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Squid ID combines its own visitor signals with third-party identity and enrichment data to build a profile. It doesn’t rely on a single provider, and it isn’t a waterfall that takes the first source to answer. Squid ID draws on all of its sources for a given visitor, merges what comes back, and resolves the record only where those sources agree.

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What goes into a profile

For each identifiable visitor, Squid ID assembles a profile from several inputs:
  • Visitor signals. The pages viewed, referrers, and device captured by the snippet on your own site.
  • Identity and demographic data. Name, contact details, location, job title and role, company, and social profiles, drawn from people-data enrichment.
  • Company and organization data. Company name, domain, industry, employee count, and headquarters, including a photo and headline where available.
Squid ID merges these into one profile for the identified person, with the company they work for attached. When one source is missing a visitor, another can still fill in the contact or firmographic details. See Person and company data.

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Cross-checking and verification

We don’t trust any single provider. A waterfall queries providers in order and stops at the first hit, which means the record you get is whatever one source happened to say. Squid ID works the other way: for each candidate match it compares the data across as many as five independent datasets and resolves a person only when those sources agree. Each field, the name, the work email, the company, the title, has to corroborate across the providers that hold it. Agreement across independent sources is what turns a candidate into a confirmed match. When sources disagree, we weigh how much. A small, explainable gap, like a provider that is a few weeks stale on a title, is reconciled to the value the others support. But when the conflict is material, when the datasets cannot agree on who this person actually is, we do not guess and we do not show the record. A withheld record is better than a wrong one. The matching runs on our own AI algorithms. They resolve the same person across providers that format and key their data differently, dedupe the duplicates that fall out of merging several sources, and verify each resolved field against the others before it reaches your list. What you see is a profile that several independent datasets stand behind, not a single provider’s best guess.

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Coverage and accuracy

De-anonymization turns anonymous web traffic into named people. That is a step change from what most teams have today, which is a visit count and nothing else. So the number that matters is not how close we get to 100 percent, it is how far we lift you above zero. Identifying even a double-digit percentage of your B2B traffic surfaces warm accounts and contacts that were completely invisible a moment ago. Identification is probabilistic by nature. It matches the signals from a browser and device to identity data, and two things follow from that. Coverage. Not every visitor can be tied to a person with confidence. Instead of guessing, we only surface the matches our data stands behind (see the cross-checking above), so a realistic rate on good B2B traffic is a meaningful share of it, not all of it. Fewer people you can trust beats a padded list you cannot, and every match is someone you could not see at all before. See Match rate for what to expect. Accuracy. Once in a while the data on a match is right but tied to the wrong person. Devices get shared. A work laptop, a household computer, or a hot desk can put several people behind the same browser and the same identifiers, so the identity can resolve to a real person other than the one at the screen. This is inherent to how device-based identification works rather than something specific to Squid ID, and our cross-checking keeps it uncommon. If you ever spot one, flag it and we refund the match. See Flag a match. Treat identification as a high-quality stream of buyers you otherwise never knew visited, not a perfect census. A verified fraction of your traffic is worth far more than the whole of it left anonymous.

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Data freshness

Enrichment data reflects the providers’ most recent records, which can lag real life. People change jobs and companies restructure, so a title or company can be out of date. Repeat visits give Squid ID newer signals to match against, and a profile can be updated when better data arrives. If a match is wrong, flag it. See Flag a match.

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Privacy

How this data is handled, and your responsibilities, are covered in Privacy and compliance.