Merge two spreadsheets
Merge two spreadsheets where the join key isn't perfectly clean.
Combining two contact lists, two product catalogs, two member rosters? VLOOKUP works when the join key is identical on both sides. Real spreadsheets are never that clean — names spelled differently, companies with and without 'Inc', phones formatted six ways. Drag both files into ListMatchGenie and the engine handles the fuzzy join, then exports a single combined file with both sources' columns side by side.
The problem
VLOOKUP is fine when the join key is identical. It almost never is.
Two team rosters from different departments — 'Sarah Patel' on one, 'S. Patel' on the other. VLOOKUP returns #N/A.
Two product catalogs from the manufacturer and the distributor — same products, different SKU formatting. Exact-match join: 30% miss rate.
Two member lists pre/post a system migration — surnames sometimes have apostrophes, sometimes don't. VLOOKUP doesn't notice.
Building helper columns with TRIM/LOWER/REGEXREPLACE works for casing and whitespace but fails on nickname pairs, alias emails, and company-name suffixes.
Power Query merge has a fuzzy option but it's character-distance only — won't catch Bob↔Robert or 'Acme Inc'↔'Acme Corporation'.
Manually reconciling the misses takes hours and you have no record of which rows were treated as matches and why.
How the Genie solves it
Drop both files in. The Genie does the fuzzy join. Out comes a combined file.
Source + master upload model
Drag File A as your source and File B as your master. The Genie matches every row in A against B, returns a clustered review queue with confidence scores, and lets you accept the join in bulk by pattern.
Multi-field fuzzy matching
Match on name + company together, or email + phone, or whatever combination of columns makes sense. The engine weighs the evidence — agreement on three fields beats disagreement on one.
Nickname, alias, format variants handled
Bob↔Robert, sarah.patel@↔spatel@ on shared domain, '(617) 555-1234'↔'617-555-1234', 'Acme Inc'↔'Acme Corporation' — all weighted into the score, not blindly rejected.
Combined output preserves both sides' columns
Export the merged file with all of File A's columns + all of File B's columns + match status + confidence score per row. No VLOOKUP-and-pray; every column you care about comes back.
Review the borderline cases, accept the rest
Cluster matches by reason. Bulk-accept high-confidence groups, review low-confidence ones with side-by-side row comparison.
Re-run on next month's data
Save the match profile. When the next pair of files arrives, it's drag, click, export — under 5 minutes.
Real example
Merging an event registration list with the CRM master
Same workflow whether your two files are CRM exports, ERP data, member rosters, or anything else.
Source file
event_registrations.csv · first_name, last_name, email, company
Master file
crm_contacts.csv · firstname, lastname, email, company_name
Sarah Patel · sarah.patel@globex.com · Globex Inc
S. Patel · spatel@globex.com · Globex
matchedSurname exact, name initial matches, email alias on shared domain, company variant — composite 0.92, append CRM data to event row
Bob Tan · bob@initech.co · Initech
Robert Tan · robert.tan@initech.co · Initech
matchedNickname pair (Bob↔Robert), email alias on shared domain, company exact — confidence 0.95
M. Johnson · — · Wayne Enterprises
Michael Johnson · m.johnson@wayne.com · Wayne
reviewSurname exact, name initial matches, but no source email and only company surname-suffix variant. Could be a different M. Johnson at the same company — review
New Person · np@newco.com · NewCo
(no match)
unmatchedGenuinely new — no overlap with CRM, treat as net-new attendee
Before and after
What changes when you use ListMatchGenie
Without ListMatchGenie
- Build VLOOKUP across two columns — get 30%+ #N/A results because of name and company variations.
- Add 5 helper columns to normalize each side; run another VLOOKUP; still miss nicknames and alias emails.
- Try Power Query's fuzzy merge — get character-distance matches that flag random near-misses and miss the real ones.
- Manually reconcile the 600 #N/A rows over 4 hours — and have no record of which decisions you made.
With ListMatchGenie
- Drag both files in. Schema auto-detection on each.
- Pick the columns to match on (auto-suggested by name) and the match profile.
- Get a clustered review queue with confidence scores and 'why this matched' explanations.
- Export the merged file with both sides' columns + match metadata. Done in 5–15 minutes.
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.
