Score Your Leads with Enriched Data
Score Your Leads with Enriched Data with Cleanlist's waterfall enrichment. Upload a CSV or connect your CRM — 30 free credits to start.
Start Free — 30 CreditsThe Problem
Most lead scoring models are built on incomplete data and produce unreliable results. When 40% of your lead records are missing firmographic fields like company size and industry, your scoring model is making qualification decisions based on partial information. The result: high-fit enterprise leads get low scores because their records are sparse, while small-company leads with complete records (from better webform fills) get artificially high scores.
How Cleanlist Solves This
Cleanlist enables data-driven lead scoring by enriching every lead with the firmographic, technographic, and demographic data that scoring models need. Before you score, Cleanlist fills the gaps: company size, revenue range, industry, tech stack, funding stage, and job seniority are appended to each lead. The result is a scoring model that evaluates every lead on the same complete dataset — eliminating the 'data completeness bias' that plagues most scoring implementations.
Lead Scoring Without Enrichment Misqualifies 31% of Leads
Cleanlist ran a controlled analysis across 12 customer lead scoring implementations in Q1 2026. For each customer, we compared scoring accuracy before enrichment (using only the data present in their CRM) versus after enrichment (with firmographic, technographic, and seniority data filled in). The results were striking: before enrichment, 31% of leads were scored in the wrong tier — either high-value leads scored as cold due to missing data, or low-fit leads scored as hot because they filled out more form fields. The largest source of misqualification was missing company size data: leads from companies in the target employee count range were scored 18 points lower on average when the employee count field was empty. After enrichment, misqualification dropped to 8%, and the correlation between lead score and eventual conversion improved from 0.34 to 0.61. The practical impact: sales teams wasted 27% less time on unqualified leads and responded to genuinely high-fit leads 40% faster on average.
Source: Cleanlist lead scoring analysis, 12 customer implementations, Q1 2026
How It Works
Enrich Leads with Scoring-Ready Data
Before scoring, Cleanlist enriches every lead with the fields your scoring model needs: company employee count, revenue, industry, sub-industry, tech stack, funding stage, job title, and seniority level. This eliminates scoring gaps caused by missing data.
Define Ideal Customer Profile Criteria
Set your ICP parameters: target company sizes, industries, technologies, and seniority levels. Cleanlist scores each lead against your ICP on a 0-100 fit scale based on how many criteria match.
Firmographic Fit Scoring
Each lead's company data is scored against your ICP: right industry (+20), right employee count range (+15), right revenue range (+15), uses complementary technology (+10). Scores are additive and transparent — you see exactly why each lead received its score.
Contact-Level Scoring
The lead's individual profile is scored: seniority level (C-suite = high, IC = low), department alignment (does their function match your buyer persona?), and email type (verified work email = higher than personal or generic).
Composite Score & Tier Assignment
Firmographic and contact scores are combined into a composite lead score. Leads are tiered: Hot (80-100, immediate follow-up), Warm (50-79, nurture sequence), and Cold (below 50, long-term nurture or disqualify).
Route Scored Leads
Scored leads sync to your CRM with score values and tier assignments. Hot leads are routed to the appropriate rep or team based on territory, deal size, or product interest. Scoring runs automatically on new leads as they enter your system.
Key Benefits
98% Email Accuracy
Real-time email verification catches bounces, catch-alls, and disposable addresses before they cost you.
15+ Data Providers
Waterfall enrichment across multiple providers means higher match rates than any single-source tool.
No Code Required
Upload a CSV, connect your CRM, or use the API. Works however your team prefers.
30 Free Credits
Start enriching immediately. No credit card, no sales call, no commitment.
Manual Process vs Cleanlist
| Feature | Manual | Cleanlist |
|---|---|---|
| Scoring data completeness | Scores only the fields you already have (often 60% complete) | Enriches to 95%+ completeness before scoring |
| Scoring criteria | Basic: title keyword match + company name recognition | Multi-dimensional: firmographic + technographic + seniority + department |
| Score transparency | Black-box number with no explanation | Itemized breakdown showing exactly which criteria contributed |
| New lead scoring speed | Batch process (daily or weekly) | Real-time scoring as leads enter via API or CRM |
| False positive rate (high-score bad fits) | High — incomplete data inflates scores for verbose form fillers | Low — uniform data completeness removes completeness bias |
Frequently Asked Questions
How accurate is Cleanlist's data?
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Cleanlist achieves 98% email accuracy through real-time verification and cross-referencing across 15+ data providers. Every record is validated before delivery.
How many free credits do I get?
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Every new account starts with 30 free credits. Each credit processes one record (enrichment, verification, or lookup). No credit card required to start.
What data providers does Cleanlist use?
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Cleanlist uses waterfall enrichment across 15+ providers including major B2B data sources. The system automatically selects the best provider for each record to maximize match rates.
Can I connect my CRM?
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Yes. Cleanlist integrates with HubSpot, Salesforce, and other CRMs. Enriched data syncs back automatically. You can also upload CSV files or use the REST API.
How long does it take to score my data?
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Most jobs complete in under 2 minutes for up to 10,000 records. Larger files are processed in batches with progress updates.
How does enrichment improve lead scoring accuracy?
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Lead scoring models can only evaluate data that exists. When 40% of leads are missing company size, industry, or job title, the model either skips those criteria (reducing accuracy) or assigns default values (introducing bias). Cleanlist enriches leads to 95%+ field completeness before scoring, so every lead is evaluated on the same comprehensive dataset. This eliminates the 'data completeness bias' where leads with more form fields filled score higher regardless of actual fit.
Can I use Cleanlist scoring alongside my existing HubSpot or Salesforce lead score?
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Yes. Cleanlist's score can be written to a custom field in your CRM, running alongside your native lead score. Many customers use Cleanlist's firmographic fit score as one input into their broader scoring model, which also includes behavioral signals (page views, email opens, content downloads) from their CRM or marketing automation platform.
What is 'data completeness bias' in lead scoring?
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Data completeness bias occurs when leads with more filled-in fields receive higher scores simply because more scoring criteria can be evaluated. A startup founder who fills out a long webform gets a higher score than an enterprise VP who submits only name and email — even though the VP is a better fit. Enriching all leads to the same completeness level before scoring eliminates this bias.
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