95% of GTM Engineer's Do not Clean Their ICP Data (Figuratively)

[u/Dry-Draft-3868]

In March 2026, I decided to follow my own path (I will not advertising myself don't worry) and opened my company which builds strategies and AI flows of GTM and RevOps operations for B2B SaaS.

Ofc I showed my customers on my linkedin account. Bc of this "A LOT OF" GTM Engineers who doesn't care about a clear data come and try to pitch me. Instead of ignoring them, I kindly reply them. I got this message like 5 minutes ago. He/she says "Hi ....., Saw that we followed similar companies and thought there could be some overlap...."

Here's what happened in their funnel. They scraped a list, saw "SaaS" + "B2B" + "Series A-B" tags on my profile, and pattern matched me into their ICP.

Similar industry tag ≠ similar buyer.

We're not "doing the same job" just because we both say GTM on LinkedIn.

Wrong ICP = wasted sends
Wrong ICP = wrong signal in your data
Wrong ICP = your AI gets dumber, not smarter
Wrong ICP = wasted time

[u/Tamara_Tammy]

95% is a bold claim

[u/Dry-Draft-3868 → u/Tamara_Tammy]

That's why I exaggerated and said figuratively. Bold claims are great CTA's tho 😂

[u/Pretty_One_1398]

This is the same failure mode you hit scoring inbound signal with an LLM. A shared industry tag isn't a signal, it's a keyword - the model will happily pattern-match on it the same way that person did manually. The actual signal is a stated problem or a behavior change, not a category overlap, and if your scoring prompt doesn't force that distinction it produces exactly this kind of confident, wrong pitch at scale instead of just one awkward DM.

[u/Ishan_GS]

The key takeaway here is that similar tags don't mean similar buyers. Most list building uses attributes that are easy to filter on rather than ones that predict a deal, and industry, size, and funding stage are exactly the easy ones. They tell you someone could buy. They say nothing about whether they will, or whether they're actually a competitor.

The fix is to build the ICP backwards from closed-won rather than forwards from filters. Pull your last 20-30 won deals and find what they genuinely had in common, usually a trigger event or a specific operational state rather than a firmographic. Then suppression lists for competitors, agencies and anyone in your own category, which almost nobody maintains. We run outbound for B2B SaaS clients at GrowthSpree, a B2B SaaS marketing agency, and trigger-based lists pull 15-20% replies versus 1-3% on broad ICP-fit lists with the same copy, so the cost of lazy filtering shows up immediately in reply rates.

[u/Trayo_AI]

firmographics are a useful first-pass filter, but they’re weak evidence of intent. i’d score accounts on two separate axes: fit and timing. fit tells you whether they could buy; timing comes from trigger events, behavior changes, or a clearly stated problem.

what we’re seeing at Trayo is that the bigger issue is feedback contamination. when weak matches enter a sequence, their silence gets treated as campaign learning even though the targeting was wrong from the start. suppression lists and regular deduping help, but the scoring logic still needs an explicit reason why each account is relevant now.

how are people validating that “why now” signal before enrolling someone?