Clean Your Contact Database
Clean Your Contact Database with Cleanlist's waterfall enrichment. Upload a CSV or connect your CRM — 30 free credits to start.
Start Free — 30 CreditsThe Problem
Your contact database is a liability when it is dirty. Marketing sends campaigns to dead addresses and tanks sender reputation. Sales calls disconnected numbers and loses selling time. RevOps builds routing rules on incorrect job titles, sending enterprise prospects to SMB reps. The root cause is rarely a single bad import — it is the gradual accumulation of entropy from dozens of data sources, each with different formatting standards and accuracy levels.
How Cleanlist Solves This
Cleanlist's contact cleaning goes beyond removing invalid records. It standardizes inconsistent data across all your sources: normalizing name capitalization, standardizing phone formats, canonicalizing company names, deduplicating across fuzzy-matched fields, and removing contacts that are unreachable, unengaged, or compliance-risky. The result is a uniform, trustworthy contact database that every team can rely on.
Inconsistent Job Titles Are the Silent Killer of Lead Routing Accuracy
Lead routing rules in most CRMs depend on job title fields to determine which rep or team gets the lead. But job titles in B2B are wildly inconsistent: 'VP of Sales,' 'Vice President, Sales,' 'VP Sales & Partnerships,' 'Head of Revenue,' and 'Chief Revenue Officer' may all describe the same seniority level but match different routing rules. Cleanlist's analysis of 2.3 million contact records from 90 customer databases found that 23% of contacts had job titles that would trigger incorrect routing in a typical round-robin or territory-based assignment system. The fix is title normalization — mapping all variations to a standardized seniority-function matrix (e.g., VP-level / Sales function). After normalization, routing accuracy improved to 94% for customers who implemented the cleaned title data in their CRM routing logic.
Source: Cleanlist title normalization analysis, 2.3M records from 90 customer databases, Q1 2026
How It Works
Import Contacts from Any Source
Connect your CRM, upload spreadsheets from different teams, or point Cleanlist at multiple data sources simultaneously. The tool reconciles records across sources and identifies where the same contact appears with conflicting data.
Data Profiling & Quality Score
Every record is scored on completeness (how many fields are filled), accuracy (how many fields pass validation), and freshness (how recently the data was confirmed). A summary histogram shows the distribution of quality scores across your database.
Standardization & Formatting
Names are capitalized consistently (handling McDonalds, O'Brien, van der Berg). Phone numbers are formatted to E.164. Addresses are parsed into structured components. Job titles are mapped to a canonical hierarchy for consistent segmentation.
Deduplication
Fuzzy matching identifies duplicate contacts across variations: nickname-to-formal-name mapping (Bob/Robert, Bill/William), company name variants (IBM/International Business Machines), and email domain aliases (microsoft.com/outlook.com for internal accounts).
Validation & Enrichment
Invalid emails and disconnected phones are flagged. Missing fields are optionally filled through waterfall enrichment. Contacts who have changed jobs are identified with updated employment information.
Export Clean Dataset
Download your cleaned contacts with all original columns preserved plus new quality-indicator columns. Suppressed contacts (invalid, duplicate, compliance-risk) are exported separately for your records and audit trail.
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 |
|---|---|---|
| Name standardization | UPPER/lower inconsistencies persist | Smart capitalization (handles McNames, O'Names, hyphenated) |
| Cross-source reconciliation | VLOOKUP between spreadsheets (error-prone) | Automatic matching across CRM, CSVs, and imports |
| Nickname resolution | Bob and Robert treated as different people | Mapped via 5,000+ nickname-formal name pairs |
| Time to clean 20K contacts | 2-3 days of spreadsheet work | Under 30 minutes (end-to-end automated pipeline) |
| Ongoing maintenance | Quarterly manual audits (if remembered) | Continuous validation + monthly re-verification |
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 clean 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 Cleanlist handle contacts that appear in both my CRM and an uploaded spreadsheet?
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Cleanlist uses multi-field fuzzy matching (name + email + company + phone) to identify the same contact across sources. When a match is found, the records are merged using the freshest and most complete data from each source. You can preview all proposed merges before they are applied.
What happens to contacts that fail validation?
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Failed contacts are not deleted — they are segmented into a 'Needs Attention' group with specific failure reasons (bounced email, disconnected phone, company no longer exists). You can then decide to re-enrich them with updated info, archive them, or review individually.
Ready to clean your contacts?
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