What is ICP Scoring?
ICP scoring is a lead qualification method that rates prospects based on how closely they match your Ideal Customer Profile, using firmographic, technographic, and behavioral attributes.
ICP scoring (Ideal Customer Profile scoring) is a systematic approach to evaluating and ranking leads based on their resemblance to your best-fit customer profile. Unlike traditional lead scoring, which often relies heavily on behavioral signals like email opens and page visits, ICP scoring emphasizes firmographic and technographic fit - characteristics that indicate whether a company is fundamentally a good match for your product, regardless of engagement activity.
An ICP is typically defined by a combination of attributes: company size (revenue and headcount), industry or vertical, geographic location, technology stack, growth stage, and organizational structure. ICP scoring assigns weighted values to each of these attributes and calculates a composite score for every lead or account in your database. A company that matches all criteria scores highest, while one that only partially matches scores lower.
The advantage of ICP scoring over behavioral lead scoring is that it identifies good-fit companies even before they engage with your marketing. A perfect-fit company that has never visited your website is still a valuable prospect, while a poor-fit company that downloads every whitepaper is still unlikely to convert. The best qualification systems combine both ICP scoring (fit) and behavioral scoring (intent) for a complete picture.
Building an effective ICP scoring model requires clean, enriched data. You cannot score companies on revenue if you do not have revenue data, or on technology stack without technographic information. This is why enrichment and ICP scoring are deeply connected - the quality of your scoring model is directly limited by the completeness of your underlying data.
Cleanlist provides ICP scoring as a built-in capability that works on top of its enrichment engine. Teams define their ICP criteria and weights, and Cleanlist automatically scores every record as it is enriched. This eliminates the gap between data collection and qualification - records enter the CRM already scored and prioritized. The scoring model can be adjusted as teams learn more about what drives conversion, with Cleanlist re-scoring existing records when the model changes.
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See how Cleanlist handles icp scoring →Frequently Asked Questions
What is the difference between ICP scoring and lead scoring?
ICP scoring evaluates firmographic and technographic fit - how closely a company matches your ideal customer profile based on attributes like size, industry, and technology. Traditional lead scoring focuses on behavioral engagement like email opens, website visits, and content downloads. The best qualification systems use both: ICP scoring for fit and behavioral scoring for intent.
What data points are used in ICP scoring?
Common ICP scoring attributes include company revenue, employee headcount, industry or vertical, geographic location, technology stack, growth rate, funding stage, and organizational structure. The specific attributes and their weights depend on your business - a company selling enterprise software might weight revenue heavily, while a startup might prioritize growth rate and funding stage.
How do I build my first ICP scoring model?
Start by analyzing your best existing customers - look for common firmographic and technographic patterns among your highest-value, fastest-closing accounts. Define 5-8 key attributes and assign weights based on correlation with success. Cleanlist can help by enriching your customer list with firmographic data, making it easier to identify the patterns that define your ideal customer profile.
Related Terms
Firmographic Data
Firmographic data describes the characteristics of a business organization, including industry, revenue, employee count, location, and company structure - the B2B equivalent of demographic data.
Lead Enrichment
Lead enrichment is the process of automatically appending additional data to incoming leads - such as company details, contact information, and firmographics - to enable faster qualification and more personalized outreach.
Data Enrichment
Data enrichment is the process of enhancing existing data records with additional information from external sources, improving accuracy, completeness, and usefulness for sales and marketing teams.