If you’re running outbound campaigns, you know junk leads will tank your results and waste your time. Importing messy lists just makes things worse—bad data in, bad results out. This guide is for anyone who wants to get better results with Luna by starting with a clean, accurate lead list, even if you’re not a data nerd. I’ll walk you through the real steps (and pitfalls) to import and clean your leads in Luna, so you can actually reach the people you want.
Step 1: Get Your Lead List Ready (Don’t Skip This)
Before you even think about importing anything into Luna, take a hard look at your CSV or spreadsheet. Most issues with targeting start here. If your list is a mess, Luna can’t magically fix it.
What to check before importing: - File format: CSV is safest. Excel files usually work, but CSV avoids weird formatting problems. - Column headers: Make sure you have clear headers (like First Name, Last Name, Email, Company, etc.). Don’t use cryptic or duplicate names. - Consistent data: Quick scan for obvious weirdness. Are there rows with missing emails? Are company names sometimes all-caps or spelled differently?
Pro tip: Open the file in Google Sheets or Excel. Sort by each column to spot blanks or strange values. Delete any obvious junk (test records, fake names, “asdf@asdf.com” emails, etc.).
What’s worth cleaning now—and what isn’t
Don’t get obsessed with perfection. Just fix glaring problems: - Remove rows with missing or fake emails. - Standardize company names if you see obvious duplicates (“Acme Inc.” vs. “ACME INC”). - Ditch any columns you know you’ll never use.
Ignore: Typos in first names, job titles that aren’t perfect, or minor formatting stuff. You can live with those for now.
Step 2: Import Your List into Luna
Now you’re ready to get your leads into Luna. The import process is designed to be straightforward, but there are a few places you can trip up if you’re not paying attention.
How to import:
- Log in to Luna.
- Find the import tool. Usually under “Leads” or “Contacts”—look for an “Import” or “Upload” button.
- Upload your CSV. Drag and drop, or select your file.
- Map your columns. Luna will try to match your columns to its fields. Double-check these—sometimes it guesses wrong, especially with weird or custom column names.
- Make sure “Email” goes to “Email,” “First Name” to “First Name,” etc.
- If you have columns Luna doesn’t recognize (like “Favorite Snack”), decide if you want to create a custom field or just skip it.
- Preview the import. Most CRMs (including Luna) will show a preview of how your data will look. Check for misaligned fields or weird formatting.
- Start the import. Hit confirm or import, then let it run.
Heads up: Large lists (10,000+ rows) can take a while. Don’t panic if it’s not instant.
Common issues (and how to dodge them):
- Encoding errors: If you see strange characters (like �), your CSV might be in the wrong encoding. Save as UTF-8.
- Duplicate detection: Luna will try to spot duplicates, but if your emails or company names aren’t consistent, you’ll end up with messy records. More on this below.
- Column mismatch: If your columns aren’t mapped right, you’ll get fields in the wrong place. Always check the preview.
Step 3: Clean Up Your Data Inside Luna
Even if your import goes smoothly, you’re not done. Most lists have hidden problems—duplicates, weird formatting, or missing info—that mess up targeting. Luna has some built-in cleanup tools, but you’ll need to use your own judgment too.
Check for duplicates
Luna usually de-duplicates based on email address, but this isn’t foolproof: - If someone used two different emails at the same company, both might get in. - If your list is companies, not people, company name variations (“Acme Inc” vs “Acme Incorporated”) may slip through.
What to do: - Use Luna’s “Find Duplicates” or “Merge Records” tool. - Sort by email or company name, and manually spot-check for obvious repeats. - For big lists, export and run a quick duplicate check in Excel or Google Sheets, then re-import if needed.
Fill in missing info
You don’t need every field filled, but missing emails or company names make leads useless. Missing job titles or phone numbers are less critical (unless you plan to call).
- Filter for blanks: In Luna, filter for leads with missing critical info. Delete or try to fill in the gaps.
- Don’t stress over minor gaps: A missing job title isn’t the end of the world. Focus on what you actually need for your campaign.
Standardize key fields
Consistent data is key for accurate targeting and filtering. Quick fixes: - Company names: Standardize as much as you can. Pick one style (“Inc.” or no “Inc.”) and stick with it. - Job titles: Group similar titles if you plan to segment by role (e.g., treat “Sales Manager” and “Sales Lead” as the same).
Pro tip: If you’re using tags or custom fields for segmentation, keep the naming simple and consistent. The more complicated your system, the more likely you’ll mess it up later.
Step 4: Segment and Tag for Accurate Targeting
Now that you’ve got clean data, set yourself up for painless targeting. This is where most people either overthink things or skip tagging entirely—both cause headaches down the line.
Set up tags or lists
- Basic tags: Industry, company size, job function—whatever matters for your outreach.
- Campaign tags: If you’re running multiple campaigns, tag leads by source or intent (“Webinar Q2,” “Inbound Demo Request”).
- Don’t go tag-crazy: Too many tags create clutter. Stick to what you’ll actually filter by.
Build simple, useful segments
- Use Luna’s filtering to create dynamic lists (e.g., “VP-level in SaaS companies,” “CTOs in California”).
- Save these filters for quick access.
What doesn’t work: Complicated, multi-step tagging systems. You’ll forget what half the tags mean in a month. Keep it simple.
Step 5: Test Your Data Before Launching Campaigns
You wouldn’t send a batch of mailers without double-checking the addresses, right? Same logic here. Even after cleaning, do a quick reality check.
How to spot-check:
- Randomly pick 10–20 leads and check: Are the emails real? Do job titles and company names make sense? Any obvious mistakes?
- Send a test campaign to a small segment. Watch for bounces or weird replies.
- Check reporting: Luna will usually flag high bounce rates or deliverability issues. If you see a lot, go back and check your data.
Pro tip: If you’re getting lots of bounces, your list still has problems. Don’t just keep blasting—pause and fix.
What to Ignore (and What to Obsess Over)
- Ignore: Fancy enrichment tools that promise perfect data. They can help, but they’re expensive and rarely perfect.
- Don’t obsess over: Minor typos, slightly outdated job titles, or missing LinkedIn URLs.
- Do obsess over: Email accuracy, duplicate removal, and tagging by major segments.
Keep It Simple and Iterate
Importing and cleaning lead lists in Luna isn’t rocket science—but it’s easy to overcomplicate. Most of the value comes from doing the basics well: start with a good list, fix the big issues, keep your tags simple, and sanity-check before hitting send. Don’t let "perfect" be the enemy of "good enough." Clean up what matters, get your campaigns running, and tweak as you go. You’ll save time, avoid headaches, and actually reach the people you want.