What Are the Main Account Scoring Models?
Account scoring models help revenue teams prioritize which accounts to pursue, when to engage, and how to allocate resources by combining ICP fit, intent, engagement, and buying-group signals into a governed, repeatable system.
The main account scoring models fall into six practical categories: (1) Fit/ICP scoring, (2) Engagement scoring, (3) Intent scoring, (4) Composite “Fit + Intent + Engagement” scoring, (5) Predictive propensity scoring, and (6) Tiered ABM scoring. The best teams don’t pick one— they define a simple core model (usually composite) and add a tiering layer that governs investment, plays, and SLAs across Marketing, Sales, and RevOps.
What “Good” Account Scoring Must Do
The Six Main Account Scoring Models
Use the table below to pick the right model for your GTM motion, data maturity, and ABM program stage. In practice, most teams run a composite model with a tiering overlay.
Model Overview: What It Uses, What It Outputs, and Where It Breaks
| Model | Best For | Primary Inputs | Output | Common Pitfall |
|---|---|---|---|---|
| 1) Fit / ICP Scoring | Early programs; territory planning; TAM focus | Firmographics, technographics, geo, revenue, employee band | “Should we pursue?” (Fit score) | High-fit accounts with no buying signal flood Sales |
| 2) Engagement Scoring | Lifecycle orchestration; measuring account activity | Web visits, email engagement, event attendance, product activity | “Are they interacting?” (Engagement score) | Noise from students, competitors, or non-buyers |
| 3) Intent Scoring | Identifying in-market accounts | Third-party intent, search topics, competitor comparisons, review-site spikes | “Are they researching?” (Intent score) | Treating topic interest as a buying decision without validation |
| 4) Composite (Fit + Intent + Engagement) | Most B2B teams; best balance of actionability + explainability | Weighted mix of ICP, intent, engagement + buying-group coverage | Priority bands (A/B/C) + “next best action” | Overweighting one signal (e.g., engagement) and inflating scores |
| 5) Predictive / Propensity | High-volume markets with strong historical CRM data | Closed-won patterns, stage velocity, activity sequences, enrichment | Probability to create pipeline / close | Black-box models that Sales doesn’t trust (or biased training data) |
| 6) Tiered ABM Scoring | 1:1, 1:few, and 1:many investment governance | Strategic value, whitespace, partner alignment, exec sponsorship signals | Tier assignment + budget/service level | Tiers become political and stop reflecting measurable outcomes |
How to Choose (and Operationalize) the Right Model
- Start with your decision: Define the action the score triggers (route to SDR, launch 1:few play, exec outreach, suppress spend).
- Split Fit vs. Readiness: Build Fit first (stable), then layer Readiness (intent + engagement) to time outreach.
- Weight by motion: Enterprise ABM leans on Fit + buying-group coverage; SMB velocity may lean more on engagement and speed.
- Make it explainable: Keep top drivers visible (e.g., “ICP match + surge on 3 topics + 2 buying roles engaged”).
- Define bands + SLAs: For each band (Hot/Warm/Cold), specify owners, timelines, plays, and exit criteria.
- Close the loop monthly: Compare scores to pipeline creation, stage conversion, and win rate; adjust weights and remove noisy signals.
Operational Snapshot: Turning Scores Into Pipeline
A composite model (Fit + Intent + Engagement) becomes powerful only when it routes accounts into repeatable plays: Tiered investment, SDR sequences, account-based content, and RevOps governance. The best programs treat scoring as an operating system—not a dashboard.
If your current scoring doesn’t change outreach behavior, it’s not a model—it’s a report. Tie scores to plays, SLAs, and resource allocation to make prioritization real.
Frequently Asked Questions about Account Scoring Models
Make Account Scoring Operational
Turn scoring into a governed system that prioritizes accounts, aligns teams, and reliably creates pipeline—without overwhelming Sales.
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