Growth-stageFintech & Financial Services
Scoring intent when nobody fills out a form: a behavioral MQL model and the attribution layer under it
A scoring model the client could argue with
A scoring model the client could argue with
The target list was small and finite, and deals ran for years across a large, multi-function buying committee. In that shape, a form-fill-driven MQL funnel is the wrong instrument, there simply are not enough hand-raises to qualify on, and the ones that do arrive skew wrong because the generic contact form was dominated by existing end users of a separate self-serve product line rather than prospects.
Demo and contact form fills already auto-stamped MQL through the existing web-form → automation → CRM path, and that was deliberately left alone. The behavioral score does not touch hand-raisers. Its entire job is to surface the contacts at target accounts who are evaluating quietly.
Version one used five groupings (high-intent pages, repeat engagement, key conversions, evaluation content, identity capture).
Within G1 and G3, a single pageview under thirty seconds scores 2; a single pageview at thirty seconds or more scores 5; two or more pageviews on scored URLs inside the same session score 8; and hitting two distinct scored URLs in one session adds a 3-point bonus.
The rolling window was stretched from 90 days to 180 at the client's insistence, a three-month window against a multi-year cycle produces a score that is mostly noise.
The behavioral subtotal is multiplied by a title-specific tier: 1.5x for the champion / pain owner, 1.3x for influencers and evaluators, 1.1x for director-level secondary roles, and 0.7x for a low-propensity catch-all.
The model was presented to the client's team as what 30 actually costs each persona: a top-tier persona MQLs on 20 behavioral points (one deep session, three return visits, one case study), a second-tier on 24, a third-tier on 28, and the catch-all needs 43, which effectively cannot be reached without maxing two groups.
Once MQL flips true it stays true until the score sits under 25 for thirty consecutive days, and the notification fires once on the false-to-true flip rather than on every nightly refresh. Existing end users, off-ICP accounts and unmatched free-mail addresses are scored but suppressed rather than routed.
A scoring model the client could argue with
Rather than a black box, the model was delivered as an explicit weights-and-caps table plus a worked example showing the same browsing behaviour MQL-ing for a buyer persona and not MQL-ing for a support-adjacent one.
7 steps
An automated ABM engine: batch enrollment, generated personalization, and the guardrails that stop a bad send
Workflow success rate materially improved and was measurable
Attribution
4 steps
Closing the gap between MQL and pipeline
~8,800 contacts re-attributed
Outbound
5 steps
Consolidating two sales-engagement systems into one: 22 cadences rebuilt one-to-one
22 cadences migrated one-to-one
