Website tracking ID lookup

What are tracking IDs, and why do they matter?

Almost every website carries a handful of invisible identifiers — small strings of letters and numbers that tie the site to an analytics account, an advertising account, or a marketing platform. Individually they are mundane plumbing. Collectively, they are one of the most powerful tools in open-source investigation, because the same person often reuses the same account across every site they run.

When someone builds ten websites, they rarely create ten separate Google Analytics accounts. They copy the same measurement snippet into all of them, because it's easier and because all the traffic data flows into one dashboard they already own. That convenience leaves a fingerprint. If two apparently unrelated domains carry the identical Google Analytics ID or the same AdSense publisher ID, it is strong evidence they share an owner — far stronger than a shared web host or IP address, which countless unrelated sites sit behind by coincidence.

This is why investigators, journalists, brand-protection teams, and fraud analysts look at tracking IDs first. A scammer will change registrars, swap hosting providers, hide behind privacy protection, and rotate domain names — but forget that every one of their lookalike sites still phones home to the same analytics account. The tool above reads the IDs a site exposes directly in its homepage HTML, so you can see those signals for yourself.

The tracking IDs this tool detects

ads.txt and sellers.json: the ad-network paper trail

Beyond IDs buried in page code, the advertising industry created two public files that make monetization relationships explicit — and they are a goldmine for understanding how a site earns.

ads.txt is a plain-text file a publisher places at /ads.txt on their own domain. It lists every advertising system authorised to sell that site's inventory, each with the publisher's account ID and whether the relationship is DIRECT (the publisher deals with the ad system directly) or RESELLER (an intermediary resells it). The standard exists to fight ad fraud — it lets buyers verify they're purchasing legitimate inventory — but as a side effect it publicly declares exactly which ad networks a site works with and under which account numbers.

The investigative value is direct: two sites whose ads.txt files list the same publisher account ID on the same ad system are selling ad space through the same account — a strong monetization link. The tool above fetches and parses a site's ads.txt so you can read these seller relationships at a glance. sellers.json is the complementary file, published by the ad exchanges themselves, mapping those seller account IDs back to the businesses behind them — closing the loop from "this ID" to "this company."

The network effect: connecting a portfolio

Individually, each of these signals links two sites. Together, they compound. A single operator running twenty sites might vary the domain names, hosting, and registrars — but if all twenty share one AdSense publisher ID, funnel affiliate clicks through one Amazon tag, and declare the same seller account in ads.txt, the monetization layer stitches the whole portfolio together. This is why monetization IDs are the backbone of competitor research and hidden-network discovery: the money has to flow somewhere, and where it flows is where the connections show.

As more domains are inspected through this tool, shared IDs begin to reveal these clusters directly — when an ID you're looking at has been seen on other domains, that connection is surfaced alongside it. The picture is scoped honestly to what has actually been observed here, not a claim about the entire internet, but for mapping a specific operator's footprint it is often all the thread you need to start pulling.

How shared IDs reveal common ownership

The core principle is simple: these identifiers correspond to accounts, and accounts belong to people or organisations. A web host is shared by strangers; an analytics or ad account is not. When the same account ID appears on multiple sites, the most economical explanation is that one party controls all of them.

The strength of the signal varies by type. An AdSense publisher ID or a GTM container ID is a very strong link, because those are centrally managed and tied to payment or deployment. A Google Analytics ID is strong. A Facebook Pixel is strong for advertising-driven sites. None of them is absolute proof on its own — a shared ID could, in rare cases, reflect a shared agency, a template, or a compromised account — which is why responsible investigators treat a matching ID as a lead to corroborate, not a verdict to publish. But as a starting thread to pull, few signals are more productive.

What this tool can and cannot see

This lookup reads a site's static homepage HTML — the code the server sends before any JavaScript runs. That is where many sites place their tracking snippets, so the tool finds IDs on a large share of the web, and it is especially effective on the simpler, hastily-built sites that OSINT work often targets.

It has honest limits, though. Sites that load analytics only after a cookie-consent banner is accepted, or that inject their IDs entirely through a Tag Manager container or an external script executed in the browser, may not expose those IDs in the raw HTML — so the tool can return fewer codes than a full browser would, or none at all. A blank result therefore means "no IDs were exposed in the static HTML," not necessarily "this site has no tracking." Conversely, when the tool does surface an ID, it is a real, verifiable string taken directly from the page.

Using tracking IDs responsibly

Tracking IDs are public information — they are transmitted to every visitor's browser by design, so reading them is neither hacking nor a privacy breach. But the conclusions drawn from them carry weight. A shared ID is evidence of a link, not proof of wrongdoing, and the same technique that unmasks a scam network could misattribute a coincidence. Treat a match as a hypothesis to verify against other evidence — registration data, content, infrastructure — before acting on it. Used carefully, tracking-ID analysis is one of the most efficient ways to map a single operator's footprint across the web.