Data Quality Automation

Visitor intelligence data is only as valuable as the CRM data it enriches. If your CRM has stale domains, wrong company names, or missing fields, Kopimore can't match correctly and the automation falls apart. This lesson covers using VI data to actively improve your CRM data quality.

Domain Validation and Cleanup

The most common CRM data problem affecting visitor intelligence is incorrect or missing website domains on company records. Run a monthly audit: export all company records, filter for those missing the Website field, and use enrichment APIs to fill them. Apollo and Clearbit both offer bulk enrichment via CSV upload.

Using Kopimore to Fill Data Gaps

When Kopimore creates or updates a company record, it appends firmographic data: industry, employee count, revenue range, location, and technology stack. For existing CRM companies with stale data, configure Kopimore to update these fields when it identifies a match — effectively using live visit events as a trigger for data freshness updates.

Duplicate Detection and Merging

Kopimore creates duplicates when: (1) an existing company record has an incorrect domain, (2) the same company has multiple subsidiaries with different domains, or (3) a company rebrands and changes domains. Set up a monthly duplicate review in your CRM, searching for companies where Kopimore_Identified = true that share similar names but different domains.

Data Governance Rules

Document explicit rules for your team: which Kopimore fields override existing CRM fields vs which supplement them. Generally: Kopimore should update "Last Visit" fields (always fresher than manual data) but should not override manually entered fields like "Account Type" or "Contract Value" that a rep has intentionally set.