Question

How does the new Advanced SRN from X and AppsFlyer improve iOS ad tracking after ATT changes in 2025?

I’ve been running iOS campaigns on X (formerly Twitter) and like many others, tracking performance after Apple’s ATT has been a nightmare. The delays and lack of user-level data through SKAN made it really hard to optimize.

I just read about the new Advanced SRN integration between X and AppsFlyer, which apparently uses a mix of deterministic attribution (when available) and privacy-safe modeling to track installs and conversions more accurately.

They’re also combining this with their SSOT (Single Source of Truth) model for more unified data, claiming up to 27% lower eCPI. Sounds promising, but I haven’t tested it yet.

Has anyone tried this integration yet? Does it actually give better visibility and ROAS on iOS compared to what we had with SKAN-only setups?

Answer 1

The Advanced SRN integration between X and AppsFlyer is essentially their answer to the measurement gap created by Apple’s App Tracking Transparency (ATT) changes. After ATT, most iOS ad measurement relied heavily on SKAdNetwork (SKAN), which is aggregated, delayed, and often missing critical user-level signals. That made optimizing campaigns, especially for lower-funnel events, frustrating.

1. Deterministic + Modeled Attribution

• When a user consents to tracking (and X can capture the click/install chain), AppsFlyer uses deterministic attribution, giving you real-time, user-level data.

• For users who don’t consent, instead of relying solely on SKAN’s delayed postbacks, the system applies privacy-preserving modeling to fill in gaps.

• This hybrid approach often results in more complete datasets, reducing the “unknown” share of conversions.

2. Real-Time SRN (Self-Reporting Network) Data

• X’s SRN connection to AppsFlyer can pass back click and impression data without waiting for SKAN postbacks.

• This helps shorten the feedback loop for campaign optimization.

3. SSOT (Single Source of Truth) Reporting

• Instead of juggling SKAN data, MMP data, and platform data separately, the SSOT model merges them into a single, deduplicated dataset.

• This removes the risk of double counting installs and improves cross-channel budget allocation.

4. Impact on eCPI & ROAS

• The 27% lower eCPI figure comes from having fewer “untracked” installs—meaning you can bid more intelligently, pause underperforming ad sets sooner, and shift budget toward higher-ROAS segments.

• For performance marketers, this can restore some pre-ATT efficiency.

Caveats:

• Gains will vary depending on your opt-in rate and how much traffic you drive from X.

• Privacy-safe modeling is still modeling—accuracy depends on the quality of your data inputs and historical patterns.

• If most of your installs are from non-consenting users, you’ll still have some measurement uncertainty (just less than before).

If you’ve struggled with iOS measurement post-ATT, this integration is worth testing. Early adopters in other SRN contexts (like Meta’s AEM + MMP hybrid setups) have seen real improvements in optimization speed and spend efficiency. I’d recommend running a side-by-side test with your current SKAN-only setup for 2–4 weeks to validate the lift in ROAS and install accuracy.