How Self-Attributing Networks Work
Self-Attributing Networks operate under a fundamentally different attribution model than standard ad networks. In the conventional flow, an ad network shares click and impression data with the MMP, and the MMP performs attribution by matching these touchpoints to installs. SANs reverse this, they perform attribution internally using their own data and report the result to the MMP as a claim.
When a user sees or clicks an ad on a SAN platform and later installs the app, the SAN's internal systems detect the install (typically through their own SDK integration) and match it to the prior ad engagement using their first-party data. The SAN then sends an attribution claim to the MMP, asserting that it drove the install. The MMP receives this claim alongside click data from non-SAN sources and applies deduplication logic to determine the final attribution.
This model exists because SANs possess massive first-party datasets that they consider competitively sensitive. Meta knows which users saw and engaged with ads across Facebook and Instagram. Google knows search queries, YouTube views, and Play Store behavior. Sharing this raw data with third-party MMPs would expose proprietary signals. By performing attribution internally, SANs maintain data control while still participating in the cross-network attribution ecosystem.
The SAN Attribution Flow
Understanding the technical flow of SAN attribution helps growth teams configure their measurement stack correctly and interpret their data accurately. The process begins when the advertiser's app integrates both the MMP SDK and the SAN's own SDK (Meta SDK, Google SDK, etc.). When a user installs and opens the app, both SDKs activate and begin their respective processes.
The MMP SDK collects device identifiers and checks for matching clicks from non-SAN sources. Simultaneously, the SAN SDK communicates with the SAN's servers to check whether the device had any prior ad engagement on the SAN platform. If the SAN finds a match, it sends an attribution claim to the MMP via a server-to-server API. This claim includes the campaign details, timestamp of the ad engagement, and the attribution method used.
The MMP then runs its deduplication logic. If no other source has a competing claim, the SAN gets attribution. If a non-SAN network also has a matching click, the MMP compares timestamps and applies its attribution rules, typically last-click wins. If two SANs both claim the same install, the MMP uses the engagement timestamps to determine priority. The final attribution decision is recorded in the MMP's reporting, and postbacks are sent to the winning source.
Managing SAN Relationships
Working with SANs requires a different operational approach than managing standard ad networks. Because SANs control their own attribution, you have less visibility into the raw data behind their claims. You cannot independently verify that a user actually saw or clicked an ad on the SAN platform, you are trusting the SAN's internal matching. This makes cross-referencing SAN-reported data against your MMP data essential.
Discrepancies between SAN-reported conversions and MMP-attributed conversions are normal and expected. The SAN counts every install it claims, while the MMP deduplicates across all sources and may attribute some of those installs to other networks or organic. A 10–20% discrepancy is typical. Larger discrepancies warrant investigation, they may indicate configuration issues, SDK integration problems, or attribution window mismatches.
Linkrunner provides clear visibility into SAN attribution claims alongside all other traffic sources, making it straightforward to identify discrepancies, understand deduplication outcomes, and ensure your cross-network attribution is accurate. By presenting SAN and non-SAN data in a unified view with consistent metrics, Linkrunner eliminates the fragmented reporting that makes SAN management unnecessarily complex for growth teams.
Deduplication and Attribution Conflicts
Attribution conflicts between SANs and other sources are one of the most common and misunderstood aspects of mobile measurement. A conflict occurs when multiple sources claim credit for the same install. This happens frequently because users interact with ads across multiple platforms, they might see a Meta ad, search on Google, and click a display ad before installing. Each platform that recorded an engagement will claim the install.
The MMP resolves these conflicts using a hierarchy of rules. First, it compares attribution methods, a deterministic match (device ID) typically beats a probabilistic match (fingerprint). Second, it compares engagement types, a click usually beats a view. Third, it compares timestamps, the most recent qualifying engagement wins under last-click attribution. These rules are configurable, and the specific settings can significantly impact how credit is distributed across your media mix.
Configuration matters more than most teams realize. Your attribution window settings for each SAN directly affect conflict resolution. If Meta has a 7-day click-through window and Google has a 30-day window, Google will win conflicts for installs that happen 8–30 days after a Google click, even if the Meta engagement was more recent within its own window. Standardize attribution windows across SANs where possible, and understand the implications when standardization is not feasible.
SANs in the Privacy-First Era
The privacy landscape has strengthened the position of Self-Attributing Networks relative to standard ad networks. SANs have massive first-party data assets that do not depend on third-party identifiers like IDFA or GAID. When Apple introduced ATT and IDFA opt-in rates dropped, SANs could still perform attribution using their logged-in user data, email addresses, phone numbers, and platform-specific identifiers that persist regardless of ATT status.
This first-party data advantage extends to targeting. While standard ad networks lost much of their targeting precision when device-level identifiers became restricted, SANs retained their ability to target based on platform behavior, interests, and demographics derived from their own ecosystems. This has led to a concentration of ad spend toward SANs, particularly on iOS, where the targeting and measurement gap between SANs and non-SANs has widened significantly.
For growth teams, this concentration creates both opportunity and risk. SANs deliver strong performance because of their data advantages, but over-reliance on a small number of platforms creates vulnerability to policy changes, cost inflation, and audience saturation. Diversifying across SANs and non-SAN sources, while maintaining rigorous cross-network measurement, is the sustainable approach. Monitor your SAN concentration ratio and actively test emerging channels to maintain a balanced and resilient media mix.
