How do you define scoring criteria for accounts?

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

ICP Fit — Industry, employee size, revenue band, region, tech stack, compliance needs; include hard disqualifiers.

Buying Group Coverage — Count of engaged roles (economic, technical, user), title seniority, and function diversity.

Third-Party Intent — Keyword/topic surges, recency, and share of research against competitors.

First-Party Engagement — High-intent pages (pricing, integrations), repeat visits, content depth, and event interactions.

Solution Fit Signals — Installed tech, pain indicators, trigger events (hiring, funding, M&A, stack changes).

Noise Guards — Internal traffic filters, frequency caps, down-weight generic assets, require Fit+Intent combos.

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

How many criteria should we start with?

Begin with 8–12 well-defined signals across Fit, Intent, and Engagement. Add only when a new signal proves incremental prediction.

Do we still need lead scoring?

Yes. Account criteria set which companies to prioritize; lead scoring helps sequence which people to engage and how.

How do we prevent false positives?

Require Fit + Intent combinations, add recency decay, cap frequency, and down-weight generic content interactions.

How often should we tune criteria?

Review monthly. Compare bands on meetings, opps, win rate, ACV, and velocity; version changes with QA and rollback.

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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How Do You Define Scoring Criteria for Accounts?