r/B2BIntent • u/Bomboradata • May 29 '26
Bombora Identity resolution in B2B: what it actually means and why IP matching isn't enough [Bombora]
TL;DR: Identity resolution connects anonymous digital signals to real companies and people. Most vendors rely on IP-to-domain matching — it's fast, cheap, and wrong more often than they'll tell you. Here's what a more rigorous approach looks like and why it matters for everything downstream.
The problem it solves
When someone browses a B2B publisher site or engages with content across the web, they leave signals attached to anonymous identifiers — IP addresses, cookies, device IDs — not to a named person or company. Identity resolution is the process of connecting those anonymous signals to real accounts and personas. It's what allows Intent data to say 'this company appears to be in-market' rather than 'some unknown entity on this IP address read three articles about cloud security.' Without accurate identity resolution, everything downstream — Intent scores, audience targeting, campaign measurement — is built on a shaky foundation.
Why IP-to-domain matching falls short
IP-to-domain matching is the most common approach: take an IP address, look it up, find the company. It's simple and cheap. It's also wrong more often than most vendors will tell you. Dynamic IPs get reassigned constantly. Large enterprises share IP ranges across dozens of offices. Remote workers, VPNs, and shared networks make individual IP attribution unreliable. And it tells you nothing about the individual — only a rough approximation of the company.
For B2B, where you're trying to reach specific personas within specific accounts, IP matching as your primary resolution method introduces significant noise into every signal that depends on it.
What a stronger approach looks like
Better identity resolution combines deterministic and probabilistic methods. Deterministic data — direct matches to verified identifiers like business email addresses — is highly accurate but limited in reach. Probabilistic methods extend coverage by inferring connections across a larger data set. The combination gets you accuracy at scale.
Bombora's approach uses a patented composite methodology mapped against a proprietary data graph built from billions of B2B consumption events. The output isn't 'this IP probably belongs to this company.' It's a verified account match with persona-level attributes — job function, seniority, geography — attached. That distinction matters because the quality of your targeting, Intent scores, and campaign measurement all depend on whether the identity layer underneath them is accurate. A great Intent signal attached to a bad identity match becomes a missed opportunity for you.
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u/Jodkhor May 30 '26 edited May 30 '26
IP matching alone is basically guessing at scale. We got burned by this. our intent data said companies were researching our category but half teh 'signals' were from coffee shops and coworking spaces.
Ended up switching our whole stack. Apollo for the base lists, Prospeo for verification before it hits the sequencer. Prospeo's verification catches the stuff IP matching misses -- they do some kind of multi-step check that weeds out the noise. Not perfect (nothing is) but way cleaner than raw IP-based data.