How Do You Define Scoring Criteria for Accounts?
Build a transparent, outcome-based account scoring model that blends Fit, Intent, and Engagement—so you prioritize the right companies, route them quickly, and fund the plays that win.
Define account scoring by documenting clear criteria across three pillars: Fit (ICP tiers & disqualifiers), Intent (topic surges & research recency), and Engagement (multi-contact activity depth). Normalize data sources, set thresholds & weights, apply recency decay, and convert raw points into bands (e.g., Observe → Researching → Active Buying) tied to plays, routing, and SLAs. Iterate monthly against pipeline and win-rate lift.
Core Criteria to Include
Account Scoring Criteria Playbook
Move from opinion-based scores to an auditable, banded model tied to programs and outcomes.
Align → Inventory Data → Draft Criteria → Weight & Threshold → Validate → Launch → Govern
- Align on ICP & exclusions: Document A/B/C tiers, verticals, regions, and hard no-go rules.
- Inventory data: Map firmo/techno, intent providers, first-party events, and identity stitching coverage.
- Draft criteria list: Select 8–12 signals across Fit/Intent/Engagement; define what earns or deducts points.
- Set weights & thresholds: Apply recency decay, frequency caps, and minimum Fit + Intent requirements for routing.
- Validate on history: Back-test against won/lost opportunities; tune for separation and lift.
- Launch with bands: Convert scores to bands and bind each band to ABM motions, budgets, and SLAs.
- Govern & iterate: Monthly model council reviews lift by band; version, QA, and rollback changes.
Scoring Criteria Capability Maturity Matrix
| Capability | From (Ad Hoc) | To (Operationalized) | Owner | Primary KPI |
|---|---|---|---|---|
| ICP Definition | Loose persona notes | Tiered ICP with disqualifiers and target density by region | RevOps | % Pipeline from A/B ICP |
| Signal Catalog | Clicks-only | Balanced Fit/Intent/Engagement with recency & frequency logic | Demand Gen | Meetings by Band |
| Banding & Plays | Flat score | Score bands mapped to 1:Many/1:Few/1:1 motions & budgets | ABM Lead | Opp Creation Rate |
| Routing & SLAs | Manual triage | Automated queues, alerts, cadences based on band changes | Sales Ops | Speed-to-First-Touch |
| Governance | Set-and-forget | Versioned models with change logs, QA, and rollback | Rev Council | Win Rate / Velocity |
Client Snapshot: Criteria That Predict
After replacing a points-only model with banded Fit–Intent–Engagement criteria, a B2B team cut low-propensity outreach by 40% and increased opportunities from A/B ICP accounts by 28% within one quarter, with no additional spend.
Keep the criteria explainable. If a rep can’t say why an account is “Active Buying,” the model needs fewer, clearer signals and better decay/threshold settings.
Frequently Asked Questions
Turn Criteria into Revenue Signals
We’ll define Fit–Intent–Engagement criteria, set thresholds, and bind bands to ABM motions, routing, and SLAs.
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