If you’re wrangling lead lists and want to make ScrapeLi actually save you time (instead of creating a new mess), this guide’s for you. We’ll walk through importing your data, cleaning it up, and getting your workflow humming—no hand waving, no hype. I’ll flag what matters, what you can skip, and how to avoid common rookie mistakes.
Step 1: Understand What ScrapeLi Wants
Before you even open ScrapeLi, know this: garbage in, garbage out. ScrapeLi’s workflow is built around structured data—mainly CSVs or Excel files with columns like “First Name,” “Last Name,” “Email,” “Company,” and “LinkedIn URL.” If your file’s a mess, expect headaches.
What works: - CSV files with clear, consistent columns - No weird merged cells or extra header rows - UTF-8 encoding (get this wrong and you’ll see gibberish characters)
What doesn’t:
- PDFs or screenshots masquerading as “data”
- Copy-pasted lists from emails or Slack
- Files with inconsistent columns (e.g., some rows missing “Email”)
Pro tip: Before you import, just open your file in Excel or Google Sheets and eyeball it. If you see weirdness, ScrapeLi will too.
Step 2: Prep and Clean Your Data
Cleaning data isn’t glamorous, but it’s the difference between a smooth workflow and hours lost debugging. Here’s how to clean up your lead list:
2.1 Standardize Your Columns
Decide on the columns you actually need. Here’s a typical set for ScrapeLi:
- First Name
- Last Name
- Company
- LinkedIn URL
- (Optional) Job Title, Location, Notes
Delete anything else. Extra columns just slow you down and confuse the tool.
2.2 Remove Duplicates
Duplicates are the fastest way to waste credits and annoy prospects. In Excel or Google Sheets:
- Select your data
- Use “Remove Duplicates” (usually under Data > Data Cleanup)
- If you have both “Email” and “LinkedIn URL,” dedupe based on both
Heads up: ScrapeLi won’t always catch dupes for you. Do it now.
2.3 Sanity-Check Your Emails
If you’re importing emails, make sure they look like real addresses. At minimum:
- No blank emails
- No “test@test.com” or obvious fakes
- No weird formatting (commas, spaces)
If you’re feeling thorough, run them through a bulk email verifier. (Don’t go overboard unless your bounce rate is killing you.)
2.4 Clean Up LinkedIn URLs
Make sure those URLs start with “https://www.linkedin.com/in/” and aren’t just search results or company pages.
Remove tracking junk (anything after a “?” in the URL).
Bad:
https://www.linkedin.com/search/results/people/?keywords=jane%20doe
Good:
https://www.linkedin.com/in/janedoe
2.5 Fill in the Blanks (Or Don’t)
If a row is missing critical info (like both email and LinkedIn URL), ask yourself: is it worth importing? Most of the time, partial data just clogs your workflow.
- Delete rows that are mostly empty
- Keep only what you can actually use
2.6 Save As a Fresh CSV
Once you’ve cleaned up, export your sheet as a new CSV file. Avoid Excel formats if you can—CSV is less likely to break things.
Pro tip: Give the file a clear, date-stamped name—like leads-cleaned-2024-06-20.csv. You’ll thank yourself later.
Step 3: Importing Into ScrapeLi
Now for the easy part.
- Log in to ScrapeLi.
- Go to the "Import Leads" or "Upload" section (ScrapeLi moves things around, but it’s usually obvious).
- Choose your cleaned CSV file.
- Map your columns. ScrapeLi will try to auto-detect, but double-check—especially for “LinkedIn URL” and “Email.”
- Hit import and wait for the confirmation.
What to watch for: - If ScrapeLi throws errors, check your file for blank rows or weird headers. - If you see “Unmapped” columns, fix your CSV and try again. Don’t just ignore them.
Pro tip: Start with a small batch (say, 20 leads) to test your process. Fix problems before importing the whole list.
Step 4: Verify and Tag Your Imported Leads
Once your leads are in ScrapeLi, don’t assume everything worked perfectly. Spot-check your data:
- Are names and emails in the right fields?
- Are any rows totally blank?
- Did ScrapeLi mangle your formatting?
If you see issues, go back to your CSV, fix them, and re-import.
Tag Your Leads:
Use ScrapeLi’s tagging or segmentation features to label this batch—e.g., “June2024-Webinar.” This’ll save you a ton of time later when you want to filter or report.
Step 5: Automate What Makes Sense—But Don’t Overdo It
ScrapeLi offers automation features like enrichment or sequence triggers. These are great if your data is clean. If not, they’ll just amplify your mistakes.
Automate: - Lead enrichment (if you have at least one reliable unique field, like LinkedIn URL or email) - Intro sequences (for small, well-segmented batches)
Don’t automate: - Outreach to lists you haven’t checked - Lists with lots of blanks or questionable emails
Rule of thumb: If you wouldn’t send this list to your boss, don’t send it to real prospects.
Step 6: Keep Your Workflow Simple (and Fix It as You Go)
The best workflow is the one you’ll actually use. Here’s what I recommend:
- Always clean your data before importing.
- Use clear, consistent file names and tags.
- Test with small batches before going big.
- Don’t chase every new feature—stick to what solves your actual problem.
What to ignore:
ScrapeLi and similar tools love to tout “AI cleaning” or “one-click enrichment.” Reality: they’re only as good as your input. Fancy features won’t save you from sloppy prep.
Troubleshooting: Common Issues and How to Fix Them
Problem: Imported leads missing key fields
Fix: Check your CSV headers and make sure they exactly match what ScrapeLi expects.
Problem: Special characters or weird symbols show up
Fix: Save your CSV as UTF-8. Google Sheets does this by default; Excel sometimes doesn’t.
Problem: “Duplicate” warnings, even after deduping
Fix: Make sure you checked both emails and LinkedIn URLs. Sometimes similar names trick the system.
Problem: Can’t map columns during import
Fix: Rename your CSV headers to simple, obvious terms (“Email,” not “E-mail Address”).
Pro Tips for Staying Sane
- Batch your imports: Don’t try to clean and import thousands of leads in one go. Do it in manageable chunks.
- Document your process: Jot down what works for you. Next time, you’ll spend less time guessing.
- Don’t trust automation to catch your mistakes. It won’t.
Wrapping Up
Data cleaning isn’t fun, but it’s absolutely worth the effort. If you keep your process simple—prep, clean, import, and check—you’ll spend less time firefighting and more time actually working your leads. ScrapeLi is a solid tool, but it’s not magic. Your workflow is only as good as your data. Start small, stay organized, and iterate as you go. That’s really all there is to it.
Now, go run your first clean import. Future you will thank you.