Automate Duplicate Contact and Lead Merging
Reps create a new lead every time a prospect fills out a form under a slightly different email, or an SDR imports a list that already partly exists in the CRM. Within a year, a mid-sized pipeline can accumulate thousands of duplicate contact and lead records, each with fragments of the real history: one has the phone call notes, another has the email thread, a third has the most recent title change. Admins run manual dedup exports quarterly, but by the time the spreadsheet is reconciled the data has already drifted again, and reps waste time working a lead that's secretly already a customer under a different record.
STARTING PRICE
From €299
Standard tier · Multi-step workflow with AI extraction/decisioning and 2-3 integrations.
Get a quote →Saves roughly 4-6 hrs/week for a sales ops admin.
How the automation works
We build a matching and merge pipeline that runs continuously rather than as a quarterly clean-up project. New and existing records are scored on a blend of exact and fuzzy signals — email domain, normalized phone, name plus company — and anything above a high-confidence threshold merges automatically, with all activity, notes and email history consolidated onto the surviving record. Medium-confidence matches route to a review queue with the conflicting fields shown side by side, so a human makes the final call in seconds instead of researching from scratch. Every merge is logged and reversible for 30 days in case a false match slips through.
Process flow
- 01
New or updated record trigger
A new lead, contact or bulk import triggers a scan against the existing database rather than waiting for a scheduled batch job.
- 02
Fuzzy match candidates ai
The record is compared against existing contacts using normalized email, phone and name-plus-company similarity, tolerant of typos, nicknames and formatting differences.
- 03
Score merge confidence ai
Each candidate pair gets a confidence score; high-confidence pairs are queued for auto-merge, ambiguous pairs are queued for human review with the differing fields highlighted.
- 04
Merge and consolidate integration
Field values, activity timelines, email threads and notes are consolidated onto the surviving record using configurable field-precedence rules (most recent wins, or a designated source-of-truth system wins).
- 05
Log and notify output
Every merge is written to an audit log with a 30-day undo window, and the record owner is notified so they know which record is now authoritative.
Inputs
- New lead/contact records
- Existing CRM contact database
- Bulk import files
- Field-precedence rules
Outputs
- Merged contact records with consolidated history
- Review queue for ambiguous matches
- Merge audit log
- Duplicate rate dashboard
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Franchise locations, regional offices and holding-company subsidiaries can share a domain and even a switchboard number, so matching on email domain or phone alone will wrongly merge legitimately separate accounts — company-level matching needs a human-confirmed hierarchy, not just string similarity.
- Common names at large companies (multiple 'John Smith' contacts at a 5,000-person enterprise account) produce false-positive fuzzy matches that merge two different people into one record, silently deleting one person's history.
- Auto-merging without a reversible audit trail is dangerous — a bad merge that collapses a live deal contact into a closed-lost one can misroute an active opportunity, so every automatic merge needs a fast undo path, not just a confirmation dialog on the way in.
- Field-precedence rules matter more than the matching logic itself: silently overwriting a manually-verified phone number with a stale one from a bulk import because it merged 'more recently' erodes trust in the CRM faster than duplicates did.
Frequently asked questions
Will this merge two different people who happen to share a name?
Only exact and near-exact matches on email or phone trigger an automatic merge; same-name-different-company matches route to manual review instead of merging automatically.
Can a bad merge be undone?
Yes — every merge is logged with a 30-day undo window that restores both original records and their full activity history.
Does this work with our custom Salesforce fields?
Yes, the matching and field-precedence rules are configured against your actual field schema, including custom objects, not just the standard Lead and Contact fields.
How is this different from Salesforce's built-in duplicate rules?
Native duplicate rules mostly block creation of new duplicates going forward; this also cleans up your existing backlog and handles the ambiguous cases your out-of-the-box rules skip.