Data quality budgets get cut because nobody expresses them as numbers a CFO recognises. These five metrics do, and each maps to a revenue consequence.
Coverage and match rate
Coverage: what share of your ICP exists in your database at all. Match rate: what share of inbound leads and CRM accounts you can resolve to a registered entity. Low coverage caps pipeline; low match rate breaks routing and reporting.
Report both per market, because a global average hides the country where you have almost nothing.
Fill rate and bounce rate
Fill rate is the share of records with the fields a campaign actually requires — not every field, just the required ones. Bounce and wrong-contact rate measure whether those fields are true.
A dataset with 90% fill and 25% bounce is worse than one with 60% fill and 2% bounce, because bad data costs rep time and sender reputation.
Time-to-first-touch
The hours between a lead or signal appearing and a human contacting it. It is the metric where data quality, routing and process meet, and it correlates with conversion more strongly than almost anything else you can control.
Set a target, alert on breaches, and review the outliers weekly — they almost always trace back to a matching or ownership problem in the data.
Frequently asked questions
What is a good match rate?
Above 85% for inbound leads with a company domain, and above 95% for existing CRM accounts once registration numbers are stored.
How do I build a business case for data spend?
Convert bounce rate and unmatched leads into wasted rep hours, add lost pipeline from slow first touch, and compare against the annual data cost.
