Quick Answer: The best method for detecting and merging duplicate companies in a CRM is a multi-signal deduplication workflow that combines deterministic matching, fuzzy entity resolution, and human-approved merge logic. In practice, the most reliable approach scores records across normalized company name, domain, phone, address, and tax/registration identifiers, then routes high-confidence matches to auto-merge and ambiguous cases to review.
Enterprise-grade CRM deduplication should not rely on a single field like company name, because subsidiaries, rebrands, spelling variants, and partial records routinely create false positives and false negatives. The strongest method is a layered matching system: first normalize and standardize company data, then compare exact identifiers such as website domain, phone number, and government or tax IDs, and finally apply fuzzy matching on names and addresses using a weighted confidence score. Once candidate duplicates are identified, the merge process should preserve canonical fields, maintain a full audit trail, map related activities and owners, and apply survivorship rules so the most complete and recent data becomes the source of truth. This approach reduces data decay, protects account histories, and improves pipeline attribution and routing accuracy.