Account Scoring: How to Build a B2B Model That Works

Lead scoring was built for a world where one person found you, downloaded something, and eventually talked to sales. B2B deals no longer work that way. Forrester's 2024 research found that on average 13 people are involved in a purchasing decision. When you score each of them separately, you get thirteen lukewarm leads instead of one hot account. Account scoring fixes that by rolling every signal up to the company that will actually sign the contract.

This post explains how to build an account scoring model that sales trusts: what goes into it, why fit and engagement should never be added together, how to count buying-committee breadth, and a worked example you can adapt.

Account Scoring vs Lead Scoring

The difference is the unit, and it changes almost everything downstream.

• Lead scoring rates one person. It rewards that person's own behavior: emails opened, assets downloaded, pages viewed. It is good at deciding when a known contact is ready for a call.
• Account scoring rates one company. It aggregates behavior across everyone from that company, identified or not, and combines it with how well the company fits your ideal customer profile. It is good at deciding which companies deserve sales and marketing attention right now.

You need both. Account scoring decides where to focus, lead scoring decides who to call inside that account. If you only have lead scoring today, keep it: the sections below show how to roll its signals up to the company.

The Mistake: One Number for Fit and Engagement

A common account scoring model adds fit points and engagement points into one total out of 100. It looks tidy. It produces bad decisions.

A perfect-fit enterprise account that has never visited your site and a poor-fit startup whose intern read twenty blog posts can land on the same score. Sales sees two "65s" and has no idea that one is a strategic target with no intent and the other is noise with lots of activity. The single number hides the only thing the rep needed to know. The same argument applies to individual leads, covered from the outbound side in this explainer on fit score vs intent score.

Keep two scores on two axes instead:

• Fit score (0-100): how closely the company matches your ICP. Changes slowly. Based on firmographics and technographics.
• Engagement score (0-100): how actively the company is researching right now. Changes daily. Based on website behavior, intent data, and campaign responses, with decay.

Then act on the combination, not the sum. A simple grid does the job:

This grid also gives marketing a clear job for the high-fit, low-engagement corner, which is where most account-based programs should spend their budget.

What Goes into the Fit Score

Fit comes from your ideal customer profile, and it should be built from your closed-won data, not from what leadership hopes the ICP is. Typical inputs:

• Industry (weighted by your win rate per vertical)
• Employee count or revenue band
• Geography (where you can sell, support, and comply)
• Tech stack (uses a platform you integrate with, or a competitor you displace) (our guide to building a B2B martech stack around personalization covers which integrations matter)
• Business model signals (for example, has a sales team, runs paid acquisition, sells B2B)

Weight each input by how strongly it separates won deals from lost ones. For example, if most of your closed-won revenue comes from software companies with 200-2,000 employees, those two attributes deserve most of the fit points. Our guide to building an ICP for website personalization walks through the analysis.

What Goes into the Engagement Score

Engagement should measure buying activity, not general interest. Three kinds of input matter:

Depth: what the account looked at

Weight late-stage pages heavily: pricing, comparison pages, integration docs, security pages, case studies from the account's industry. Weight blog traffic lightly. A company reading ten blog posts is learning about a topic. A company reading pricing and your SOC 2 page is evaluating a vendor.

Breadth: how many people are involved

This is the input lead scoring can never capture, and in our view it is the most useful one. Two people from the same company on your site in the same week means a conversation is happening internally. Three people from different functions means a buying group has formed. With the typical purchase involving many people (the Forrester figure above is 13), even partial visibility into that group is valuable.

Score breadth by distinct visitors, with a bonus when you can infer different roles (for example, one visitor reading technical docs and another reading pricing).

Recency: when it happened

Engagement without decay is the second most common modelling mistake. An account that binged your site in March and went silent is not hot in October. Halve engagement points every 14-30 days depending on your sales cycle. Shorter cycles need faster decay.

If you buy third-party intent data, add it as a separate engagement input with a cap, so a topic surge alone cannot push an account into the "sales now" column without any first-party activity. Our post on using intent data for website personalization covers how to blend the two. Our guide to intent data providers explains how to pilot one before you rely on it.

Which Signals Indicate Buying, Not Browsing

Not every visit is a buying signal. Someone reading three blog posts may be researching an article of their own. The useful distinction is between first-party signals (what the account does on your site, which you observe directly) and third-party signals (topic research elsewhere, reported by an intent data vendor, which you have to take on trust).

• First-party, strong: pricing, comparison and alternatives pages, integration or API docs, security and compliance pages, implementation or onboarding pages, case studies from the account's own industry. These are evaluation pages.
• First-party, pattern: repeat visits within a short window. One visit a month is interest; three visits in a week is an evaluation. Speed matters too: an account that covers pricing, two case studies and the security page in two days is moving faster than one that reads the same pages over a quarter.
• Third-party: topic surges, review-site activity, hiring for a role your product serves. Useful for spotting accounts before they visit, weaker as proof they are buying from you.
• Negative: careers-page-only visits, competitor domains, students and out-of-market regions. Without deductions, a competitor studying your site can trigger a sales alert.

Example engagement weights, consistent with the worked example below. Treat them as a starting point to back-test, not a benchmark:

Two rules keep the engagement score honest. Cap any single form fill or download at around 10 points, so one gated asset cannot push an account to "sales now" on its own. And apply decay only to engagement, never to fit: a company does not stop matching your ICP because it went quiet.

Worked Example: Three Accounts, One Week

An illustrative model applied to three fictional accounts. The weights are made up for the example; yours should come from your own win data.

Fit weights: target industry 35, employee band 30, integrates with your CRM 20, target region 15.

Engagement weights: pricing view 15, comparison or security page 15, industry case study 10, each additional distinct visitor 10 (max 30), blog view 2 (max 10), intent surge 10. Points halve every 21 days.

Check the arithmetic on the first row: pricing 15, security page 15, case study 10, and three additional visitors at 10 each makes 70. Added into one total out of 200, the fintech would land at 88, comfortably second, and a rep could easily call it as a hot lead. On two axes the picture is clearer: strong fit, but nobody there is evaluating yet. It belongs on the target list for outbound and personalized content. The agency is not worth an SDR's afternoon.

Using Account Scores on Your Website

Scores are usually treated as a sales tool, but they are just as useful for deciding what the website shows. A few rules that follow from the grid:

• High fit, low engagement: show industry-specific proof and a low-commitment CTA (benchmark report, ROI calculator). The goal is to earn a second visit.
• High fit, high engagement: replace content CTAs with a direct meeting offer and name the account's industry in the hero.
• Low fit, high engagement: route to a self-serve path or a lighter plan instead of a sales demo.

Markettailor's account-based marketing features apply rules like these on the page: once a company is identified, its fit and engagement tier decides which hero, proof, and CTA it sees.

Automating the Handoff to Sales

A score that nobody acts on is decoration. The handoff needs three things defined in writing and built into your CRM or marketing automation:

1. Thresholds. Use the grid, not a single cut-off. "Sales now" is the high-fit, high-engagement cell; the medium-fit, high-engagement cell goes to an SDR to qualify first. Re-check the cut-offs against the back-test each quarter, because engagement distributions shift as your content grows.
2. Routing. If the account has an owner in the CRM, route to the owner. If not, route by territory or segment, and use round-robin only as the last step. An alert in a shared inbox is an alert nobody owns. Our guide to routing website leads covers matching leads to accounts before assignment.
3. SLA. Agree a response time per tier, for example same business day for "sales now" and within a week for SDR qualification, and report on it. Accounts move out of the buying window while alerts wait.

Every alert should carry its reasons: company and fit attributes, the score with its top contributing signals ("3 visitors this week, pricing twice, security page"), the pages visited in the last seven days ranked by intent, known contacts, and a suggested angle based on what the account read. A rep who can see why an account is hot will call it; a rep who sees only "82" will not.

How to Calibrate the Model

A scoring model is a hypothesis. Test it against history before sales relies on it.

1. Export the last 12 months of closed-won and closed-lost opportunities with the account's fit attributes.
2. Score each account as it would have looked 30 days before the opportunity was created.
3. Check that closed-won accounts cluster in the high-fit, high-engagement corner. If they do not, your weights are wrong.
4. Look at the accounts in "sales now" that never became opportunities. Those are your false positives. Find the input that pushed them up and reduce its weight.
5. Re-run the back-test every quarter. ICPs drift as the product changes.

If you want a starting worksheet for the weights and thresholds, our lead scoring model template uses the same fit and behavior structure and works at the account level with minor changes.

Four Mistakes That Make Sales Ignore the Score

Scoring the whole database. Scoring 40,000 accounts produces a long tail of meaningless 12s and 17s. Score the accounts that pass a minimum fit bar, and leave the rest unscored. A smaller, cleaner list gets used.

Hiding the reasons. A score of 82 tells a rep nothing about what to say. Every score should come with its top three contributing signals ("3 visitors this week, pricing viewed twice, uses HubSpot"). Reps trust what they can explain to themselves.

Counting existing customers as prospects. A customer's employees reading your docs will light up the engagement score every week. Route customer accounts to a separate expansion or health model, or they crowd out new business. Route them to a customer experience instead; see personalization for retention and churn reduction.

Never retiring an input. Inputs get added after every quarterly review and almost never removed. After a year the model has 30 inputs, half of them redundant. When you add an input, remove one, and keep the total under a dozen.

Start Small

You do not need a predictive model to begin. Four fit attributes, five engagement inputs, decay, and a two-axis grid is a better starting point than a single-number model. Build it in a spreadsheet, back-test it, then move it into your CRM or personalization platform once sales agrees the top 20 accounts look right.

To see how fit and engagement tiers can drive what each account sees on your site, book a walkthrough from our pricing page.