This walkthrough takes you from a blank account to a finished match export in about five minutes. You'll upload a sample list, let the Genie cleanse it, match it against a second file, review the results, and download a spreadsheet with matches attached.
You don't need to install anything. ListMatchGenie runs entirely in your browser — the only thing you'll need is a CSV or Excel file to work with.
Before you start
You need two files to run a match:
- A source file — the list you want to look things up against something. Often a new lead list, a supplier export, or a dump from a CRM.
- A master file — the "truth" list you're matching into. Often your existing customer database, a supplier registry, or any list you trust as canonical.
Both can be CSV, TSV, XLSX, or XLS. Each file needs a header row and at least one data row. See supported file formats for the complete list.
No data handy?
You can skip the master file and still run a dedupe on a single list — just upload the same file into both slots, or use the Deduplicate profile. See Deduplicate a customer list.
The five-minute path
Create an account
Go to app.listmatchgenie.com/sign-up and create a free account. You can use email + password or sign in with Google.
The free plan includes 1,000 source rows, 5,000 master rows, and 5 match jobs per month — plenty to run your first match end-to-end.
Start a new match
From the dashboard, click New Match. You'll land in the match wizard at step 1 of 6: Upload.
Upload your source and master files
Drag your source file onto the Source tile and your master file onto the Master tile. Each file is scanned and profiled in seconds — you'll see row count, column count, and a per-column profile (null rate, sample values, detected type).
If any column is detected wrong (for example, a ZIP code read as a number), you can override it inline before moving on.
Review the cleanse report
Click Next to advance to Cleanse. The Genie has already inspected both files and flagged data quality issues — inconsistent casing, stray whitespace, malformed emails, duplicate rows, and more.
You don't need to fix anything by hand. The cleansing runs automatically when you advance; this screen just shows you what's about to change so there are no surprises. Read the narrative summary at the top for a human-readable recap.
What the Genie fixes by default
Trimming whitespace, normalizing casing, standardizing phone/date/SSN formats, removing exact duplicates, escaping CSV-injection prefixes, padding short ZIP codes, and transliterating accented characters. The full list is in Cleansing report.
Configure the match
On the Configure screen, pick a match profile. The profile controls which fields are compared and how they're weighted:
- Person — name + address + email. Best for customer lists, lead matching, CRM dedup.
- Company — company name + address + domain. Best for supplier lists, firmographic matching.
- Identifier — match on an ID field (SSN, NPI, account number). Fastest and most exact.
- Contact dedupe — find near-duplicates inside a single list.
Leave the confidence threshold at the default (70%) for your first match. You can tune it later.
Run the match
Click Run match. The progress bar shows live status as the Genie scans both files, blocks candidates, compares fields, and scores every pair. On small files this takes a few seconds; larger files scale linearly.
When it finishes, the wizard auto-advances to Review.
Review your results
You'll see four stat cards: source rows processed, matched, needs review, and unmatched. Below that, a sample of actual matched pairs — click any row to see both records side-by-side with highlighted differences.
Anything classified Review means the Genie found a probable match but the score fell between your threshold and a higher certainty bar. Approve or reject each one, or export as-is and let a colleague handle the review queue.
Export the results
Click Next to advance to Export. You get four formats:
- CSV — your original source rows, plus columns for match status, match score, and every master column that matched. UTF-8 with BOM so Excel opens it cleanly.
- XLSX — multi-sheet workbook (matches, unmatched, review queue, summary).
- PDF (Pro+) — a branded analytical report you can send to stakeholders.
- PPTX (Business) — an executive summary slide deck.
Every export includes the full set of
_lmg_columns so you can trace which pass matched each row, what the score was, and which master row it matched to.
What just happened
In one pass you ran the full three-stage pipeline: Cleanse → Match → Insights. Behind the scenes:
- Cleanse — the Genie parsed, profiled, and standardized both files so matching had clean inputs. A dedup report was generated for each file.
- Match — a multi-pass engine compared every source row against every plausible master row, blocked by ZIP or phonetic code to keep the search space manageable, scored each candidate, and classified the best match per source row as
match,review, orunmatched. - Insights — the Genie generated a narrative summary of what matched, what didn't, and where the data-quality issues were concentrated. That summary is available on the job detail page and in any report you generate from this job.
Where to go next
Recommended reading order
New users benefit most from the three-stage pipeline overview before diving into specifics. After that, pick the guide closest to your actual use case.
- Understand the product model → The three-stage pipeline
- Pick the right match profile → Match profiles
- Tune accuracy → Setting the confidence threshold
- See all tier limits → Tier limits