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AdExchanger broke the story. They ran the numbers. They showed the receipts.
But the real scandal isn’t in the reporting—it’s in the math that underpins the entire idea of “household identity.”

Truthset’s study for CIMM and Go Addressable didn’t just poke holes in the system—it torched it.The headline finding: IP-to-email matching accuracy at 16% and IP-to-postal at 13%. That’s not data integrity. That’s guesswork in a business suit.

Across six of the biggest data vendors, Truthset analyzed 1 billion emails, 250 million IPs, and 164 million postal addresses. And here’s the kicker: 90% of the linkages didn’t even overlap between vendors.
So, every provider has its own definition of truth—and none of them are the same.

The open web’s dirty secret is that it’s been pretending IP matching is deterministic. It isn’t. A household IP might represent one family—or an entire apartment building. It might change daily, hourly, or every time someone resets a router.

As Jon Watts from CIMM pointed out, advertisers are “being sold a pup,” paying for deterministic-sounding precision that doesn’t exist.
Kathryn Barnitt at Truthset called the state of IP data “messy,” even with billions of dollars riding on it.
And Scott McKinley, Truthset’s founder, warned that the worst thing the industry could do now is “bury its head in the sand.”

They’re all saying what many buyers have known for years: this isn’t data science—it’s data theater.

Vendors have learned the magic trick: inflate scale, and no one asks about accuracy.
By over-assigning IPs per household and keeping stale data in circulation, they create the illusion of reach while eroding the very precision advertisers are paying for.

It looks impressive on a dashboard. But the second you zoom in, it falls apart.
In urban areas, match rates look “okay.” In border states, they’re catastrophic. And everywhere else, it’s just noise—expensive, overconfident noise.

The result? A measurement ecosystem that’s built to sell volume, not validity.

We went deeper—talking to researchers, data scientists, and the people actually buying on these signals.
Their verdict: IP matching is an unreliable, decaying signal dressed up as deterministic truth.

It breaks attribution. It wastes budget. It puts the open internet at a competitive disadvantage to platforms like Amazon, Google, and Meta, which operate with deterministic, logged-in data.

And worst of al is that it erodes trust.

This isn’t a “data problem.” It’s a business incentive problem.
Brands reward scale. Agencies reward cheap CPMs. Vendors reward anything that keeps the illusion alive.

When 84% of your “matches” are wrong, you’re not targeting people—you’re targeting probability.

The full analysis—including expert interviews, technical breakdowns, and what advertisers can actually do to fix this mess—is available now for ADOTAT+ subscribers.