How to Clean Your CRM Data: A Step-by-Step Guide for RevOps

Your CRM is lying to you. Not intentionally - but those pipeline forecasts, lead scores, and routing rules are only as accurate as the data underneath them.

Most CRMs are packed with duplicates, outdated contacts, incomplete records, and inconsistent formatting. RevOps teams inherit this mess and are expected to build reliable systems on top of it.

This guide shows you how to clean your CRM data systematically - and keep it clean.

Why CRM Data Quality Matters for RevOps

Dirty CRM data breaks everything RevOps tries to build:

Lead scoring fails. If job titles are inconsistent ("VP Sales" vs "Vice President of Sales" vs "VP, Sales"), your scoring model can't accurately weight seniority.

Routing breaks. Territory assignment based on geography doesn't work when half your records have "US" and half have "United States."

Forecasting is wrong. Duplicate opportunities inflate pipeline. Missing close dates make predictions impossible.

Attribution is unreliable. If contacts aren't properly associated with accounts, you can't measure which campaigns drive revenue.

The downstream impact is significant. Sales teams chase bad leads. Marketing wastes budget on wrong segments. Leadership makes decisions on faulty data.

The CRM Data Quality Assessment

Before cleaning, understand what you're dealing with.

Step 1: Run a data quality audit

Check these metrics across your CRM:

Step 2: Identify the worst offenders

Run reports to find:

• Fields with lowest completion rates
• Most common duplicate patterns
• Records with no activity in 12+ months
• Contacts missing email or phone

Step 3: Quantify the business impact

Connect data quality to business metrics:

• How many leads failed routing due to missing fields?
• What's the bounce rate on marketing campaigns?
• How many sales calls went to wrong numbers?

This builds the business case for cleanup resources.

Phase 1: Deduplication

Duplicates are the most damaging data quality issue. They split activity history, confuse sales reps, and inflate metrics.

Find duplicates

Look for matches on:

• Exact email match
• Fuzzy name + company match
• Phone number match
• LinkedIn URL match

Most CRMs have built-in duplicate detection:

• Salesforce: Setup > Duplicate Management
• HubSpot: Contacts > Actions > Manage Duplicates

For complex matching, export to a spreadsheet or use a deduplication tool.

Define merge rules

Before merging, decide which record wins:

Execute merges carefully

1. Start with exact email matches (low risk)
2. Review fuzzy matches manually
3. Merge in batches of 100, verify results
4. Document merge decisions for audit trail

Phase 2: Standardization

Inconsistent data formats break segmentation, routing, and automation.

Standardize job titles

Job titles are notoriously inconsistent. Common variations:

• VP Sales / VP of Sales / Vice President Sales / Vice President of Sales
• SDR / Sales Development Rep / Sales Development Representative / BDR
• CEO / Chief Executive Officer / Founder & CEO / Co-Founder/CEO

Use Smart Columns to normalize titles automatically:

• Map variations to standard values
• Extract seniority level as separate field
• Identify department from title

Standardize geography

Create consistent location formats:

• Country: Use ISO codes (US, GB, CA) or full names consistently
• State: Abbreviations (CA) or full names (California), pick one
• City: Proper capitalization, no abbreviations

Standardize company names

Company name variations cause duplicate accounts and broken reporting:

• Acme Corp / Acme Corporation / Acme Corp. / ACME
• With or without Inc., LLC, Ltd.
• Acquired companies (old name vs new name)

Normalize to legal company names or create a preferred display name field.

Standardize phone numbers

Format all phones consistently:

• E.164 format: +1 555 123 4567
• Or your preferred format: (555) 123-4567

Remove extensions to a separate field. Flag international numbers.

Phase 3: Enrichment

Clean data isn't enough - you need complete data. Enrichment fills gaps and updates stale information.

Identify enrichment candidates

Records that need enrichment:

• Missing email address
• Missing phone number
• Missing company information
• Job title but no seniority level
• No activity in 12+ months (may have changed jobs)

Run waterfall enrichment

Single-source enrichment leaves gaps. Waterfall enrichment queries 15+ providers to maximize fill rates.

What enrichment adds:

• Verified work email
• Direct dial phone
• Current job title and company
• Company firmographics (size, revenue, industry)
• LinkedIn profile URL

Update stale records

B2B data decays at 22.5% per year. People change jobs, companies get acquired, phone numbers change.

Set up quarterly re-enrichment for:

• Records with no activity in 6+ months
• Key accounts (refresh regardless of activity)
• Records where email bounced

Phase 4: Validation Rules

Prevent bad data from entering in the first place.

Required field validation

Make critical fields required at the point of entry:

• Email (with format validation)
• Company name
• Lead source

But be careful - too many required fields reduce form conversion.

Format validation

Enforce formats on entry:

• Email: Must contain @ and valid domain
• Phone: Must be proper length for country
• URL: Must start with http:// or https://

Real-time verification

Add verification at the point of entry:

1. Lead submits form with email
2. Email is verified in real-time
3. Invalid emails are rejected with error message

This prevents bad data from ever entering your CRM.

Picklist standardization

Replace free-text fields with picklists where possible:

• Industry (dropdown, not free text)
• Country (dropdown with ISO values)
• Lead source (defined list)

Picklists ensure consistency and enable reliable reporting.

Phase 5: Ongoing Maintenance

Data quality is a process, not a project.

Daily: Automated hygiene

Set up automation to handle routine cleanup:

• New leads automatically enriched
• Duplicates flagged for review
• Invalid emails quarantined

Weekly: Spot checks

Review a sample of recent records:

• Are required fields being filled?
• Is data formatting consistent?
• Are there new duplicate patterns?

Monthly: Quality metrics

Track data quality KPIs:

• Duplicate creation rate
• Field completion rates
• Email validity rate
• Enrichment success rate

Quarterly: Deep audit

Full data quality assessment:

• Re-run duplicate detection
• Re-enrich stale records
• Review and update validation rules
• Train team on data entry standards

CRM-Specific Workflows

Salesforce Cleanup

1. Enable Duplicate Management: Setup > Duplicate Rules
2. Install Data.com Clean (or third-party enrichment)
3. Create validation rules: Setup > Object Manager > [Object] > Validation Rules
4. Use Flow Builder for automated enrichment triggers

HubSpot Cleanup

1. Use Operations Hub for data quality automation
2. Enable automatic duplicate detection: Settings > Duplicates
3. Create data quality command center: Reports > Dashboards
4. Use workflows to trigger enrichment on lead creation

Integration with Cleanlist

Connect Cleanlist to your CRM for automated enrichment:

HubSpot: Native integration, one-click setup Salesforce: Managed package from AppExchange Other CRMs: API integration or Zapier

Configure triggers:

• Enrich on lead creation
• Re-enrich when email bounces
• Quarterly refresh for all records

Measuring Success

After implementing these processes, track improvement:

Frequently Asked Questions

How long does a full CRM cleanup take?

For a database of 50,000 records, expect 2-4 weeks for initial cleanup. This includes deduplication, standardization, and enrichment. Ongoing maintenance takes 2-4 hours per week.

Should I delete old records or archive them?

Archive rather than delete. Old records may re-engage, and you need history for attribution. Create an "Archived" status and exclude from active campaigns.

How do I get sales reps to maintain data quality?

Make it easy and visible. Reduce required fields to essentials, add real-time validation, and show reps how bad data hurts their own numbers (bounced emails, wrong phone numbers).

What's more important: completeness or accuracy?

Accuracy. A smaller database with verified, accurate data outperforms a larger database with questionable quality. Start with accuracy, then build completeness through enrichment.

How often should I re-enrich data?

Quarterly for most databases. Monthly for high-velocity sales teams. Always re-enrich when you detect signals of data decay (bounced emails, returned mail, failed calls).

Clean CRM data isn't a nice-to-have - it's the foundation of everything RevOps builds. Start with deduplication, standardize formats, enrich gaps, and build processes to keep it clean. Automate enrichment with Cleanlist and spend your time on strategy instead of cleanup.