B2B data cleaning is the process of identifying and correcting errors in business databases, including duplicate records, invalid email addresses, inconsistent formatting, outdated contact information, and corrupted fields. On average, CRM databases contain 10-25% duplicate records and 30% of B2B contact data goes stale every year due to job changes, company closures, and acquisitions. Professional data cleaning typically improves email deliverability by 15-30 percentage points and recovers hundreds of hours of sales rep time previously wasted on bad records.
What Data Cleaning Actually Looks Like
A $950M medical device company came to us with a problem. They'd invested heavily in contact enrichment, purchasing data from five different vendors: BetterContact, Clay, FullEnrich, FastPeople, and ZoomInfo. Each vendor delivered. The data itself was fine.
The problem was everything else.
They had 120,000+ contact records across six states. Seventy different column names trying to describe the same 22 pieces of information. "BUSINESS NAME" in one file. "Business Name" in another. "business name" in a third. One vendor's column was labeled "Fuell Enrich Email" because someone made a typo that became permanent.
Phone numbers were losing their first digit during formatting. Thousands of records with numbers that just wouldn't dial. LinkedIn URLs existed in a dozen different formats, all pointing to the same people but looking like separate contacts to their CRM.
And they couldn't just merge everything together. They'd paid each vendor for their data. Each email, each phone number needed its attribution preserved. You don't spend serious money on five data sources just to throw away the paper trail.
What we built
Smart column mapping that handled all 70+ variations and consolidated them down to 22 clean, standardized fields. The mapping worked case-insensitively and used fuzzy matching to catch typos and inconsistencies.
A multi-phase matching algorithm. Exact matching for the straightforward stuff. Fuzzy matching for company name variations. LinkedIn URL normalization so "www.linkedin.com/in/johndoe" and "linkedin.com/in/johndoe" stopped looking like different people.
Cell-level validation that caught the phone corruption issue before it shipped. Vendor attribution preserved in separate columns so they could track which data came from where.
The results
What would have taken a contractor 60 weeks, we completed in days. That's $120,000+ in labor costs they didn't spend. More importantly, the phone number corruption alone would have cost them an estimated $600,000 in missed opportunities. Bad numbers erode sales team trust in the entire database. When reps don't trust the data, they stop using it.
We caught it before it shipped.
What We Clean
Every database accumulates problems differently. Some have obvious duplicates. Some have subtle formatting issues that break downstream systems. Here's what we look for:
- Duplicate detection and merge. Not just exact matches. We use fuzzy matching to catch "Acme Corp" versus "Acme Corporation" versus "ACME Co." and merge them while preserving the best data from each record.
- Email validation. We check deliverability, not just format. A properly formatted email that bounces is worse than useless because it damages your sender reputation.
- Phone standardization. Everything converted to consistent +1 XXX-XXX-XXXX format. We catch corrupted numbers, strip invalid characters, and flag numbers that don't match expected patterns.
- Address normalization. Consistent formatting, abbreviation standardization, postal code validation. Your mail actually reaches people.
- Job title mapping. "VP of Sales" and "Vice President, Sales" and "Sales VP" all become the same standardized category. Makes segmentation and targeting actually work.
- Company name standardization. Legal entity names validated. Parent-subsidiary relationships mapped. Your account hierarchy makes sense.
- LinkedIn URL normalization. All the different ways people format LinkedIn profiles resolved to consistent, deduplicated records.
- Multi-value field protection. Some fields contain pipe-separated values that can't be processed like regular text. We identify and protect them so nothing gets corrupted.
Data Cleaning by Industry
Every industry has unique data challenges. Healthcare deals with NPI validation and provider credentialing. Financial services requires compliance-grade audit trails. SaaS companies struggle with freemium-to-paid tracking across product-led funnels.
We've cleaned data for companies across these industries:
Technology
Commerce & Logistics
Other Industries
Don't see your industry? Contact us—we've likely worked with similar data challenges.
How It Works
Step 1: Send us your data. Export from Salesforce, HubSpot, Outreach, Marketo, or send us Excel/CSV/Google Sheets. We work with whatever format you have.
Step 2: We analyze and provide a cleaning plan. Before we touch anything, you'll know exactly what we're going to do. What fields we'll standardize. What duplicates we've identified. What issues we found.
Step 3: AI processing + human QA. Automation handles the scale. Humans handle the judgment calls. Every project gets both. We don't ship "pretty good" data.
Step 4: You get clean data back. Same format you sent, or we can push directly to your CRM. Your choice.
"Email deliverability went from 72% to 94%."— Hansen, VP of Sales
Pricing
Data cleaning starts at $0.02-0.05 per record, depending on complexity. Minimum project is $500, which covers up to 5,000 records for basic cleaning.
We'll give you a fixed quote after reviewing your data. No surprises, no hourly billing that spirals out of control. You know the cost before we start.
Common Questions
How long does data cleaning take?
Most projects complete in 24-48 hours. Larger datasets (100K+ records) may take 3-5 days. We'll give you a specific timeline after reviewing your data.
What file formats do you accept?
We work with exports from Salesforce, HubSpot, Outreach, Marketo, and any spreadsheet format (Excel, CSV, Google Sheets). If your system can export data, we can clean it.
How is this different from ZoomInfo or other data platforms?
ZoomInfo sells access to their contact database. We clean your data. The relationships you've already built, the customer history that matters to your business. Different problem, different solution. We also don't require annual contracts or recurring fees.
What about data security?
Encrypted transfers, no data sharing with third parties, deletion after project completion unless you request otherwise. Happy to sign NDAs or discuss specific compliance requirements.
Ready to Stop Fighting Your Data?
Two options:
Not sure yet? Tell us about your data challenges. We'll give you an honest assessment of where your biggest gaps are and whether we can help.
Ready to fix this? Send us a sample file. We'll show you what clean data looks like.
Related: Data Enrichment | Data Analysis | Pricing