If you’ve ever tried uploading a messy contact list and found yourself swearing at your screen, this guide is for you. Whether you’re new to Pandamatch or just tired of cleaning up after a bad import, let’s get you set up with contact lists that don’t cause headaches. No fluff, no hype—just what actually works (and what’s more trouble than it’s worth).
1. Start with the Right Source
Before you even think about importing, get a handle on where your contacts are coming from. Are you exporting from Gmail, Outlook, an old CRM, or a spreadsheet some intern made five years ago? Garbage in, garbage out.
Pro tips: - Always export to CSV if you can. Excel and Google Sheets cause fewer problems than weird custom formats. - Keep a backup of your original export. You’ll thank yourself if you need to start over.
2. Review the Data—Don’t Skip This
Open your CSV (or XLSX) in Excel or Google Sheets. Scan through the first 20-30 rows and the last few. Look for: - Empty columns or rows - Obvious duplicates - Weird characters (emoji, foreign symbols, odd punctuation) - Columns that don’t belong (notes, timestamps, irrelevant IDs)
If you find a mess, clean it up now. It’s way easier here than inside Pandamatch.
What to ignore:
Don’t waste time making it “perfect,” but do get rid of junk you know you won’t use.
3. Standardize Your Columns
Pandamatch expects certain fields for contacts—think: First Name, Last Name, Email, Phone, Company, etc. If your columns are called “E-mail Address,” “Surname,” or “Cell #,” you’re likely to have mapping issues.
How to fix: - Rename your columns to match what Pandamatch expects. Use simple, clear names. - Delete any columns you don’t actually need.
Common columns you’ll want: - First Name - Last Name - Email - Phone - Company - Tags or Lists (if you use them)
If you have custom fields, just make sure you know what they are—Pandamatch usually lets you map them, but stay organized.
4. Tidy Up the Data
The less junk you bring in, the less you’ll have to clean up later. Here’s what’s worth your time:
- Strip out obvious duplicates (sort by email and scan for copies).
- Make sure every row has at least one way to contact the person (email or phone).
- Watch for broken emails (missing “@” or spaces in the address).
- Remove obvious test data like “asdf@example.com” or “Jane Doe 1234.”
Pro tip:
Don’t obsess over typos in names. Fixing “Jhon” to “John” is rarely worth the effort, unless you’re doing a very small, high-touch list.
5. Save as UTF-8 CSV
Pandamatch is pretty forgiving, but character encoding can trip you up if you have international names. Always save your file as UTF-8 encoded CSV (not the default Windows CSV).
How to do it: - In Excel: Use “Save As” → “CSV UTF-8 (Comma delimited) (*.csv)” - In Google Sheets: File → Download → Comma-separated values (.csv, current sheet)
If you skip this, you might see “????” instead of accented characters.
6. Import the File into Pandamatch
Now, log in to Pandamatch and start the import process. Upload your file, and Pandamatch will usually try to match your columns automatically.
Pay close attention to field mapping: - Double-check every field. If “Phone” is matched to “Company,” fix it now. - If Pandamatch can’t match something, assign it manually or skip it.
Don’t sweat custom fields unless you actually need them. Extra fields just clutter things up for most users.
7. Deal with Import Errors
Even if you do everything right, you’ll probably hit a snag the first time. Here’s what usually goes wrong:
- Unrecognized columns: Pandamatch ignores columns it doesn’t know—no harm done, but check if anything important got dropped.
- Duplicate contacts: Depending on your settings, Pandamatch might merge or skip duplicates. It’s better to clean these before import.
- Formatting errors: Invalid emails or numbers can cause rows to be skipped.
How to fix: - Download the error report (if offered) and review what didn’t import. - Fix the issues in your spreadsheet, then re-upload—don’t try to patch things inside Pandamatch unless it’s a tiny list.
8. Clean Up After Import
Once your import is done, take five minutes to spot-check the results:
- Search for a handful of contacts you know should be there.
- Check for obvious weirdness (all-caps names, missing emails, etc.).
- If you imported tags or lists, make sure they stuck.
If something looks way off, don’t panic. It’s almost always easier to fix in your spreadsheet and re-import than to click through individual records.
9. Ongoing Maintenance: What’s Worth Doing?
If you’re managing a living contact list, you’ll want to keep things from getting messy again. Here’s what actually helps:
- Regular exports: Back up your current list every month or quarter.
- Spot-check new imports: Don’t trust outside sources—review files before importing.
- Set rules: Decide what counts as a duplicate (usually email or phone) and stick with it.
What’s not worth it: - Obsessively merging every duplicate by hand. Use Pandamatch’s tools or do bulk cleanups. - Perfecting every detail. Focus on what moves the needle—clean, usable contacts.
What to Avoid (Hard Lessons Learned)
A few mistakes most people make at least once: - Uploading a 10,000-row mess “just to see what happens.” You’ll spend more time cleaning than if you’d prepped it. - Ignoring encoding warnings. If you see “invalid character” errors, fix your file and re-upload. - Mapping every possible field. More fields = more clutter. Stick to what you actually use.
Keep It Simple (and Don’t Overthink It)
Importing contacts into Pandamatch doesn’t have to be a grind. Spend a few extra minutes cleaning your file, do a careful import, and fix issues in batches—not one by one. The goal isn’t perfection; it’s a usable list that won’t haunt you later. Stick to the basics, iterate when you need to, and you’ll avoid 90% of the headaches most people run into.