How to import and clean large contact lists in Aisdr efficiently

Let’s face it: importing a massive contact list into any CRM can turn into a mess if you’re not careful. Duplicates, weird formatting, missing info—it’s easy to end up with garbage data that costs you time and hurts your results. If you’re using Aisdr and want a no-nonsense way to get your contacts in, cleaned up, and ready to use, this guide is for you. Whether you’ve got thousands of emails from a trade show or a spreadsheet of sales leads, here’s how to do it right.


Step 1: Clean Your Data Before You Touch Aisdr

You’ll save yourself a ton of pain if you prep your list before importing. Aisdr’s tools are solid, but no CRM can magically fix a garbage spreadsheet. Here’s where to start:

  • Open your contact file in Excel or Google Sheets.
  • Standardize columns. Make sure every column has a clear label—First Name, Last Name, Email, Phone, Company, etc. Skip weird abbreviations or “Column A.”
  • Ditch useless info. If you’re importing a list and half the fields are empty (old fax numbers, random notes), delete those columns.
  • Hunt down duplicates. Sort by email or phone and look for repeats. Most CRMs, including Aisdr, can de-dupe, but it’s faster and safer to catch obvious ones now.
  • Fix formatting. Emails in ALL CAPS, numbers with dashes or spaces, inconsistent country codes—clean these up now. Use “Find and Replace” and formulas; it’s worth it.

Pro tip: Save your file as CSV. Excel files (.xlsx) sometimes have hidden formatting that can trip up imports.


Step 2: Map Your Fields to Match Aisdr

Every CRM has its own idea of what “Name” or “Company” means. If your columns don’t match what Aisdr expects, you’ll get errors or—worse—messy data in the wrong place. Here’s what to do:

  • Check Aisdr’s import template.
    • Download a sample CSV from Aisdr if you can. This tells you exactly what header names it likes.
  • Rename your columns in your spreadsheet to match Aisdr’s preferred format.
    • “First Name” instead of “FName,” “Phone” instead of “Mobile Number,” etc.
  • Decide what to skip. If you’re not using a field (like “Twitter Handle”), leave it out. The less clutter, the better.

Don’t overthink custom fields. If your list has oddball data—like “Favorite Snack”—you can always add custom fields in Aisdr later. Get the basics right first.


Step 3: Import Your List into Aisdr

Now you’re ready to bring your list into Aisdr. Here’s how to do it without breaking anything:

  1. Log in and go to the Contacts section.
  2. Look for the “Import” button.
    • Usually, it’s up top or in a dropdown menu.
  3. Upload your CSV file.
  4. Aisdr will ask you to map columns.
    • Double-check each field. Is “Email” going into the right spot? Are you missing anything?
  5. Preview the import.
    • Most CRMs will show you a sample row. Scan for anything weird—names in the wrong column, blank fields, etc.
  6. Start the import.
    • For big lists, this might take a while. Don’t reload the page.

Heads up: If you get errors, read them. They’re usually pretty specific: “Email address is invalid,” “Required field missing,” etc. Fix your file, re-upload.


Step 4: Post-Import Cleanup (Don’t Skip This)

Importing isn’t the finish line. Even with careful prep, you’ll nearly always find something off. Here’s what to do:

  • Check for duplicates.
    • Use Aisdr’s “Find Duplicates” or similar tool. Merge or delete as needed.
  • Spot-check a few random records.
    • Click into 10-20 contacts at random. Are names, emails, and companies all where they should be?
  • Look for empty or weird fields.
    • Filter your contact list for blanks or gibberish. Clean these up now rather than months later.
  • Tag or segment new contacts.
    • If you imported a list from a specific event or campaign, tag them now. You’ll thank yourself later.

Pro tip: Run a quick export of your imported contacts and compare it to your original file. It’s a fast way to spot missing or mangled data.


Step 5: Set Up Ongoing Data Hygiene

Messy data creeps in over time, even if you start clean. Don’t wait until it’s a disaster. Here’s what actually works:

  • Schedule regular cleanups.
    • Once a quarter, block 30 minutes to run de-dupe, fix blanks, and check for junk fields.
  • Train your team.
    • Make sure everyone knows how to add contacts properly. One person pasting in 500 names with no formatting can undo all your work.
  • Automate where you can.
    • Use Aisdr’s built-in validation tools for things like email format and required fields.
  • Avoid over-customization.
    • The more custom fields you add, the more you’ll have to clean later. Stick to what you actually use.

What to Ignore (or Not Worry About)

There’s a lot of noise about “AI-powered cleaning” and “smart enrichment.” Honestly, most of these tools are just fancy ways to charge you more to fill in LinkedIn profiles or guess job titles. They’re fine if you’ve got a budget and a real use case, but don’t count on them to fix truly bad lists. Human review is still the gold standard.

Likewise, don’t get too hung up on perfecting every single record. If 95% of your contacts are clean, that’s a win. The last 5% will take as much time as the first 95%—and may not matter much anyway.


Common Pitfalls (and How to Dodge Them)

  • Importing without a backup. Always keep an untouched copy of your original file. Undo is great, but not perfect.
  • Ignoring field types. If you put phone numbers in a “notes” field, you’ll regret it when you try to call someone.
  • Not testing with a small batch. For really big lists, import 10-20 contacts first. See how it goes, then do the rest.
  • Assuming the CRM will fix everything. Automation helps, but it won’t catch every duplicate, typo, or missing piece.

Wrapping Up: Keep It Simple, Iterate Often

Getting a huge contact list into Aisdr doesn’t have to mean hours of frustration or a messy database. Most headaches come from rushing the prep or skipping post-import checks. Take a little extra time up front, stick to what Aisdr actually needs, and keep your team in the loop.

Don’t sweat perfection. Clean, usable data beats “perfect” spreadsheets every time. Import, spot-check, fix what’s broken, and move on. The next time will be even easier.