ICP Scoring

ICP scoring is a system for evaluating how closely a sales prospect matches a company's Ideal Customer Profile (ICP). An ICP score is calculated by comparing the prospect's attributes — company size, industry, role, pain signals, budget indicators, and behavioural intent — against a predefined criteria set. High ICP scores indicate the prospects most likely to close, renew, and expand.

ICP scoring is the numerical or categorical evaluation of a prospect's fit against your predefined Ideal Customer Profile criteria, used to prioritise sales effort toward the leads most likely to become high-value, long-term customers.

How ICP scoring works

ICP scoring follows a structured process that compares each prospect against a set of weighted criteria your sales and marketing teams define in advance.

1. Define your ICP criteria. Identify the attributes your best customers share: company size (employee count, ARR), industry, geography, tech stack, buyer role, and typical pain points. These become your scoring dimensions.
2. Assign weights to each criterion. Not all attributes matter equally. A $50M ARR company in your target vertical is worth more points than a $5M startup in the same vertical. Weight each criterion by its correlation with your historical close rate.
3. Collect prospect data. Source firmographic and demographic data from the form submission, LinkedIn profile, company website, or a data enrichment provider. The more complete the data, the more accurate the score.
4. Calculate the ICP score. Sum the weighted scores across all criteria and normalise to a 0–100 scale or a simple A/B/C/D tier for routing decisions.
5. Route and act on the score. High-scoring leads go straight to an AE or sales engineer, mid-tier leads enter a nurture sequence, and low-scoring leads are deprioritised. The score drives the action, not gut feel.

ICP criteria to score against

A robust ICP scoring model evaluates six distinct attribute types. Each adds signal; together they produce a reliable fit score.

• Firmographic: company size (employees, revenue), industry vertical, geography, company stage, and funding status.
• Technographic: the existing tech stack — the tools, platforms, and integrations a company already uses. High-fit prospects typically run complementary technologies.
• Demographic: buyer role and seniority. A VP of Sales evaluating a sales tool is a higher-fit signal than an intern doing research.
• Pain signals: stated problems that your product solves. A company that just announced a sales-expansion initiative is a better fit for a revenue tool than one in cost-cutting mode.
• Budget indicators: funding rounds, headcount growth rate, tech-spend signals, and fiscal-year budget cycles — evidence that a company has the resources to buy.
• Behavioural intent: pages visited, content consumed, demo engagement time, and return visits. Behavioural signals indicate active evaluation, not passive awareness.

Manual vs. automated ICP scoring

ICP scoring can be done manually by an SDR or RevOps analyst reviewing form submissions, or automatically by an AI system that scores in real time during the demo conversation. The contrast shows up across five dimensions.

• When it happens. Manual scoring runs after the form is submitted, during CRM review. Automated scoring runs in real time, during the demo itself.
• Who does it. Manual scoring depends on an SDR or RevOps analyst. Automated scoring is handled by an AI qualification engine.
• Data completeness. Manual scoring is limited to the fields a prospect actually filled in. Automated scoring draws on the full discovery context captured in conversation.
• Speed. Manual scoring takes hours to days. Automated scoring is instant.
• Consistency and scale. Manual scoring varies by rep and is capped by headcount. Automated scoring is consistent, rule-based, and runs across unlimited concurrent sessions.

ICP scoring vs. lead scoring

ICP scoring and lead scoring are complementary systems that answer different questions, and conflating them leads to poor routing decisions.

• ICP scoring measures fit — is this the right type of company? It draws on firmographic, demographic, and technographic data, and is best used for sales prioritisation and routing.
• Lead scoring measures intent — is this prospect actively buying? It draws on behavioural signals like page views, clicks, and email opens, and is best used for outreach timing and marketing automation.

The two are strongest together: high ICP fit plus a high lead score is the clearest trigger for immediate routing to a sales engineer.

How Floe automates ICP scoring during the demo

Traditional ICP scoring happens after a prospect fills out a form — by which point they may have lost interest or been contacted by a competitor. Floe's ICP scoring runs in real time, inside the demo conversation itself.

As the AI demo agent walks a prospect through the product, it gathers discovery data conversationally — company size, current tools, stated pain, buying timeline — and maps each answer against your ICP criteria, producing a score before the session ends. When the demo completes, your CRM receives a full qualification record: ICP score, tier, discovery summary, and recommended next action.

The result: your sales engineers only ever see leads that have already been scored, qualified, and ranked — and the qualification happened while the prospect was most engaged, not three days later. Closing that gap between intent and human follow-up is one of the most direct ways to improve your demo conversion rate.

FAQ

What is ICP scoring? ICP scoring is a system for evaluating how closely a sales prospect matches a company's Ideal Customer Profile (ICP). An ICP score is calculated by comparing the prospect's attributes — company size, industry, role, pain signals, budget indicators, and behavioural intent — against a predefined criteria set. High ICP scores indicate the prospects most likely to close, renew, and expand.

What is the difference between ICP scoring and lead scoring? ICP scoring evaluates fit — whether a prospect matches your ideal customer profile based on firmographic and demographic attributes. Lead scoring evaluates intent — how active and engaged a prospect is, based on behavioural signals like page views and email opens. The two are complementary: high ICP fit plus a high lead score is the ideal combination for sales prioritisation.

How does AI automate ICP scoring? AI automates ICP scoring by evaluating prospect attributes in real time during the demo conversation, rather than waiting for a human to review form submissions. It can analyse company size, industry, role, stated pain, and behavioural signals simultaneously and produce a score instantly, without relying on the prospect to self-report their data accurately in a form.

What attributes should I include in my ICP scoring model? A strong ICP scoring model typically evaluates six attribute types: firmographic (company size, industry, location), technographic (existing tech stack), demographic (role, seniority), pain signals (stated problems your product solves), budget indicators (company stage, funding, spend signals), and behavioural intent (pages visited, demo engagement, content consumed).