Scoring That Sells: Balancing Fit vs. Intent
Get cleaner pipeline by combining ICP Fit (company/person match) and Buying Intent (behaviors & signals) into a governed, testable score. Reduce noise, route faster, and focus reps where propensity × potential value is highest.
Balance fit and intent by using a hybrid score: a stable Fit Score (ICP match on firmographics, technographics, persona, account tier) multiplied or blended with a dynamic Intent Score (first-party behavior, third-party intent, recency/frequency, in-product signals). Use stage-specific thresholds to route (MQL→AQL→SAL) and segment-specific weights (SMB vs. enterprise, net-new vs. customer). Review quarterly with lift tests vs. revenue, not just MQL volume.
What “Good” Looks Like
The Hybrid Fit × Intent Scoring Workflow
Use this sequence to design, deploy, and tune scores that correlate with revenue, not vanity metrics.
Define → Instrument → Calibrate → Activate → Govern
- Define ICP & tiers: Industry, size, geo, tech stack, account tiering; map personas and buying roles.
- Instrument signals: Web/app events, content depth, email engagement, review sites, intent providers; standardize UTM & event names.
- Calibrate weights: Start simple (Fit 60% / Intent 40% for ABM; reverse for velocity). Add time decay and negative points.
- Activate routing: Set score gates for MQL/MQA, auto-enroll plays, notify owners, and suppress low-fit nurture.
- Govern & learn: Run A/B lift tests on thresholds, analyze conversion by decile, refresh quarterly with sales feedback.
Fit vs. Intent Scoring Maturity Matrix
| Capability | From (Ad Hoc) | To (Operationalized) | Owner | Primary KPI |
|---|---|---|---|---|
| ICP Definition | Loose personas, no tiers | Normalized ICP fields, account tiers, buying roles | RevOps/Product Marketing | Qualified Account Coverage |
| Signal Instrumentation | Clicks & forms only | Depth, recency, product usage, 3P intent, negatives | Marketing Ops/Analytics | Signal Completeness % |
| Model Design | Single blended score | Separate Fit & Intent with tunable weights & decay | RevOps/Data Science | Lead→SQL Lift vs. Baseline |
| Routing & Plays | Manual triage | Automated gates, playbooks by tier & stage | Sales Ops/SDR | Speed-to-First-Touch |
| Governance | Sporadic tweaks | Quarterly lift tests, taxonomy freeze, change log | RevOps | Win Rate by Decile |
Client Snapshot: Cleaner Pipeline, Higher Win Rate
A SaaS firm split Fit and Intent, added decay and negative signals, and raised the MQL gate to require minimums on both. Results: fewer handoffs, +19% opp creation per rep, and +11% win rate from top two deciles. See related outcomes in our work with Comcast Business and Broadridge.
Orchestrate actions along The Loop™: when Fit is high but Intent is low, nurture; when both are high, route and execute plays.
Frequently Asked Questions on Fit vs. Intent
Turn Scores into Revenue
We’ll define ICP Fit, operationalize Intent signals, and deploy routing & plays with lift tests tied to opportunity creation and win rate.
Optimize Lead Scoring Align Scoring with ABM