CRM Hygiene Playbook

Use this playbook to establish and maintain CRM data quality standards. Bad CRM data breaks everything downstream: reporting is wrong, routing fails, sequences go to the wrong people, and leadership loses trust in the numbers. This playbook covers both the initial cleanup and the ongoing maintenance process that keeps data clean.

CRM Data Quality Assessment

Before fixing anything, measure how bad the problem is. Run these checks against your current CRM data.

Data Completeness Audit

For each object (Contacts, Companies, Opportunities), check what percentage of records have the required fields filled in:

Contacts:

Field	Required?	% Complete	Target	Status
Email	Yes	{{X}}%	> 99%
First Name	Yes	{{X}}%	> 99%
Last Name	Yes	{{X}}%	> 99%
Job Title	Yes	{{X}}%	> 90%
Phone Number	Recommended	{{X}}%	> 70%
Company (association)	Yes	{{X}}%	> 95%
Lead Source	Yes	{{X}}%	> 95%
Lead Status	Yes	{{X}}%	> 99%
Owner	Yes	{{X}}%	100%
Last Activity Date	Auto-tracked	{{X}}%	100%

Companies:

Field	Required?	% Complete	Target	Status
Company Name	Yes	{{X}}%	100%
Domain	Yes	{{X}}%	> 95%
Industry	Yes	{{X}}%	> 85%
Employee Count	Yes	{{X}}%	> 80%
Annual Revenue	Recommended	{{X}}%	> 60%
Country	Yes	{{X}}%	> 90%
Owner	Yes	{{X}}%	100%
ICP Tier	Recommended	{{X}}%	> 70%

Opportunities:

Field	Required?	% Complete	Target	Status
Opportunity Name	Yes	{{X}}%	100%
Amount	Yes	{{X}}%	100%
Close Date	Yes	{{X}}%	100%
Stage	Yes	{{X}}%	100%
Contact Association	Yes	{{X}}%	100%
Company Association	Yes	{{X}}%	100%
Source / Campaign	Yes	{{X}}%	> 90%
Next Step	Recommended	{{X}}%	> 80%
Competitor	Recommended	{{X}}%	> 60%

Duplicate Detection

Object	Total Records	Estimated Duplicates	Duplicate Rate	Target
Contacts	{{X}}	{{X}}	{{X}}%	< 3%
Companies	{{X}}	{{X}}	{{X}}%	< 2%

Common duplication causes:

• Same person entered by marketing form and SDR manual entry
• Company name variations (e.g., “Salesforce” vs “Salesforce, Inc.” vs “salesforce.com”)
• Import files with records that already exist
• Integrations creating duplicates on sync errors

Data Accuracy Spot Check

Randomly sample 50 contact records and 50 company records. Check:

• Is the email address valid and deliverable? (Run through a verification tool like NeverBounce or ZeroBounce)
• Does the job title match what shows on LinkedIn?
• Is the person still at the company listed?
• Is the company still in business?
• Is the employee count roughly accurate?

Accuracy rate: {{X}}% — Target: > 85%

Phase 1: Initial Cleanup

Step 1: Deduplication

• Run deduplication tool (Salesforce Duplicate Management, HubSpot dedup, Dedupely, or Ringlead)
• Set merge rules: most recent data wins, except for Lead Source (keep the first)
• Review auto-detected duplicates before merging — spot-check 20 records
• Merge confirmed duplicates
• Document merge rules for ongoing use

Deduplication priority order:

1. Companies (dedupe these first — they anchor contact records)
2. Contacts (after company dedup, some contact dupes resolve automatically)
3. Opportunities (rarely duplicated, but check)

Step 2: Field Standardization

Standardize free-text fields that should be picklists:

Field	Current State	Standardized Values	Action
Industry	Free text, 200+ variations	15 standard values + “Other”	Map existing values to standard list, convert to picklist
Job Title	Free text	Keep as free text but add a Job Level picklist: C-Level, VP, Director, Manager, IC	Create mapping rules
Country	Mix of codes and names	ISO 2-letter codes (US, GB, DE)	Bulk update + validation rule
Lead Source	40+ values, many unused	Consolidate to 10 primary sources	Map and merge, add validation
Lead Status	Inconsistent use	Open, Working, Qualified, Unqualified, Nurture, Disqualified	Reset and enforce

Step 3: Enrich Missing Data

For records missing critical fields (title, company size, industry), use data enrichment tools:

• Run enrichment on all contacts missing Job Title (batch update)
• Run enrichment on all companies missing Employee Count and Industry
• Verify enrichment accuracy on a 50-record sample before applying to full database
• Set up ongoing auto-enrichment for new records entering the CRM

Step 4: Archive Stale Data

• Identify contacts with no activity in the last 12 months AND no open opportunity
• Verify they have not recently visited your website (check marketing automation data)
• Move confirmed stale contacts to an “Archived” status — do not delete
• Identify companies with no associated contacts or opportunities — flag for review
• Remove test records, internal employee records used for testing, and obviously fake entries

Phase 2: Ongoing Maintenance

Daily Automated Checks

Set up these automations in your CRM or workflow tool:

Check	Trigger	Action
New lead missing required fields	Lead created with empty Title, Company, or Source	Notify owner via Slack; hold from routing until fields complete
Duplicate detected on create	New record matches existing on email or company domain	Flag for review; do not auto-merge
Opportunity past close date	Close date < today AND stage is not Closed Won/Lost	Notify AE and manager; require stage update
Contact bounced	Email bounce recorded	Update status to “Invalid Email”; remove from active sequences
Company data changed	Enrichment detects company size or industry change	Update record; notify account owner if material change

Weekly Manual Checks (15 minutes)

• Review new leads created this week — spot-check 10 records for completeness
• Check for opportunities stuck in the same stage for 30+ days
• Review and merge any flagged duplicates from the automated detection
• Check integration sync logs for errors (CRM ↔ MAP, CRM ↔ Sequencing tool)

Monthly Data Health Report

Metric	This Month	Last Month	Target	Trend
Overall field completeness (contacts)	{{X}}%	{{X}}%	> 90%
Overall field completeness (companies)	{{X}}%	{{X}}%	> 85%
Duplicate rate (contacts)	{{X}}%	{{X}}%	< 3%
Stale records (no activity 12+ months)	{{X}}	{{X}}	Decreasing
Integration error count	{{X}}	{{X}}	< 10
Records enriched this month	{{X}}	{{X}}

Include this health report in the Operational Health section of your GTM metrics dashboard.

Quarterly Deep Clean

• Re-run the full Data Completeness Audit from Phase 1
• Verify email deliverability on contacts used in active sequences (batch verification)
• Audit picklist values for new entries that do not match standards
• Review and update ICP scoring criteria based on recent closed-won data
• Check that all integrations are still syncing correctly
• Update enrichment rules if ICP or target market has changed

CRM Governance Rules

Document these rules and share with every CRM user:

Record Creation Rules

Rule	Details
No manual company creation without checking for existing record first	Search by domain, not name
All contacts must be associated with a company	No orphan contacts
Lead Source is set at creation and never changed	First source wins
Lead Status must be updated within 48 hours of any outreach	No leads stuck in “Open” for more than 2 days

Field Update Rules

Rule	Details
Opportunity Amount must reflect actual expected value, not aspirational	Updated at every stage change
Close Date can only move forward (to a later date) with manager approval logged	Prevents sandbagging
Stage changes require a logged activity within 24 hours explaining the change	Accountability for pipeline movement
Contact job title updates from enrichment override manual entry	Enrichment data is more current

Data Ownership

Object	Primary Owner	Responsible For
Contact/Lead records	SDR or AE assigned	Keeping activity logs current, updating status
Company records	Account owner	Ensuring firmographic data is accurate
Opportunity records	AE	Amount, stage, close date, next steps
Data quality rules & enforcement	RevOps	Validation rules, automation, reporting

How to Customize

• For teams using Salesforce, set up validation rules that prevent records from being saved without required fields. This is more effective than relying on people to fill in data after the fact. Also use Salesforce’s built-in Duplicate Management rules rather than third-party tools for the first pass.
• For teams on HubSpot, use required properties on forms and in deal pipelines. HubSpot’s Operations Hub (Professional or Enterprise) includes data quality automation that can fix formatting issues automatically (capitalize names, format phone numbers, standardize country fields).
• For teams with large databases (100K+ contacts), prioritize cleanup by segment. Start with contacts associated with active opportunities and target accounts. Then clean contacts in active nurture sequences. Leave archived/inactive contacts for last. The biggest business impact comes from cleaning the data your team actually uses daily. Connect your cleanup efforts to the broader workflow automation to prevent future data quality issues.