Meta. Google. TikTok. AppLovin. Twitter/X. Snapchat. And more are coming…

Giants of the digital advertising world – and all members of a not-so-exclusive club: the Self-Attributing Network (SAN). These platforms have one thing in common (aside from commanding a lion’s share of your media budget): they don’t let anyone else tell them what conversions they did or didn’t drive.

Instead, they take matters into their own hands – and your data – and decide for themselves. On the surface, it sounds efficient. In practice? It's a measurement nightmare.

What Exactly Is a SAN?

A Self-Attributing Network is an ad network that refuses to play by the same attribution rules as everyone else. Rather than rely on the advertiser’s measurement tools (like MMPs or independent platforms like INCRMNTAL), SANs attribute conversions internally, using their own black-box logic. You don’t get raw data. You get their verdict.

For example:

A user sees an ad on TikTok, doesn’t click, but installs the app a few hours later. TikTok claims the credit.

That same user also saw an ad on Instagram, clicked it, and then installed. Meta claims the credit.

The user also searched on Google and clicked your branded search ad right before installing. Google says: yep, that’s us.

Three networks. One user. One install. Three full attributions. 

Why SANs Get Good Results (on Paper)

Let’s give credit where credit is due – SANs generate massive reach, engagement, and yes, conversions. Their targeting is tight, their UX is sticky, and their ad units are often embedded seamlessly into user journeys. If anyone can drive performance at scale, it's these platforms.

And because they operate in closed ecosystems, they have:

First-party data galore

Full visibility into user behavior within their apps

Optimization algorithms that can react faster than any media buyer

But here’s the kicker: because they control the attribution, they can assign credit more generously than third-party tools would allow. This isn't fraud – it’s just self-interest, algorithmically enforced.

Advertisers often find that SAN-reported results significantly overstate performance compared to third-party tools, internal data, or reality. The reasons?

Double-Counting Conversions: SANs don’t coordinate with each other. So if multiple SANs claim the same user, you end up with multiple reported conversions – for the same event.

Last-Touch Bias Within Their Walls: A SAN might claim any exposure – even a passive view – as the cause of a conversion, especially if no other trackable click occurred outside their platform.

Lack of Transparency: Most SANs do not provide user-level data, which prevents true cross-channel deduplication or attribution modeling. You're left trusting their black-box output, unable to validate it.

Disregard for Holistic Lift: SANs don’t care whether a conversion would have happened organically. Their metric is binary: Did we touch this user? Yes? Cool – credit us.

As we explain in Algorithmic Attribution and its follow-up Part Deux, self-attribution is a system designed to favor the platform’s own interests – not yours. It’s attribution theater: slick, scripted, and always starring the same hero.

Why This Matters More Than Ever

In a privacy-centric, post-IDFA, post-cookie world, marketers are starved for signal. And SANs have all the signal – but none of the accountability. You’re paying millions in ad spend based on data you can’t independently verify. The result? Over-investment in channels that may look great in siloed reports, but do little to drive true incremental growth.

At INCRMNTAL, we believe attribution should be algorithmic but neutral. It should quantify what a campaign actually caused, not just what it touched. Self-attribution fails this test by design.

Self-Attributing Networks are the ultimate performance marketers – for themselves. They generate good results, yes, but they also measure themselves, grade themselves, and hand you the report card. If you’re relying solely on SAN-reported metrics, you’re not measuring performance – you’re reading ad-funded fiction.

If you care about actual impact – not inflated metrics – it’s time to start measuring with tools built for truth, not spin.

Read more:

Algorithmic Attribution

Algorithmic Attribution: Part Deux