Salesforce dedup
Find Salesforce duplicates that Duplicate Rules can't catch.
Salesforce's built-in Duplicate Management catches exact matches. The other 60% of your duplicates — typos, formatting variants, alias emails, nickname pairs, Person Account quirks — survive every dedup job. Export Leads, Contacts, or Accounts to CSV, run them through ListMatchGenie, push the cleaned file back via Data Loader. No AppExchange package, no $2,500/year commitment, no admin approval.
The problem
Salesforce's built-in dedupe is exact-match only. The duplicates that ate your data quality scores are still there.
Standard Duplicate Rules and Matching Rules fire on exact field equality by default. 'John Smith / john@acme.com' and 'J. Smith / john.smith@acme.com' both pass — both stay as separate Contacts.
Phone formatting kills the dedupe. '(617) 555-1234' vs '617-555-1234' vs '+16175551234' creates three Contacts on one person, and your validation rules don't normalize on insert.
Nickname pairs (Bob/Robert, Kate/Catherine, Mike/Michael, Liz/Elizabeth) are invisible — Salesforce has no nickname database, and Matching Rules' fuzzy options only handle one-character typos.
Person Accounts add a third entity type. Dedup rules don't transfer cleanly between Lead → Contact → Person Account, and the merge UX differs in subtle ways that catch admins out.
Custom objects and custom fields are second-class citizens for Duplicate Management. If your dedupe logic needs to consider a custom 'Internal_Customer_ID__c' field, you're building Apex.
AppExchange tools (Cloudingo, RingLead/ZoomInfo Operations, DemandTools, DupeBlocker) start at $2,500–$25,000+/year, require admin training, and assume you want continuous in-org automation. Many teams just need a quarterly cleanup or a one-time post-acquisition merge.
How the Genie solves it
Probabilistic matching that handles real Salesforce data — without an AppExchange install
Multi-field weighted matching
Name + email + phone + AccountName scored together with calibrated weights, not one column at a time. So 'J. Smith / john.smith@acme.com / 617-555-1234' matches 'John Smith / john@acme.com / (617) 555-1234' even though no individual field is identical.
Phone normalization built in
Automatic during cleanse: '(617) 555-1234', '617-555-1234', '+16175551234', '6175551234' all resolve to the same match key before scoring. No pre-work in Excel, no Apex helper.
Nickname database
Robert↔Bob, Catherine↔Cathy↔Kate, Michael↔Mike, Elizabeth↔Liz↔Beth, Jennifer↔Jen are interchangeable in scoring — and the engine knows when they aren't (Kate isn't Katherine automatically; common spellings stay distinct).
Email alias awareness
'john.smith@acme.com', 'jsmith@acme.com', 'john+work@acme.com' — the engine recognizes common alias patterns and shared domains, weighted alongside name and phone evidence.
Custom-field preservation
Export your Salesforce object with all custom fields (Internal_Customer_ID__c, Industry__c, anything). The cleaned export comes back with every original column intact — Data Loader upserts cleanly without rebuilding your field map.
No AppExchange install, no admin approval
CSV in, CSV out. No managed package, no OAuth dance, no Salesforce admin approval workflow, no security review. If you can use Data Loader you can use this.
Real example
A Salesforce Contacts export deduped
Same workflow for Leads, Contacts, Accounts, and Person Accounts. Below: four representative pairs from a real-world style export.
Source file
salesforce_contacts.csv · FirstName, LastName, Email, Phone, AccountName
Master file
(self-dedup — same file matched against itself)
Robert Tan · rtan@globex.com · 617-555-1234 · Globex
Bob Tan · robert.tan@globex.com · (617) 555-1234 · Globex Inc
matchedNickname pair (Robert↔Bob), email alias on shared domain, phone format variation, AccountName variation (Globex / Globex Inc) — composite confidence 0.94, merge
Jennifer Lee · jen.lee@initech.co · — · Initech
Jen Lee · jen.lee@initech.co · 415-555-9912 · Initech
matchedSame email exact, nickname (Jennifer↔Jen), AccountName exact — confidence 0.99 even though one row is missing phone
Liz Marquez · lmarquez@wayne.com · 555-0188 · Wayne Enterprises
Elizabeth Marquez · elizabeth.marquez@wayne.com · — · Wayne Enterprises
reviewNickname (Liz↔Elizabeth), shared surname + AccountName, but emails are different aliases and phones don't agree — confidence 0.71, send to review queue for human eye
Catherine Ngozi · cngozi@acme.org · 312-555-7700 · ACME Foundation
(no match)
unmatchedSingle occurrence in the file — no duplicate found, kept as-is
Before and after
What changes when you use ListMatchGenie
Without ListMatchGenie
- Run a standard Salesforce Duplicate Job → finds maybe 1,200 exact-match dupes out of ~8,000 actual duplicates in a 50K Contact org.
- Spend two weeks tuning Matching Rules with the limited fuzzy options → still misses nicknames, aliases, and phone-format variants.
- Stand up an AppExchange dedup tool → $2,500–$25,000/year, 2+ weeks of configuration, admin training, security review.
- Manually merge in the Duplicate Manager UI → 5 seconds per pair × 8,000 pairs = a quarter of a quarter spent merging Contacts.
With ListMatchGenie
- Export your Contacts (Data Loader, Workbench, or a saved report) and upload to ListMatchGenie. The Genie auto-detects schema and profiles every column before matching starts.
- Get a review queue with confidence scores per cluster — typos, aliases, nicknames, format variants all surfaced with explanations of why each cluster matched.
- Bulk-accept high-confidence patterns (e.g., 'all phone-format dupes' or 'all Robert↔Bob pairs') in a single click; review low-confidence pairs one at a time.
- Export the cleaned CSV with merge mappings preserved alongside your original columns. Data Loader upserts cleanly. Save the match profile and re-run quarterly.
Let the Genie handle the grunt work.
Free tier is real. No card. No forms. Just upload your first list and see the Genie clean and match it in under a minute.
