If you’re trying to run a serious outreach campaign or just keep your sales pipeline from falling apart, you need your contact lists clean and accurate—especially if you’re dealing with thousands (or tens of thousands) of entries. Messy data means wasted time and money. This guide is for anyone using 11x who wants to actually reach real people and not just blast emails into the void.
Let’s cut the fluff. Here’s how to get your giant spreadsheet of contacts into 11x, clean it up, and avoid the usual headaches that come with “big list” imports.
Step 1: Prep Your Contact List Before Import
You can’t polish a turd. If your CSV or Excel file is a mess, 11x isn’t going to magically know what you meant. Garbage in, garbage out.
What to check before you even open 11x:
- Column names: Use clear headers like
First Name,Last Name,Email,Phone,Company, etc. Don’t get cute or creative. - Consistent formatting: Make sure emails look like emails, phone numbers are all in the same format, etc.
- Duplicates: Get rid of obvious duplicate rows now. Excel’s “Remove Duplicates” is fine for this.
- Empty rows: Delete them. They just make things harder later.
Pro tip: If your list is over 10,000 rows, split it up into smaller files. Imports fail more often than anyone admits, and smaller batches are easier to troubleshoot.
Step 2: Importing Into 11x
Now you’re ready to bring your list into 11x. Here’s how to make it as painless as possible.
1. Log in and find the import screen
- Go to your 11x dashboard.
- Look for something like “Contacts,” “Audience,” or “Import.” (They like to change menu names, so poke around if needed.)
2. Upload your file
- 11x accepts CSVs. XLS/XLSX sometimes work, but CSV is more reliable.
- Drag and drop or use the upload button.
3. Map fields
- 11x will try to guess which columns in your file match its fields. Double-check every mapping.
- “First Name” → “First Name”
- “Email” → “Email”
- If you see “Unknown” or “Skip,” fix it, or you’ll lose data.
- Ignore “custom fields” unless you really need them.
4. Choose deduplication options
- Most CRMs and outreach tools (11x included) can skip or merge duplicates by email. Use this if your list isn’t perfect.
- If you’re not sure, pick “skip” — you can always clean up later.
5. Confirm and import
- Hit “Import” and wait.
- For big lists, imports can take a while. Walk away, get coffee, check back in 10-30 minutes.
What can go wrong?
- Weird characters: Sometimes non-English letters or emojis break things. Open your CSV in Notepad or Google Sheets to catch these before importing.
- File size limits: If you’re importing more than 50,000 contacts at once, expect problems. Break it up.
- Blank fields: 11x sometimes treats blank emails as errors. Best to delete those rows before importing.
Step 3: Cleansing Contacts After Import
Even with prep, junk sneaks through. Here’s how to clean up your list inside 11x.
1. Filter and find bad data
- Use the search/filter tools in 11x to spot:
- Blank or obviously fake emails (e.g. “test@test.com”, “a@b.com”)
- Weird phone numbers (all 0’s, missing area codes)
- Outdated info (old company names, bounced emails)
2. Bulk edit or delete
- 11x usually lets you select multiple contacts and delete or edit in bulk.
- Be ruthless. If a record looks wrong, it probably is.
3. Use built-in cleansing tools (if available)
- Some 11x plans include data enrichment or email verification.
- These tools can help, but don’t trust them blindly—automated “enrichment” gets stuff wrong, especially if you’re in a niche industry.
- Use these as a second pass, not your main line of defense.
4. Tag or segment as you go
- Tag contacts by source, status, or anything else useful (“2024 trade show,” “web signup,” etc.).
- Segmentation now saves you pain later when you want to target specific groups.
What NOT to bother with:
- Don’t spend hours merging every tiny duplicate by hand. It’s not worth it unless you’re dealing with VIPs.
- Don’t trust “auto-correct” features to fix names or companies. They mess up more than they help.
Step 4: Set Up for Accurate Targeting
A clean list is only useful if you can actually use it. Here’s how to set yourself up for better targeting:
- Segment by key fields: Use job title, company size, or industry to group contacts. Don’t segment by “favorite color” or anything irrelevant.
- Double-check the most important fields: If your campaign depends on job title, make sure that column is actually filled in (and not just “N/A” everywhere).
- Set up suppression lists: Don’t email people who have opted out or bounced. 11x can usually handle this with a suppression/“do not contact” list.
- Test with a small batch: Before you blast 10,000 contacts, send to a small, random group. Look for bounces, spam complaints, or weird personalization fails.
Step 5: Keep It Clean Over Time
Importing and cleaning is not a one-time thing. Lists go stale fast.
- Schedule regular cleanups: Once a quarter is enough for most teams. More often if you’re high-volume.
- Use bounce/engagement data: If someone’s email bounces, delete or suppress it. If nobody ever opens, maybe it’s time to let go.
- Track your sources: If a particular list or vendor keeps giving you junk data, stop using them.
Pro tip: Don’t get obsessed with “perfect” data. Aim for “good enough to not embarrass yourself.” Over-cleaning wastes time.
What Actually Matters (And What Doesn’t)
There’s a lot of noise out there about “AI-powered cleansing,” “360-degree views,” and all that jazz. Here’s what matters for most teams:
What works: - Clean, well-labeled CSVs. - Removing duplicates and obvious junk. - Segmenting by fields you actually use.
What doesn’t: - Overly complicated custom fields. - Trusting automation to fix everything. - Spending days manually correcting minor typos.
Wrap-Up: Keep It Simple, Iterate Often
Don’t wait for perfect data. Import, clean up the biggest messes, segment by what matters, and keep moving. You’ll get better at this with each round. The main thing is to start with a solid process, stay skeptical of magic fixes, and set aside a little time every so often to keep your lists from turning into a dumpster fire.
Good data isn’t glamorous, but it’s what makes your targeting work. Stick with the basics and you’ll avoid most of the pain.