model-driven variance. AEM trends are great for pacing but may overestimate real installs compared to SKAN’s deterministic counts.
Pro tip: Use AEM for directional insights and pacing, and treat SKAN’s postbacks as your final reconciliation.
Although AEM often provides speed and volume, there are clear cases where SKAdNetwork still outperforms. Let’s look at when that happens.
When SKAN Might Actually Outperform AEM
When running iOS campaigns, you often rely on Meta’s Aggregated Event Measurement (AEM) for real-time signals. Still, there are clear scenarios where SKAdNetwork (SKAN) delivers better ROI on down-funnel actions.
You’re seeing a lower Cost Per Trial with SKAN: In one February 2025 Trial Start campaign, SKAN reported a Cost Per Trial (CPT) of $49 over the most recent 14 days, compared to $71 via AEM. This makes SKAN 30 percent more cost-efficient on the same budget and creative set.
Meta’s fingerprinting misses some users: If AEM can’t match installs or trial starts because of fingerprinting gaps, your down-funnel conversions go uncounted. SKAN, by contrast, relies on Apple’s secure attribution calls and often captures these events more reliably.
You run limited iOS postbacks (high CPIs, low budgets): Brands with few postbacks per day are common when your cost-per-install is high or your spending is modest. See AEM inflate early-stage metrics but drop them off on trials or subscriptions. In tests, SKAN has kept pace with or even outpaced AEM on these lower-funnel events, despite having fewer signals.
AEM over-attributes installs but under-attributes trials: You may notice AEM crediting more installs, yet it fails to align those with actual trial starts or purchases. SKAN’s time-delayed, privacy-preserving model often yields a cleaner view of who converts beyond installation.
By split-testing AEM and SKAN head-to-head, you’ll discover which attribution framework you should lean on to drive the most efficient, bottom-line growth for your specific app and UA goals.
Knowing each tool’s strengths is one thing; learning how to use them together is another. Here are two frameworks to bring your data into harmony.
Actionable Frameworks: Reconciling SKAN & AEM
Since neither SKAN (deterministic but delayed) nor AEM (probabilistic but real-time) tells the whole story, you need a strategy to cross-reference data. The Million-Dollar Question: How to deal with varying numbers across sources?
1. Single-Channel Uplift Analysis
Best when testing on a single network (e.g., Meta only).
First, lock in your baseline, say you averaged 700 trials per week before ads. When you start your Meta campaign, keep every other marketing lever static. After a set period:
Collect counts: Meta AEM might show 500 trials; your backend logs 1,400 total.
Derive real increment: 1,400 − (700 + 500) = 200 extra trials driven by Meta.
Compute true CPA: divide your ad spend by the sum of reported + uplifted trials.
Next, bring in your cohort-based LTV projections. If your true CPA sits comfortably below LTV, ramp up; if not, refine targeting or pause.
2. Four-Layer Data Blend
When you’re juggling multiple channels (Meta, TikTok, Google, Snapchat, etc).
You’ll integrate:
Blended Actuals (your backend’s ground truth)
SKAN Postbacks (deterministic but delayed)
Modeled Reports (e.g., Meta AEM, Google Ads Conversion Modeling, other probabilistic attributions).
Organic Baseline (pre-campaign trends)
How to proceed:
Benchmark discrepancies by reviewing all four layers across past campaigns.
Calculate an Adjustment Factor:
Multiply your modeled counts by this factor to normalize across sources.
Adjust each channel’s modeled numbers by the Adjustment Factor.
Apply a quick decision matrix:
If SKAN-driven ROI is strong → scale confidently.
If only the modeled data looks good, → investigate over-attribution.
If adjusted and blended figures align → optimize spend across those channels.
Continue refining as SKAN adoption grows or privacy thresholds shift, recalculating AF and reviewing your matrix regularly.
Running SKAdNetwork 4 and Meta AEM side-by-side is now standard practice for iOS UA teams. SKAdNetwork provides deterministic, privacy-compliant install and post-install event data across three conversion windows; however, its anonymized postbacks can arrive up to 41 days after the install. They may undercount events below Apple’s privacy thresholds.
Aggregated Event Measurement delivers modeled conversion counts for up to eight prioritized events within Meta’s ecosystem in just a few hours, eliminating SKAN’s data “blackouts” and reducing swings caused by privacy thresholds.
Understanding the differences in reporting delays, attribution windows, channel coverage, event configuration, and privacy thresholds enables marketers to apply Single-Channel Uplift Analysis and the Four-Layer Data Blend frameworks. These methods reconcile SKAN and AEM discrepancies by blending deterministic postbacks with modeled reports and your backend ground truth. As a result, you can optimize bids in real time, prove true incrementality, and confidently scale your campaigns.
Ready to transform your iOS UA strategy? Get started today with a 14-day free Trialand see how Segwise AI can sharpen your attribution insights, streamline your workflows, and boost your ROAS without additional engineering or credit-card requirements.
FAQs
1. What are the key differences between Meta AEM and Apple SKAdNetwork?
Meta AEM delivers near real-time, modeled conversion data limited to Meta’s ecosystem. At the same time, SKAdNetwork provides delayed, deterministic install and event postbacks across all iOS ad networks, adhering to strict privacy thresholds.
2. Can Meta AEM fully replace SKAdNetwork for iOS campaign tracking?
Although AEM covers web and in-app events within Facebook, Instagram, and Audience Network, it doesn’t attribute installs from non-Meta channels or satisfy Apple’s SKAN requirements; therefore, SKAdNetwork remains mandatory for complete iOS attribution.
3. Why do conversion counts in Meta AEM and SKAdNetwork often differ?
Variances arise from AEM’s probabilistic modeling and near-real-time windows versus SKAN’s 24–48 hour randomized delays, longer attribution windows, broader channel coverage, and Apple’s privacy thresholds.
4. How do I configure Meta AEM and SKAdNetwork to work together?
In Meta Events Manager, verify your domain and rank up to eight events for AEM reporting while integrating the latest Meta SDK or MMP to map SKAN conversion values and register Apple postbacks.
5. What are the advantages and limitations of Meta AEM compared to SKAdNetwork?
AEM offers faster, smoother feedback without data blackouts but relies on modeled counts within Meta’s footprint. SKAN yields fully deterministic, privacy-compliant data across all ad networks at the cost of multi-day reporting delays and potential under-reporting.
