If you’ve ever scraped a bunch of leads with Instant Data Scraper, you know the raw results can be a mess—missing names, weird columns, inconsistent formats, and more. Before you dump all that into your CRM and make things worse, you need to clean and organize it. This guide is for anyone who wants to turn that jumbled CSV into something your CRM (and your team) will actually understand.
Let’s get straight to it. Here’s how to take data from “dumpster fire” to “import-ready.”
Step 1: Understand What You’ve Scraped
Start by opening the CSV from Instant Data Scraper in Excel or Google Sheets. Don’t rush to “just import it”—see what you’re working with.
What you’ll probably find: - Messy column names (like “Link” or “Unnamed: 0”) - Duplicated rows - Partial or missing data (first names, company names, phone numbers) - Weird formatting (numbers as text, inconsistent capitalization)
Pro tip: If you scraped multiple pages or sources, combine all the files into one sheet before you start cleaning. Dealing with them piecemeal just wastes time.
Step 2: Decide What You Actually Need
Don’t just import every column because it’s there. Figure out what your CRM actually expects. Typically, you’ll want: - Name (first and last) - Email - Company - Phone number (if available) - Website or LinkedIn - Any relevant tags or notes
Ignore columns you don’t need, like “Profile Picture Link” or random URLs.
Honest take: The more fields you try to import, the more likely something breaks. Stick to essentials, especially on your first pass.
Step 3: Clean Up Column Names
CRMs like clean, predictable field names. Rename columns to match your CRM’s import template exactly.
- “Full Name” ➔ split into “First Name” and “Last Name”
- “Company Website” ➔ “Website”
- “Phone #” ➔ “Phone”
If your CRM has an import template, use that as your reference. It’ll save you a ton of headaches.
Step 4: Remove Duplicates
Duplicate contacts are the number one way to annoy your sales team (and mess up your CRM).
How to do it: - In Excel or Google Sheets, highlight your data. - Use “Remove duplicates” (it’s under Data > Data cleanup in Sheets, Data > Remove Duplicates in Excel). - Decide which columns to check—usually email, sometimes name + company.
Caution: If your emails are missing, you might have to match on name and company, which isn’t perfect. Do your best.
Step 5: Standardize and Validate Data
Time to get a little more hands-on.
a) Split Full Names
If you have a “Full Name” column, split it into First and Last. Use “Split Text to Columns” in Sheets or Excel. You’ll need to fix edge cases (people with two first names, etc.) by hand.
b) Fix Email Formatting
- Make sure emails are all lowercase and free of spaces.
- Delete obviously fake or broken emails.
c) Standardize Phone Numbers
Pick a format (“(555) 123-4567” or “+1-555-123-4567”) and stick with it.
- Remove spaces, dashes, and odd characters.
- Use formulas like =SUBSTITUTE(A2," ","") if needed.
If you have international contacts, add country codes.
d) Clean up URLs
- Remove tracking junk (anything after “?” in the URL).
- Make sure websites all start with “http://” or “https://”.
e) Consistent Capitalization
- Names: Proper case (“John Smith,” not “john smith”)
- Companies: Pick what looks best, but be consistent.
Pro tip: Don’t try to “auto-fix” everything with scripts unless you know what you’re doing. You’ll just end up with more problems.
Step 6: Fill in Missing Data (If You Must)
You’ll probably have gaps—missing emails, half-empty companies. Here’s what’s worth doing:
- If you’re missing an email: Check if you have a LinkedIn or website, and see if you can find it quickly. If not, skip.
- Missing company name: Sometimes you can infer it from the email domain. Sometimes you just have to leave it blank.
Don’t waste an hour hunting down a single phone number. If it’s not there, move on.
Step 7: Add Tags or Notes (Optional)
If you want to track where these leads came from, add a “Source” column—like “Scraped from LinkedIn Jan 2024.” This will help you later when you want to see which sources actually work.
You can also add tags if your CRM supports them. Don’t overthink it.
Step 8: Save as a Clean Import File
- Save your final, cleaned sheet as a CSV (not Excel or Google Sheets format).
- Double-check that there are no empty rows at the bottom.
- Make sure your header row matches your CRM’s template exactly.
Pro tip: Keep a backup of the raw, untouched data. You’ll be glad you did if something goes wrong.
Step 9: Test Import with a Small Sample
Before you import 2,000 leads and realize something’s off, do a test with 5–10 rows.
- Import the sample file into your CRM.
- Check: Are the fields mapping correctly? Any weird errors?
- Fix issues, then re-export your “clean” CSV if needed.
What to ignore: Fancy import features that promise to “auto-map” everything. They usually don’t work as well as advertised.
Step 10: Import and Watch for Common Pitfalls
Once your test looks good, go ahead and import the full file.
Common issues to watch for: - Fields mapped to the wrong place (e.g., company name in the “first name” field) - Encoding errors (weird symbols instead of letters—make sure you save as UTF-8 CSV) - Duplicates sneaking in (if your CRM doesn’t dedupe automatically)
If you run into errors, don’t panic. Go back to your CSV, fix the problems, and try again.
What Actually Works (And What Doesn’t)
Works: - Keeping your import as simple as possible - Testing with small samples - Leaning on spreadsheet tools for cleaning
Doesn’t work: - Relying on “automatic” import tools (they miss stuff) - Trying to fix everything with one magical script - Overloading your CRM with junk data “just in case”
Wrap-Up: Keep It Simple, Iterate Fast
Cleaning scraped data is tedious, but if you keep things simple and focus on just what you need, you’ll get through it. Don’t obsess over perfection—just avoid the big mistakes (duplicates, bad mapping, broken formats). Each time you do this, you’ll get faster and make fewer mistakes.
Remember: Bad data in means bad results out. Take the time to get it right, but don’t let perfect be the enemy of progress. Good luck!