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What Is Fit-Based Account Scoring?

Fit scoring ranks accounts by their ideal customer profile (ICP) match—firmographics, technographics, needs, and constraints—so ABM and Sales focus on the right companies before layering intent or engagement.

Build ICP & Tiers Operationalize Scoring & Routing

Fit-based scoring evaluates how closely an account matches your ICP—industry, size, region, tech stack, compliance, and buying complexity. Teams define a governed taxonomy, convert attributes into features (e.g., ARR band, tool overlap, regulatory needs), weight them for importance, and output a tier or score band (A/B/C). Fit provides a stable baseline to prioritize territories, segment programs, and cap over-reliance on volatile signals like intent spikes.

What Goes Into Fit?

Firmographics: industry/SIC, employee count, revenue band, growth rate, HQ & regions served.
Technographics: installed products, versions, cloud/provider, data residency, integration surface area.
Operating Model: centralized vs. BU-led, procurement model, partner reliance, contract cycle length.
Regulatory/Compliance: industry standards, privacy posture, audit cadence, required certifications.
Problem/Use-Case Fit: pain alignment, complexity, urgency proxies, whitespace potential by product.
Negative Fit: disqualifiers like conflicting tech, risk tier, or non-ICP verticals (suppression rules).

From ICP to Action

Follow this sequence to create explainable fit tiers that Sales trusts—and marketing can activate.

Define → Source → Engineer → Weight → Band → Orchestrate → Govern

  • Define ICP & exclusions: Align on ideal industries, sizes, tech compatibilities, AND clear non-ICP rules.
  • Source attributes: CRM, enrichment, technographic vendors, product telemetry, partner/PRM data.
  • Engineer features: Normalize names, create binary flags (has_X), indices (tech_fit), and risk factors.
  • Weight & cap: Assign business weights and caps so no single attribute dominates the outcome.
  • Band into tiers: Convert numeric scores to A/B/C with thresholds; document why each account is tiered.
  • Orchestrate plays: A-tier → AE+SDR SLAs and ABM activation; B-tier → nurture+partner; C-tier → suppress or self-serve.
  • Govern & improve: Monthly review wins/losses by tier, drift checks, and backtesting; update weights and rules.

Fit Scoring Capability Maturity Matrix

Capability From (Ad Hoc) To (Operationalized) Owner Primary KPI
ICP Definition Vague ideal list Documented ICP/anti-ICP with acceptance criteria Rev Council/PMM Win Rate by Tier
Data Quality Manual lookups Automated enrichment & freshness SLAs RevOps/Data Coverage, Freshness
Model & Weights Point-based guesswork Explainable weights with factor caps & audits Marketing Ops/Analytics Precision/Recall, Lift
Banding & SLAs No tiers A/B/C tiers tied to sales SLAs & ABM rules Sales Ops/ABM Speed-to-Action, Acceptance Rate
Activation & Suppression Spray & pray Program and media mapped to tiers; suppression for misfit ABM/Demand Gen Cost per SQL, ROMI
Drift & Fairness Set-and-forget Monthly drift checks, bias controls, holdouts Rev Council Forecast Accuracy

Client Snapshot: Tiering That Sales Believes

After launching a governed fit model with A/B/C tiers and suppression rules, a B2B team lifted SQL acceptance and reduced cost per opportunity in target segments. Explore outcomes: Comcast Business · Broadridge

Align fit tiers to journeys in The Loop™ Guide, then activate A-tiers with ABM programs and route with Lead Management.

Frequently Asked Questions about Fit-Based Scoring

How is fit different from intent or engagement?
Fit is who the account is—relatively stable. Intent and engagement are when they’re researching or responding—volatile and best layered on a fit baseline.
How many tiers should we use?
Three tiers (A/B/C) keep it explainable. Add sub-tiers only if SLAs and programs differ meaningfully.
Can we use ML for fit?
Yes—ML can learn weights and identify non-linear fit signals—but keep explainability and business caps so Sales understands “why.”
What about bad-fit suppression?
Implement anti-ICP rules to suppress non-target verticals, conflict tech, or risk tiers—saves media and protects SDR time.
Which metrics prove fit is working?
Win rate, SQL rate, and ACV by tier; acceptance rate and speed-to-first-touch for A-tiers; reduced cost per opportunity.

Make Fit Your Confident First Filter

We’ll define your ICP, engineer features, and connect tiers to SLAs and programs—so every rep knows where to start.

Build ICP & Tiers Operationalize Scoring & Routing
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