Firmographic enrichment

Use firmographic enrichment to answer whether an account fits the market you can serve. Start with company attributes, turn them into a fit layer, and add buyer behavior as a separate intent layer when deciding where to spend attention. Firmographic data belongs at the foundation of account segmentation and covers industry, size, geography, and revenue.¹ Keep fit and intent separate because a single blended number can mislead,² while effective models combine fit and intent.³

Define the fit layer

Choose the decision before the fields. A field belongs in the model when its value can change account qualification, routing, or priority.

Use industry, employee count, revenue, and location in the core fit layer.⁴ Add growth stage and headcount trajectory when growth is part of the account definition.⁵ Keep the core set short enough for a rep to understand why an account received its score without opening several records.

Ask which company attributes would move an account into or out of the segment you want to work.

Fill missing attributes

Compare the required field set with the account records you already have. This shows whether the problem is missing data, inconsistent fields, or a scoring rule with nothing useful to read.

When customer account data is insufficient, invest in an enrichment tool to populate fields such as industry, employee size, and annual revenue.⁶ Enrich accounts with the core attributes your routing depends on, including employee count, industry, and location.⁷ Available company properties can include annual revenue, city, company domain name, employee range, industry, number of employees, revenue range, total money raised, and year founded.⁸

Give each attribute its own field so you can filter and score it. Use a dedicated data enrichment tool when the work needs to be streamlined and automated.⁹ Ask whether the tool can populate the fields your model requires and whether the output lands in the account record your team already works from.

Move on when the records contain enough information to classify fit, or when missing values are visible as missing. A blank field that looks like a poor-fit score will distort the queue.

Score with fit and intent

Firmographic fields show whether an account resembles the market you want. Behavioral signals show whether someone connected to that account has done something that warrants attention.

Fit scores qualify contacts and companies through property values such as company size and annual revenue.¹⁰ Engagement scores qualify contacts and companies through actions and interactions such as website visits, newsletter subscriptions, CTA clicks, and marketing email opens.¹¹ Build custom lead scores based on CRM record actions or properties.¹²

Keep the fit and intent outputs visible separately. Fit-only scoring can miss high-intent buyers outside the stated ICP, while intent-only scoring can send unqualified browsers to sales, so effective models combine fit and intent.³ A combined fit and intent score can help order work, but keep the underlying signals available for inspection.

Put score properties where the team will use them. They can feed segments, workflows, and reports.¹³ Ask whether the score explains why the account is being prioritized or only produces a ranking.

Route the account to action

Employee count

If fewer than 100 employees

SMB

If 100 to 499 employees

Mid-market

If 500 or more employees

Enterprise

Employee count

• If fewer than 100 employees, SMB
• If 100 to 499 employees, mid-market
• If 500 or more employees, enterprise

Scoring matters when it changes what happens to an account. Define the action attached to each fit and intent outcome before turning on the model.

Score properties assign values to records and can help evaluate which leads are likely to become customers.¹⁴ The total lead score determines which leads reach a representative first.¹⁵ Use clear priority labels or routing bands so the person receiving the account can act without reconstructing the calculation.

Employee count can drive explicit routing. A documented model sends companies with fewer than 100 employees to SMB, companies with 100 to 499 employees to mid-market, and companies with 500 or more employees to enterprise.¹⁶ Another documented model groups companies into Low, Medium, and High priority.¹⁷ Treat those labels as an operating choice, then set the boundaries that match your coverage model.

Ask what should happen when an account has strong fit and weak intent, or weak fit and strong intent. The answer should be visible in the route, queue, or follow-up rule attached to the score.

What not to do

Keep the failure modes visible while building the model. Each one can make a complete enrichment project produce a poor outbound queue.

• Define scoring with sales and marketing together. The model requires alignment between them, with meetings and communication.¹⁸
• Keep blank fields out of the negative fit signal. If a record has no values for the model fields, place it in an Unknown bucket.¹⁹
• Do not use manual web crawling as the plan for scaled prospecting. It may work for a handful of leads but is not suitable at scale.²⁰
• Do not spread effort by gut feel. Gut-feel qualification spreads effort across leads of different quality, while a calibrated model concentrates the fastest response on leads most likely to close.²¹

Before the next outbound block, test the route on records with complete data and records with gaps. Change the rules when the output sends the wrong accounts forward.