The Email Regex Validation analysis runs a built-in regex check against values in selected email fields, testing whether the value conforms to a standard [email protected] format. Each value is categorized as Valid, Invalid, or Empty. Because Salesforce only performs minimal format checking natively (e.g. confirming an @ symbol is present), Email Regex Validation gives you a reliable picture of actual email address quality across your data.

Use to measure message deliverability risk, identify bad data sources, and prioritize cleanup efforts.

Configuration

Because any text field can be used to store email addresses in, all available text fields are presented when creating a Plauti Context job with the Email Regex Validation analysis. It is recommended to disable the Email Regex Validation analysis type in Object Configuration for those fields that do not contain email addresses in your Org.

Set a threshold for what constitutes a good, warning level, or critical email validation rate. For most fields that you apply an Email Regex Validation analysis to you'll want to have as much valid email addresses as possible. This would mean for example a good email validation rate is 90% or more, warning level would be between 80-90%, and anything less than 80% would be critical. 

Detailed Job Results

Email Validation checks each email field value against a standard email format pattern ([email protected]). Each record is categorized as ‘Valid’ (matches the email format), ‘Invalid’ (has a value but does not match the email format), or ‘Empty’ (no value at all).

The doughnut charts show the distribution of Valid, Invalid and Empty email values for each field. Hover over a section to see the percentage and number of records.

The results table below again shows the percentages of Valid, Invalid and Empty email values for each field. The percentage of valid email addresses is color-coded by the threshold settings (green for Good, amber for Warning, red for Critical).

Key Insights

• Email quality per field: Show the percentage of syntactically valid email addresses stored in each field, giving a direct measure of field-level data quality.
• Message deliverability risk: Fields with a high ‘Invalid’ percentage are likely sources of bounce rates and send failures in email campaigns.
• Source quality: A high ‘Invalid’ rate on fields fed by web forms or imports points to missing or weak input validation at the source; a low ‘Invalid’ rate on manually edited fields indicates good user behavior.
• Empty values: Empty fields are categorized separately, to easily distinguish between missing emails and malformed ones.
• Verification readiness: Only values that pass format validation are meaningful candidates for deeper mailbox-level verification, such as with Plauti Verify.

Recommended Actions