Account Scoring
Scoring at the account level rather than the individual, reflecting that B2B purchases are made by groups.
What it is for
Account scoring ranks accounts by some combination of fit — how closely they resemble customers you serve well — and intent, meaning observable signals that they are in market. The purpose is allocation: finite selling capacity should go to the accounts most likely to convert and stay.
It differs from lead scoring in its unit. Lead scoring ranks individuals and is usually about timing; account scoring ranks organisations and is usually about targeting. In an account-based motion the account score is the one that governs where a rep spends a week.
A score nobody acts on is a reporting artefact
How to build one that holds up
Start from won and retained customers, not from won customers. Fit that predicts a sale but not a renewal produces a pipeline of churn.
Separate fit from intent and keep them as two numbers. Fit is stable and changes slowly; intent is volatile and decays in weeks. Averaging them into one figure hides which is driving the rank.
Use attributes you can populate for the whole addressable market, not just for accounts already in the CRM. A score that only works on known accounts cannot direct prospecting.
Validate against outcomes before deploying. Score last year's accounts with the model and check that the top decile actually won at a higher rate — if it did not, the model encodes a belief rather than a pattern.
Set a review cadence. Fit criteria drift as the product and the market change, and a model nobody revisits silently encodes the business you had two years ago.
Where it misleads
Scores built only on won deals inherit your historical targeting. If you have only ever sold to one segment, the model will conclude that segment is the only good one.
Intent data is noisy and frequently measures research rather than buying. A spike from a team writing an internal report looks identical to one from a team about to purchase.
Firmographic fit ages badly. Headcount and funding fields are stale in most databases, so a score built on them is partly measuring data freshness.
A single blended score hides disagreement. A high-fit, zero-intent account and a low-fit, high-intent one can produce the same number and need completely different treatment.
RELATED TERMS
Buyer Intent
Signals suggesting an account is actively researching a purchase, drawn from on-site behaviour or third-party data.
Ideal Customer Profile (ICP)
The definition of the accounts your product serves best, grounded in the observed characteristics of customers who buy quickly, stay, and expand.
Lead Scoring
A model assigning a numeric value to leads based on fit and behaviour, so attention goes to the ones most likely to convert.
Segmentation
Dividing the market into groups that warrant different treatment — different motion, pricing, coverage or support model.
Territory Design
Dividing accounts among sellers so each has a patch with enough potential to support a quota, and so coverage is neither duplicated nor absent.
COMMON QUESTIONS
- What is account scoring?
- Ranking accounts by fit — how closely they resemble customers you serve well — and by intent, meaning signals that they are in market. The point is allocation: finite selling capacity should go to the accounts most likely to convert and stay. It differs from lead scoring in its unit: accounts rather than individuals, targeting rather than timing.
- What is the difference between lead scoring and account scoring?
- Lead scoring ranks individual people and usually answers 'is this person ready now'. Account scoring ranks organisations and usually answers 'is this company worth working at all'. In an account-based motion both exist and do different jobs — the account score decides where a rep spends the week, the lead score decides who to call first inside that account.
- Should fit and intent be combined into one score?
- Keep them separate. They behave differently — fit is stable and changes over quarters, intent is volatile and decays in weeks — and blending them produces a number where a high-fit, no-intent account is indistinguishable from a low-fit, high-intent one. Those two need entirely different treatment, so the score should preserve the difference rather than average it away.
- Why do reps ignore the account score?
- Usually because it has never been validated against outcomes, and they know it. If you can show that last year's top-decile accounts won at a materially higher rate, the score earns trust quickly. If you cannot, the reps are making a reasonable judgement and the fix is validation rather than enforcement.
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