Email Data Quality Score

Definition

An email data quality score evaluates how usable your subscriber data is for marketing purposes. It aggregates measurements across four dimensions: completeness (what percentage of records have values for key fields), accuracy (how many records contain verified or correct information), freshness (how recently data was collected, confirmed, or updated), and consistency (whether data follows standardised formats across all records).

Why It Matters

Poor data quality directly reduces email performance. Missing personalisation fields produce generic content that engages less. Incorrect demographic data leads to irrelevant targeting. Stale data causes bounces and spam complaints. Inconsistent formatting breaks segmentation logic. Without a data quality score, these problems accumulate invisibly until campaign performance degrades noticeably.

Best Practices

• Calculate the data quality score monthly using a weighted formula that prioritises fields critical for current segmentation
• Set minimum quality thresholds (e.g. 90% completeness on email address and name, 80% on preference fields)
• Build data quality checks into every capture point to prevent low-quality records from entering the system
• Create a remediation workflow for records that fall below threshold rather than attempting full-list clean-up at once