If your lead lists are full of duplicates, you're not alone. Duplicates waste time, skew your metrics, and annoy everyone from sales to marketing. This guide is for anyone who uses Scrapin and wants real, step-by-step instructions to deduplicate leads—without all the vague promises and hand-waving. You'll get honest guidance, not magic-bullet nonsense.
Let's get your data cleaned up.
Why Deduplication Matters (And Why Scrapin Isn't Magic)
Before we dive in: yes, Scrapin has deduplication features. No, they're not perfect—and neither is any tool on the market. If someone tells you there's a one-click solution, they're selling snake oil.
Why bother with deduplication?
- Wasted effort: Salespeople chasing the same lead twice is embarrassing.
- Bad data: Duplicates make your numbers useless.
- Poor automation: Email sequences and CRMs can go haywire.
Scrapin gives you some tools, but you'll need a bit of strategy and diligence to do it right.
Step 1: Figure Out What a "Duplicate" Means for You
Not all duplicates are obvious. Is "john.smith@gmail.com" the same as "john.smith+newsletter@gmail.com"? What about "Acme Inc" and "Acme Incorporated"?
Decide up front: - What fields matter? (Usually email, sometimes phone or company) - Is an exact match required, or do you want to catch near-matches? - Will you keep the first, most recent, or most complete record?
Pro tip: Write this down somewhere. If your team isn't on the same page, deduplication will backfire.
Step 2: Export Your Leads from Scrapin
Yes, Scrapin has built-in deduplication, but doing an export gives you control and lets you audit changes. If you blindly trust any tool's "magic" dedupe, you're asking for trouble.
To export: 1. Go to your "Leads" dashboard. 2. Select the list(s) you want to clean up. 3. Click the "Export" button (usually top right). 4. Choose CSV (it's the most portable and easy to work with). 5. Download your file.
Tip: Keep a backup. Always. If something goes sideways, you can roll back.
Step 3: Inspect Your Lead List
Open your CSV in Excel, Google Sheets, or your spreadsheet tool of choice.
What to look for: - Obvious duplicates (same email, name, company) - Typos or inconsistent formatting ("Acme" vs "acme." vs "Acme Inc") - Blank fields
If you have a big list, use filters or conditional formatting to spot repeats.
Don't: Rely on your eyes alone. Even small lists hide duplicates.
Step 4: Clean Up Data Formatting
Garbage in, garbage out. If emails are in weird cases ("John.Smith@EMAIL.com"), or company names are inconsistent, deduplication tools won't catch them.
Standardize: - Make all emails lowercase. - Trim spaces. - Normalize company names (pick a standard format).
How:
- In Excel: Use formulas like =LOWER(A2) for emails.
- In Google Sheets: Similar—=LOWER(), =TRIM(), etc.
Pro tip: If you're not comfortable with spreadsheets, take ten minutes and learn these basics. It'll save you hours in the long run.
Step 5: Use Scrapin's Deduplication Tools
Now you can use Scrapin’s built-in deduplication—but do it with your cleaned data.
How Scrapin Handles Duplicates
- Scrapin typically matches on email addresses by default.
- Some plans or integrations let you match on phone or company.
- It can merge records or just mark duplicates.
To deduplicate in Scrapin: 1. Go to the "Leads" section. 2. Look for the "Deduplicate" or "Remove Duplicates" option (usually in the list actions menu or settings). 3. Choose your matching criteria (email is safest; experiment with others if you’re brave). 4. Run the deduplication.
What works: - Simple, exact matches—emails, sometimes phone numbers. - Flagging obvious repeats.
What doesn't: - Fuzzy matching (typos, similar names) is hit-or-miss. Don’t trust it blindly. - Merging custom fields or picking the "best" data—Scrapin's logic is basic.
Ignore: Any promises of "AI-powered" fuzzy matching unless you test it on a small sample first.
Step 6: Manually Review the Results
No tool gets it right 100% of the time, especially with messy real-world data.
Check: - Are any legit leads gone? (Check your backup.) - Did Scrapin merge records correctly? - Are new issues introduced (like blank fields or partial merges)?
If you spot problems, you might need to restore from backup and try again with tweaks to your process.
Step 7: Re-import Cleaned Leads (Optional)
If you did a lot of cleanup outside Scrapin—or if the built-in tool mangled anything—you might want to delete your old list and re-import the cleaned version.
How: 1. Delete the old lead list (after backing it up!). 2. Import your cleaned CSV. 3. Double-check that everything looks right.
Caution: Importing can sometimes create new duplicates if your import settings are off. Always test with a small batch first.
Step 8: Set Up an Ongoing Deduplication Routine
Deduplication isn't a one-time thing. Set a reminder to clean your lists regularly, especially before major campaigns or hand-offs to sales.
Tips: - Make data formatting rules part of your process. - Train your team to stick to those rules. - If you import leads from multiple sources, deduplicate before importing.
What to Ignore (and What to Watch Out For)
- Ignore: Any add-on or script that claims to find "hidden" duplicates without showing you the logic. If you can't see how it works, it's risky.
- Be skeptical: of promises that deduplication will fix all your bad data problems. It won't.
- Watch out: for overzealous matching—sometimes, two people share an email domain or name. Don't nuke good leads.
A Few Tools That Actually Help
Scrapin's built-in tools are fine for basics, but if you need more power:
- Excel/Google Sheets: Still the best for large, one-off cleanups.
- OpenRefine: For heavy-duty data cleaning. Steep learning curve, but powerful.
- Deduplication add-ons: Some CRM integrations offer better matching, but always use with caution.
Keep It Simple—And Iterate
Cleaning up your leads isn't glamorous, but it's worth the time. Don't get lost in tool features or promises. Start simple: define your rules, clean your data, use Scrapin's tools carefully, and always keep a backup.
If you mess up, that's normal. Just restore and tweak your process. The goal isn't perfection—just better data, fewer headaches, and more accurate results next time around.