Clearbit Alternatives for Lead Scoring: Data You Can Use

For lead scoring you need two kinds of input: fit (who the company is) and engagement (what they did). Clearbit alternatives mostly cover fit through firmographic lookups. Pair a company data source with your own behaviour data and a simple points model before paying for anything more complex.

Fit versus engagement

A good score separates two questions. Fit asks whether the company resembles your best customers. Engagement asks whether the person is acting like a buyer. Mixing them into one opaque number hides which one drove a decision.

Most Clearbit-era workflows used enrichment for the fit half. That is the part replaceable by a company enrichment API. The engagement half comes from your product analytics, form fills and email activity, none of which any enrichment vendor can provide.

Which fields earn their place

Field	Use in scoring	Where to get it	Caveat
Employee range	Core fit signal	Company lookup	Bands, not exact numbers
Industry	Core fit signal	Company lookup	Taxonomies differ by vendor
Country	Territory and eligibility	Company lookup	Use ISO-2 codes
Website status	Filters dead companies	Some lookups	BusinessMCP shows last-measured date
Revenue or funding	Optional fit signal	Separate vendor	Not carried by narrow APIs
Technographics	Optional fit signal	Separate vendor	Not stated by BusinessMCP
Intent	Timing signal	Intent vendors	Costs more, noisier

BusinessMCP’s company data API returns name, industry, size, location, phone and LinkedIn page, and its comparison page states plainly that it has no revenue, funding, technographic or intent fields. If your model leans on those, you need another source or a different model.

A simple points model

Start small. A transparent model beats a clever one nobody trusts.

1. List the attributes of your last 20 best customers: size band, industries, countries.
2. Assign points for fit, for example 30 for target size, 20 for target industry, 10 for supported country.
3. Add engagement points: pricing page view, demo request, repeat visits.
4. Set a threshold where sales follows up.
5. Review monthly with sales: which high scores were junk, which low scores became customers?

Key takeaway: a four-field fit score that sales understands will outperform a sophisticated model they ignore. Complexity can wait for evidence.

Wiring it to enrichment

On a new lead, take the domain from a work email, call the lookup and store the result. A miss should not drop the lead; give it a neutral fit score and mark it for manual review. Form-based enrichment covers the on-submit pattern. For anonymous visitors, you can start from an IP-based company match as described in Clearbit Reveal alternatives, then enrich the resulting domain.

At BusinessMCP’s stated rates a found company costs $0.02, and a miss is free, so scoring thousands of leads a month is inexpensive. It is still worth caching results so you do not pay twice for the same domain.

Missing data is data

Unknown companies are common: small firms, newer companies, brand platforms that return name only. Treat “unknown” as its own category with its own rule rather than a zero. Some of your best small customers will be unknown to every vendor.

Pitfalls

• Overfitting to enterprise. If most of your revenue comes from small teams, penalising small size will filter out your market.
• Stale scores. Company data changes; recompute fit periodically.
• Treating the score as the decision. It prioritises; humans decide.
• Privacy. Keep scoring at company level where possible, and check regional rules before profiling individuals. BusinessMCP’s people tools exclude several regions, so do not assume contact-level data is available everywhere.
• No feedback loop. Without outcomes tied back to scores, you cannot improve them.

Alternatives for the contact layer

If you need people-level fields such as titles, contact databases are the territory of ZoomInfo and Apollo. Our vendor comparison lays out the pricing models. Be aware that those platforms are built around seats or credits and annual terms.

Putting it together

Take your fit model, enrich on submit, store the source and timestamp, add behaviour events, and review outcomes monthly. When you evaluate vendors for the fit half, use the scorecard in how to choose a company enrichment API, and test on a sample of your real leads rather than a demo list.

Worked example: scoring an inbound lead

Concrete numbers make the model easier to reason about. The weights below are invented for illustration; yours should come from your own customer history.

Signal	Rule	Points
Employee range	50 to 500 employees	30
Industry	In your top three verticals	20
Country	In a territory you serve	10
Email domain	Work email, not free-mail	10
Pricing page visit	Within seven days	15
Demo request	Submitted	40

A lead from a 120-person logistics company in a served country who visited pricing scores 30 + 20 + 10 + 10 + 15 = 85 and goes straight to a rep. A lead whose domain returns no company scores 10 + 15 = 25 from behaviour and email alone, and lands in nurture with a flag for a quick manual look. The model is crude, and that is the point: anyone on the team can explain why a lead scored what it did.

Getting the fit data

For the fit half, a domain-to-company lookup is enough. BusinessMCP’s comparison with Clearbit’s API describes the company lookup it offers: domain, URL or work email in, firmographic fields out, found-only billing, and an explicit statement of what it does not return. That last part is important for scoring, because it tells you up front that revenue, funding and intent rules need a different source. Its free company lookup tool lets you check a few of your own accounts before you design rules around the fields.

When you test, include leads that converted and leads that did not. A fit model that cannot separate those two groups on historic data will not work going forward, however good the vendor is. If your best customers turn out to be small companies that no database names, you have learned something valuable: your score should lean on behaviour, not on firmographics.