What to Expect from a Data Cleaning Project

You've decided your CRM needs professional cleaning. Maybe the bounce rates forced the issue. Maybe a CRM migration is coming. Maybe your sales leader finally asked why the pipeline report doesn't add up.

Whatever the trigger, you're about to hand your data to someone else. Here's what the process looks like so you know what to expect at each stage.

Stage 1: Scoping (Day 1)

Before any work starts, the provider needs to understand your database. A good scoping conversation covers:

• Database size: How many contact, account, and lead records?
• Known issues: Duplicates? Bouncing emails? Inconsistent job titles? Missing fields?
• Services needed: Deduplication only? Full cleaning? Cleaning plus enrichment?
• CRM platform: Salesforce, HubSpot, Dynamics, or other?
• Custom requirements: Specific fields to standardize? Particular merge rules? Records to exclude?

From this conversation, the provider should give you a fixed price and timeline. If they can't give you a number after understanding your scope, that's a red flag.

Stage 2: Data Export (Day 1-2)

You'll export your data from your CRM and send it to the provider. Most providers accept CSV or Excel files. A few can connect directly via API.

What to export:

• Contacts/Leads: All fields including name, email, phone, title, company, address
• Accounts/Companies: Company name, industry, size, address, website
• Include record IDs: So cleaned records can be matched back to your CRM on import

Stage 3: Processing (Days 2-6)

This is where the actual cleaning happens. A typical full-scope project runs through these steps in order:

Deduplication

Fuzzy matching identifies records that represent the same person or company despite differences in spelling, formatting, or completeness. Duplicate clusters are merged into golden records that keep the most complete and recent data from each duplicate.

Email Validation

Every email address is checked via SMTP verification. Results are categorized: valid, invalid (hard bounce), risky (catch-all domain), or role-based (info@, admin@). Invalid emails are flagged for removal.

Phone Verification

Phone numbers are checked against carrier databases. Disconnected numbers, landlines, and fax numbers are flagged separately from active direct dials and mobile numbers.

Field Standardization

Job titles, company names, addresses, and industry codes are normalized to consistent formats. "VP of Sales" and "Vice President, Sales" become the same value. State names and abbreviations become consistent.

Enrichment (if included)

Missing fields are filled from external data sources: phone numbers, email addresses, job titles, company size, industry, and other firmographic data.

Stage 4: Review (Day 6-7)

Before you import anything, the provider should deliver:

1. The cleaned data file formatted for your CRM's import tool
2. A change log showing what was modified, added, or flagged on each record
3. Summary statistics:
   • How many duplicates were found and merged
   • How many emails were validated vs. flagged as invalid
   • How many phone numbers were verified vs. disconnected
   • How many fields were standardized
   • Fill rate improvements from enrichment (if applicable)
4. Recommendations for preventing the same issues from recurring

Take time to spot-check 20-30 records against your CRM. Verify that merges were done correctly, that standardization makes sense for your use case, and that no critical data was lost.

Stage 5: Import (Day 7-8)

Import the cleaned data back into your CRM. Most providers deliver files formatted for your specific CRM's import tool (Salesforce Data Loader, HubSpot native import, etc.).

Best practices for import:

• Back up your CRM first. Always have a rollback point.
• Import in batches if your database is large (50,000+ records). Start with a small batch to verify everything looks right.
• Use record IDs for matching. Update existing records rather than creating new ones.
• Test your automation after import. Make sure lead scoring, routing, and sequences still work with the standardized data.

What Good Looks Like After

Within a week of importing cleaned data, you should notice:

• Email bounce rates drop to under 3%
• List segmentation returns consistent counts
• Lead routing sends records to the right reps
• Reps stop complaining about finding the same prospect under three different records
• Pipeline reports start matching what your sales manager expects

Frequently Asked Questions

How long does a data cleaning project take?

3-7 business days for databases under 100,000 records. Dedup only is 2-3 days. Full cleaning (dedup + email + phone + standardization) is 5-7 days.

What should a data cleaning deliverable include?

The cleaned file, a change log, summary statistics, and recommendations for preventing future issues. If a provider doesn't include a change log, ask for one.

How do I prepare my CRM for a data cleaning project?

Export contacts, accounts, and leads as CSV files with all fields. Include record IDs. Note any custom fields or special requirements.