How do I measure contact data accuracy?

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?

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?

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.

Contact Data Accuracy Audit Checklist

Audit the accuracy of your contact database with this checklist covering field completeness, freshness, deliverability, and enrichment gaps.

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Field Completeness Audit

Calculate fill rates for all key fieldsCheck percentage completion for: email, phone, job title, company, industry, company size, LinkedIn URL, and location. Export and analyze in a spreadsheet.easy
Identify contacts with less than 50% field completionCreate a segment of contacts with major data gaps. These records are likely too incomplete for effective outreach or segmentation.easy
Check for dummy or placeholder dataSearch for obviously fake data: test@test.com, 555-555-5555, 'N/A', 'Unknown', or blank fields disguised as populated ones.easy

Data Freshness Check

Identify contacts not updated in 12+ monthsFind records where no field has been updated in over a year. These contacts likely have outdated information that needs re-enrichment.easy
Cross-check job titles against LinkedInSpot-check a sample of contacts against their LinkedIn profiles to estimate what percentage have changed roles since your last update.medium
Re-verify email addresses on aging recordsRun email verification on contacts that haven't been verified in 6+ months. Flag newly invalid addresses for removal or re-enrichment.easy

Accuracy Scoring

Calculate your overall data quality scoreCreate a weighted score based on: field completeness (30%), email deliverability (30%), data freshness (20%), and duplicate rate (20%).medium
Compare accuracy across data sourcesIf your contacts come from multiple sources (web forms, imports, enrichment, manual entry), compare data quality by source to identify the weakest inputs.medium
Set data quality benchmarks and targetsBased on your audit, set specific improvement targets: 'Increase email fill rate to 90%', 'Reduce bounce rate to under 2%', 'Eliminate 100% of dummy data.'easy

Pro Tips

01A contact with a valid email but wrong job title is worse than one with no title — bad data leads to wrong messaging
02Use Cleanlist's Data Quality Calculator (free tool) to quickly score your database health
03Track data quality metrics monthly as a dashboard KPI, not just as an occasional audit
04The most impactful improvement is usually email accuracy — fix that first for immediate outreach ROI

Frequently Asked Questions

How do I measure contact data accuracy?
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?
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?
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.

Related Cleanlist Features

Data Enrichment Email Verification ICP Scoring

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