Duplicate Contact Management
Duplicates corrupt reporting and break automation. This workflow prevents, detects, and resolves duplicate contacts systematically.
Duplicate Contact Management
Duplicate contact management combines prevention at entry, automated detection, and human-reviewed resolution so each person exists once in the CRM with a complete history.
Duplicates corrupt reporting and break automation. This workflow prevents, detects, and resolves duplicate contacts systematically.
Problem definition
Duplicate contacts occur when the same person exists in the CRM multiple times, splitting their history, inflating counts, and causing automation to send redundant or conflicting messages.
Observable symptoms
- ✓A contact receives the same email twice
- ✓Pipeline counts are inflated
- ✓A contact's history is split across records
- ✓Sales reps contact the same person unknowingly
Likely causes
- ✓No deduplication check at form submission
- ✓Manual entry without lookup
- ✓Imports from multiple sources without matching
- ✓Name or email variations creating separate records
Required inputs
- ✓A deduplication matching strategy
- ✓Entry-time validation rules
- ✓A merge process that preserves history
- ✓A review queue for ambiguous matches
Recommended workflow
Follow this sequence to address the problem systematically.
- ✓Define match keys (email, phone, normalized name)
- ✓Add real-time duplicate checks at every entry point
- ✓Run periodic scans for existing duplicates
- ✓Queue ambiguous matches for human review
- ✓Merge confirmed duplicates preserving all history
- ✓Log merges so reporting can be reconciled
Automation boundaries
Automation detects and flags duplicates and enforces entry checks. The merge decision for ambiguous matches, where it is unclear whether two records are the same person, requires human judgment.
Human review requirements
A person reviews matches below a confidence threshold, confirms merges, and handles edge cases like shared family emails or business name variations.
Risks
Auto-merging low-confidence matches combines distinct people, losing data and creating confusion. Set the confidence threshold conservatively and review the rest.
Limitations
No matching strategy is perfect. Some duplicates will always escape detection, so periodic manual review remains necessary.
Measurement criteria
Track duplicate detection rate, merge accuracy, and the count of unresolved ambiguous matches over time. A declining unresolved queue indicates progress.
Frequently Asked Questions
What is the best match key for deduplication?
Email is the strongest single match key, but a combination of email, phone, and normalized name catches more duplicates while reducing false matches.
Should duplicates be auto-merged?
Only high-confidence matches. Ambiguous matches should be queued for human review to avoid combining distinct people.
How do we prevent future duplicates?
Add real-time duplicate checks at every entry point, including forms, imports, and manual entry, so a duplicate is caught before it is created.
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