Building the account scoring model before a target list exists

A target list built on gut feel and a loose firmographic filter, right industry, right headcount range, wastes ABM budget before the campaign has even started, because company size and sector tell you almost nothing about whether an account is actually going to buy anytime soon. The list itself is where most of the wasted spend in ABM actually originates, not the outreach that runs after it.

We build every target list from a scoring model that combines fit and intent as two separate axes, not one blended number. Fit answers whether an account looks like a customer we already win with: industry, size, tech stack, past deal patterns. Intent answers whether that account is showing any buying behavior right now. An account can score high on fit and low on intent, which makes it a candidate for nurture, not for an immediate one-to-one campaign.

The accounts worth the heaviest personalization investment are the ones that score high on both axes at once, and there are usually fewer of them than a first pass at a target list assumes. Cutting a proposed target list down after scoring, rather than adding more accounts to hit a round number, is often the single change that improves an ABM program's early results the most.

This scoring also determines tier, feeding directly into whether an account gets one-to-one, one-to-few, or one-to-many treatment. An account that clears the bar for genuine one-to-one investment but gets folded into a broad segment because the list was built by headcount alone is a mismatch that shows up later as a strategic account that never got the attention it should have.

Scoring the list before building it takes longer up front than pulling a firmographic export and calling it a target list, but it is what lets budget concentrate on accounts already showing real signal, drawn against a continuously verified database of 46M+ records, which is a large part of why engagements built this way convert at a 94% success rate instead of spreading thin across a list that only looked qualified on paper.