Two scores, each summed from typed signals, decayed and capped. Deterministic and auditable, so a skeptical rep believes it in seconds.
The scoring engine turns raw signals into the two numbers that place an account on the Demand Compass: an awareness score and a readiness score, each on a zero-to-ten scale.
It fixes the real failure of the MQL: not accuracy, but trust. A score a rep does not believe is worth zero. The engine earns trust by being explainable, not by being clever.
Each axis sums its typed signals, applies decay, and caps the total at ten. The core is deterministic arithmetic, the same signals always produce the same score, with AI only at the edge (one tag, one sub-score, an input to the score, never the score). Every score decomposes into named, dated signals.
Use it whenever you route accounts to humans. The rep opens the account already knowing why it is hot, because the reason is the very same signals that raised the number.
An account reads: new VP hired 12 days ago, three pricing-page visits this week, repo starred, readiness 8, awareness 4, In-Market. A rep believes that in four seconds; a security buyer's assessment fails a black box on sight.
The engine feeds the four quadrants and depends on the decay rule to stay current. It reads signals from the dark channel, rolled up to the account.
From The Demand Compass: The Signal-Based GTM Framework for B2B Marketing Leaders in Complex Sales, by David Moreira and Marcos Stubrin (automate rev.ops.).
ISBN 979-8-9969092-1-6 (paperback). Get the book →