Match new leads to your CRM

New leads arrive from a dozen sources — conference scans, form submissions, partner lists, web signups, purchased data. Some are genuinely new. Some are existing customers buying more. Some are coworkers of existing contacts. And some are your best account manager's own contacts, from an export they did six months ago.

You don't want any of them in your CRM twice. This guide walks you through classifying an incoming lead list against your CRM export and producing a clean import file with matches enriched and duplicates flagged.

When to use this guide

Use this workflow when:

• You have a list of new leads (the source).
• You have an export or copy of your CRM contacts (the master).
• You want the output to be: which leads already exist (and what's their CRM ID), which look like possible duplicates, and which are genuinely new.

This is the single most common use case on ListMatchGenie. If you're doing this more than once a month, consider saving the CRM export as a reusable master file.

Before you start

Prepare the lead list

Your source file should include whatever identity signal you have:

• Email is the strongest. If every lead has an email, matching is very reliable.
• First and last name is next-best.
• Phone helps. Company name helps less for lead-to-contact matching (many contacts per company).
• Source metadata (lead source, date acquired, campaign ID) should be preserved — they'll flow through to the output.

Prepare the CRM export

Your master file should be a narrow, focused slice of your CRM:

• CRM record ID (critical — this is how you'll write matches back)
• Email
• First and last name
• Phone (optional but helpful)
• Any secondary identifier you want to compare on

Skip all the fields you don't need. Narrower masters match faster and the output is easier to read.

Dedupe the CRM first

If you suspect your CRM has duplicates, run a dedupe pass on the export before using it as a master. Duplicates in the master cause review-queue entries for every source row that might match either duplicate.

The workflow

1. Upload both files

   From the dashboard, click New Match. Drop the lead list on the Source tile and the CRM export on the Master tile.

   Both files profile automatically. Verify the Genie detected your columns correctly — especially email, name, and any ID columns.

2. Map columns across files

   On the Configure step, confirm how source columns map to master columns. The Genie auto-maps columns with matching names (e.g. email in source → Email in master), but if your column names differ, map them explicitly:

   • Lead Email → crm_email
   • First Name → FirstName
   • Phone Number → Phone

   Anything that doesn't need to compare (e.g. Lead Source, Acquired Date) doesn't need mapping — those columns flow through to the export as-is.

3. Pick the Person profile

   Select Person as the match profile. Leave the confidence threshold at the default (70) for your first run.

   If you have strong email coverage, consider weighting email higher in a custom profile so the composite leans on email matches — in practice those are rarely wrong.

4. Run the match

   Click Run match. Matching small-to-medium lead lists against a tens-of-thousands-of-rows CRM typically takes under a minute.

5. Interpret the results

   On the Review step, your leads are classified:

   • match — the lead is already in your CRM. The master (CRM) columns are appended, including the CRM record ID.
   • review — the lead might be in your CRM. The top candidate is shown in the review queue.
   • unmatched — the lead is genuinely new to your CRM.

   A healthy lead list against a well-deduped CRM typically shows 20–40% matched, 5–15% review, and the rest unmatched. If you see >50% matched, either your "new" leads aren't that new or your data is very clean. If you see <10% matched, your CRM may be missing coverage or your match profile isn't picking up enough signal.

6. Work the review queue

   For each review case, you'll see the lead and the top CRM candidate side-by-side. Common decisions:

   • Obvious same person, different email — approve. The lead has a personal email, CRM has a work email.
   • Same name, same company, different first initial — usually spouse or coworker. Reject unless you can confirm.
   • Same name, totally different company/state — different person. Reject.

   Use notes to record why you accepted borderline cases. They export in _lmg_notes.

7. Export the results

   On the Export step, pick your output format:

   • CSV for re-import — one row per source lead, with match status and CRM ID attached. Drop into your lead-import tool and configure it to update existing records (for match/review approved) or create new (for unmatched).
   • XLSX for review — the multi-sheet workbook splits results by classification. Easier to look at for manual workflows.

   Both include the full _lmg_ metadata for audit.

Acting on the results in your CRM

Unmatched leads → create

These are genuinely new. Import as-is.

Matched leads → update

Update the matched CRM record with any new info from the lead (lead source, latest campaign, most recent contact date). Don't overwrite existing values blindly — prefer adding activity timeline entries over clobbering contact data.

Review-approved leads → merge

Treat these like matched leads, but flag for sales to confirm if the record is contacted.

Review-rejected leads → create

Same as unmatched — they're new.

Scaling this up

If you do this workflow regularly, consider:

• Saving the match profile with your email-weighted settings so you don't reconfigure each time.
• Saving the CRM master and updating it weekly with a fresh export.
• Using tags on match jobs to track which lead batches came from which source.

Related reading

• Match profiles — the Person profile in detail
• Setting the confidence threshold — tuning for your use case
• Field mapping — getting the source-to-master column alignment right