They stop arguing about who the ideal customer should be and go and measure who it turned out to be. Pull your last fifty to a hundred closed won and closed lost accounts, define what good actually means for you (fast cycle, high retention, expansion, low support load) and look for the attributes that correlate with it, including the ones nobody expected. Then turn that into a score that lives as a field on every account in the CRM and actually routes work: who gets a rep, who gets a sequence, who gets nothing. The two things that separate a good model from a spreadsheet nobody opens are negative signals that subtract points, and a quarterly re-run, because your ICP is a regression on live data, not a slide from the seed round.
3 hand-picked resources, 3 link-checked. Pick how you want to dig in.
Why we picked it Kellogg's framing, that the ICP begins as a founder hypothesis and should end as a regression on renewal, expansion and win rate, is the whole argument for data over opinion in one sentence. He also makes you define what best means before you model anything.
From Kellblog by Dave Kellogg 9 min read
Open kellblog.com →Why we picked it The build guide: weighted dimensions, negative signals that subtract points, and score bands that decide routing. It is the operational half that most ICP writing leaves out.
From Understory by Naufal Nugroho 15 min read
Open understoryagency.com →Why we picked it A concrete menu of the events that put an account into a buying window, from job changes to fiscal cycles, which is exactly what you need when your fit list is large and your timing sense is zero.
From UserGems 10 min read
Open usergems.com →How who you sell to reads from a different seat.
The same ground, over in Understand your customers, our Starting Up track.
