What is ICP scoring in B2B outbound automation?
ICP scoring is the practice of programmatically evaluating each prospect against your Ideal Customer Profile criteria and assigning a score that determines routing priority. Most companies have an ICP document. Few have it running as a live filter on every new and enriched lead. This page covers the scoring dimensions that matter and what it looks like to automate them.
The definition
What is ICP scoring?
ICP scoring translates your Ideal Customer Profile definition into a numeric or categorical score that can be computed automatically for every contact and company in your database. The score combines firmographic fit (size, industry, geography), technographic match (existing tools), and behavioral signals (intent, hiring, funding) into a single priority rating.
Without automated scoring, reps prioritize manually based on gut feel, recency, or whoever happens to appear at the top of a list. That inconsistency creates pipeline quality problems: high-fit leads go unworked while low-fit leads consume capacity.
The ICP document you have in Notion is a description of who you want to sell to. ICP scoring is that same definition, running automatically, on every contact, in real time.
Scoring dimensions
What does an ICP scoring model actually evaluate?
| Dimension | Signal source | Weight | Why it matters |
|---|---|---|---|
| Company size | Employee count, revenue band | High | The most reliable firmographic filter. If your ICP is 50 to 500 employees, contacts outside that range rarely convert regardless of other signals. |
| Industry and vertical | Primary NAICS or SIC code, self-reported | High | Industry controls for whether your solution is even applicable. Mismatched verticals create pipeline noise that clogs forecasting. |
| Tech stack | BuiltWith, Clay technographic data | Medium-high | Which tools a company already uses predicts integration fit and sales cycle complexity. A HubSpot shop is a fundamentally different deal than a Salesforce shop. |
| Growth signals | Hiring pace, funding recency | Medium | Companies growing fast have buying pressure. Companies with recent funding have budget. These signals modify base firmographic score upward. |
| Intent overlay | PredictLeads, content consumption | Medium | Intent data suggests the company is actively evaluating solutions in your category. High intent on an otherwise marginal ICP fit can still produce a conversation worth having. |
Company size
HighSignal source
Employee count, revenue band
Why it matters
The most reliable firmographic filter. If your ICP is 50 to 500 employees, contacts outside that range rarely convert regardless of other signals.
Industry and vertical
HighSignal source
Primary NAICS or SIC code, self-reported
Why it matters
Industry controls for whether your solution is even applicable. Mismatched verticals create pipeline noise that clogs forecasting.
Tech stack
Medium-highSignal source
BuiltWith, Clay technographic data
Why it matters
Which tools a company already uses predicts integration fit and sales cycle complexity. A HubSpot shop is a fundamentally different deal than a Salesforce shop.
Growth signals
MediumSignal source
Hiring pace, funding recency
Why it matters
Companies growing fast have buying pressure. Companies with recent funding have budget. These signals modify base firmographic score upward.
Intent overlay
MediumSignal source
PredictLeads, content consumption
Why it matters
Intent data suggests the company is actively evaluating solutions in your category. High intent on an otherwise marginal ICP fit can still produce a conversation worth having.
The payback
What does automated ICP scoring change for outbound teams?
About which leads to work first. Scoring creates a ranked list automatically, updated as new data flows in
Less manual review time when classification and scoring run automatically on every enriched contact
To measurable ROI once automated scoring is driving rep prioritization and routing decisions
How we build it
How does SeaDance build ICP scoring systems?
We start by translating your ICP narrative into a structured scoring rubric: specific thresholds for each dimension, weighted based on what your historical win data says actually predicts conversion. If you do not have enough closed-won data, we start with reasonable priors and refine over the first 60 to 90 days.
The scoring logic runs on n8n, triggered after enrichment completes. For each record, the workflow evaluates available fields against the rubric, calls an LLM where the classification is fuzzy (industry matching, company description parsing), and writes a score and reasoning back to the CRM.
Reps can see why a record was scored the way it was. The scoring dimension breakdown is exposed in CRM fields, not hidden in a black box. That transparency builds trust in the system and makes it easier to calibrate over time.
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