Contact Data Accuracy Audit Checklist
Audit the accuracy of your contact database with this checklist covering field completeness, freshness, deliverability, and enrichment gaps.
Field Completeness Audit
Calculate fill rates for all key fields
easyCheck percentage completion for: email, phone, job title, company, industry, company size, LinkedIn URL, and location. Export and analyze in a spreadsheet.
Identify contacts with less than 50% field completion
easyCreate a segment of contacts with major data gaps. These records are likely too incomplete for effective outreach or segmentation.
Check for dummy or placeholder data
easySearch for obviously fake data: test@test.com, 555-555-5555, 'N/A', 'Unknown', or blank fields disguised as populated ones.
Data Freshness Check
Identify contacts not updated in 12+ months
easyFind records where no field has been updated in over a year. These contacts likely have outdated information that needs re-enrichment.
Cross-check job titles against LinkedIn
mediumSpot-check a sample of contacts against their LinkedIn profiles to estimate what percentage have changed roles since your last update.
Re-verify email addresses on aging records
easyRun email verification on contacts that haven't been verified in 6+ months. Flag newly invalid addresses for removal or re-enrichment.
Accuracy Scoring
Calculate your overall data quality score
mediumCreate a weighted score based on: field completeness (30%), email deliverability (30%), data freshness (20%), and duplicate rate (20%).
Compare accuracy across data sources
mediumIf your contacts come from multiple sources (web forms, imports, enrichment, manual entry), compare data quality by source to identify the weakest inputs.
Set data quality benchmarks and targets
easyBased on your audit, set specific improvement targets: 'Increase email fill rate to 90%', 'Reduce bounce rate to under 2%', 'Eliminate 100% of dummy data.'
Pro Tips
- A contact with a valid email but wrong job title is worse than one with no title — bad data leads to wrong messaging
- Use Cleanlist's Data Quality Calculator (free tool) to quickly score your database health
- Track data quality metrics monthly as a dashboard KPI, not just as an occasional audit
- The most impactful improvement is usually email accuracy — fix that first for immediate outreach ROI
Related Cleanlist Features
Related Checklists
Frequently Asked Questions
How do I measure contact data accuracy?
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Create a weighted score across four dimensions: field completeness (what percentage of key fields are filled), email deliverability (what percentage of emails are verified valid), data freshness (how recently records were updated), and duplicate rate (what percentage of records are duplicates). Weight each based on your business priorities.
What is a good data quality score?
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A score of 80+ out of 100 indicates a well-maintained database. Scores of 60-79 suggest moderate quality issues that should be addressed. Below 60 indicates significant data quality problems that are likely impacting your sales and marketing performance.
How does poor data accuracy affect sales performance?
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Inaccurate contact data causes emails to bounce (damaging sender reputation), calls to reach wrong numbers (wasting rep time), personalization to be wrong (damaging credibility), and segmentation to be unreliable (sending wrong messages to wrong audiences). Studies estimate bad data costs companies 15-25% of revenue.
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