What is Account Scoring?

Account scoring aggregates signals from multiple data layers into a composite score — typically on a 0–100 scale — for each target company. The three foundational dimensions are Fit, Intent, and Engagement.

Fit is the static baseline: does this company match your ICP? Inputs include firmographics (industry, headcount, annual revenue, geography), technographics (what tools they run — accounts already using Salesforce often convert faster into Salesforce-adjacent products), and organizational signals like headcount growth rate or recent funding.

Intent measures timing: is the account actively researching your category right now? Intent inputs come from first-party signals (repeated visits to your pricing page, demo views, gated content downloads) and third-party intent data from providers like Bombora, which monitors content consumption across 5,000+ B2B publisher sites. Intent is volatile by design — it spikes and decays — which is why best-practice models keep it as a separate dimension from the stable fit score.

Lead scoring evaluates an individual contact based on that person's demographic profile and their engagement with your brand. Account scoring evaluates the entire organization — aggregating signals from every person at the company, including anonymous visitors — to judge the account's collective buying readiness.

The practical consequence is that lead scoring can miss a real buying signal entirely: five different stakeholders from the same company each visiting your pricing page once would look like five lukewarm leads under lead scoring, but like one very hot account under account scoring.

For deals above roughly $10K ACV, most revenue teams use both in tandem: account scoring sets the priority tier (which companies deserve attention at all), and lead scoring handles routing within that tier (which individual to contact first). Forrester's State of Business Buying 2024 found an average of 13 internal stakeholders involved in a B2B purchasing decision, making account-level scoring the more reliable foundation for prioritization.

Modern account scoring pulls from four signal categories. Firmographic signals — company size, industry, revenue, location, headcount growth — determine baseline ICP fit and are relatively stable. Technographic signals reveal what the account already buys and runs, which predicts both fit and expansion potential.

Behavioral signals measure first-party engagement: website visits, content downloads, email interactions, webinar attendance, repeat demo views, and pricing-page sessions. These are strong intent proxies because they reflect deliberate action taken by people at the account.

Third-party intent data — sourced from providers like Bombora or G2 buyer intent — surfaces accounts researching your category across the open web, even before they have visited your site. This is often the earliest signal that an in-market window is opening. CRM history (previous deals, past outreach, lost-deal reasons) rounds out the picture with institutional memory that raw signal data cannot provide on its own.

Without scoring, sales teams default to either working their entire addressable market equally (exhausting and ineffective) or relying on gut feel and whoever called in last (inconsistent). Account scoring replaces both with a repeatable, data-backed prioritization system.

Gartner research confirms that account-based approaches increase pipeline conversion rates by approximately 14% and drive a 28% increase in overall account engagement. The mechanism is straightforward: reps spend more time on accounts that are both a good fit and actively in-market, which improves meeting-to-opportunity rates and shortens sales cycles.

Predictive models trained on historical closed-won data compound this advantage further. By detecting non-obvious patterns — for example, a particular combination of technographic stack, intent surge, and hiring signal — predictive models surface accounts that rules-based scoring would never flag. Practitioner benchmarks consistently show 2–5x higher conversion rates in top-scored account tiers versus overall averages when a well-validated predictive model is in place.

Start by analyzing your closed-won customers to identify the firmographic, technographic, and behavioral patterns that appear most often. This becomes the ICP backbone of your fit score. Assign weighted point values to each attribute — industry match might carry more weight than geography, for example — and normalize to a 0–100 scale.

Layer intent and engagement scores on top as separate dimensions. Most mature teams run a composite readiness score that multiplies fit by a time-decayed intent modifier: an account with a strong ICP fit and a current intent surge scores higher than a fit account that has been cold for 90 days.

Validate the model quarterly: Tier A accounts should convert to pipeline at meaningfully higher rates than Tier B, with shorter cycle times. If they do not, adjust your weights. Major recalibration is also warranted when you enter a new vertical, launch a new product line, or see a significant shift in win-rate patterns — all of which change what your ideal customer actually looks like.

Komo — the AI Revenue Engine — is built around the principle that the right account, contacted at the right signal moment, converts at dramatically higher rates than cold outreach ever will. Account scoring is the foundation of that timing: Komo monitors the signals that drive score changes — funding events, job postings for relevant roles, intent surges, and website activity — and surfaces accounts the moment their score crosses a threshold worth acting on.

Because Komo keeps a human in the loop on every send that matters, account score changes do not automatically fire generic outreach. Instead, they trigger a researched, personalized draft that a rep reviews and approves. This means a score change translates into a better message, not just faster volume.

The result is that reps spend their time on accounts that scoring has already validated as in-market and ICP-fit, while Komo handles the monitoring, research, and drafting work between CRM and inbox that would otherwise consume the day.