If you’ve ever stared at a massive B2B contact list filled with typos, duplicates, and half-baked entries, you know the pain. Cleaning and deduplicating this kind of data isn’t just busywork—it’s the difference between effective outreach and embarrassing mistakes. Whether you’re in sales ops, marketing, or just the unlucky soul in charge of the CRM, this guide is for you. Here’s how to use Tamr, a data mastering tool, to make sense of the chaos.
Why B2B Contact Lists Are Such a Mess
Let’s get real. Most B2B lists are stitched together from years of imports, exports, and third-party vendors. Common problems:
- Duplicates: “Jane Smith” at “ACME Corp” shows up five times, spelled three different ways.
- Inconsistent formatting: Emails in ALL CAPS, company names with/without “Inc.,” phone numbers with random dashes.
- Incomplete records: Blank fields, outdated info, or just “asdf” in the notes section.
- Multiple sources: Lists bought, scraped, or exported from different CRMs—each with their own quirks.
You can clean up a few hundred rows in Excel if you’re desperate, but once you get into tens or hundreds of thousands, you need something built for the job.
What Tamr Does (and Doesn’t)
Tamr is designed for large-scale data mastering. It uses machine learning to group similar records, fill in gaps, and help you build a single, trusted list. But let’s be clear: Tamr isn’t magic. It won’t fix everything with one click, and it definitely needs a human to guide it. Still, it’s a big step up from manual cleanup or cobbled-together scripts.
Step 1: Get Your Data Ready
Before you touch Tamr, you need to know what you’re working with.
Gather Your Sources
- Export your contact lists from CRMs, spreadsheets, email platforms, whatever you’ve got.
- Check for file formats Tamr supports—CSV and Excel are safe bets.
- Make a note of where each file came from (you’ll want this for troubleshooting later).
Basic Pre-Cleaning (Don’t Skip This)
Tamr can handle a lot, but garbage in, garbage out applies. Spend an hour on the basics:
- Standardize column names (“First Name” vs. “FName” vs. “First”).
- Make sure emails and phone numbers are in their own columns.
- Delete obvious junk rows.
- Remove columns you’ll never use (do you really need “Last Modified By”?).
Pro Tip: If you’ve got a million rows and the file is sluggish, split it into chunks or use a database export.
Step 2: Import Into Tamr
Once you’ve got your files, it’s time to get them into Tamr.
- Log into Tamr.
- Create a new project.
- Upload your data sources—Tamr lets you connect files, databases, or cloud sources.
- Map your columns to Tamr’s schema fields (e.g., “Company Name” to “company_name”).
Tamr will preview your data. Double-check that things land in the right columns—small mistakes here cause big headaches later.
What to Ignore: Don’t waste time fixing every little formatting detail yet. Tamr can handle a lot of that during processing.
Step 3: Configure Matching Rules
This is where Tamr earns its keep.
How Tamr Finds Duplicates
Tamr uses a combo of rules and machine learning to spot duplicate records—even if names are misspelled or companies are listed with/without “LLC.” Out of the box, it’ll look at things like:
- Names (with fuzzy matching)
- Email addresses
- Phone numbers
- Company names
Set Your Priorities
You’ll need to guide Tamr:
- Tell it which fields are most important for matching (e.g., email is usually more reliable than name).
- Add any “must match” rules—like, only merge records if company and email are both similar.
- Adjust thresholds for how close is “close enough.” Too strict: you’ll miss true matches. Too loose: you’ll accidentally merge different people.
Pro Tip: Start with conservative settings—better to miss some duplicates than to merge two very different contacts.
Training Tamr
The first time Tamr runs, it’ll suggest matches. You review a sample—accept or reject their suggestions. The more you teach it, the smarter it gets. This takes some effort, but it’s way faster than doing everything by hand.
Step 4: Review and Approve Matches
This is the “trust but verify” phase.
- Tamr will group potential duplicates for review.
- You (or your team) approve or reject matches. Focus on edge cases—Tamr is good, but not perfect.
- Use Tamr’s interface for bulk actions (approve/reject in batches if you’re confident).
What Works: Tamr handles common duplicates and even “tricky” ones (e.g., “Jon Smith” vs. “John Smyth” at the same company).
What Doesn’t: If your data is really inconsistent or full of nicknames, Tamr may struggle. Don’t expect miracles with garbage data no one could fix—not even a human.
Step 5: Merge and Clean Up Records
Once you’ve approved matches, Tamr can merge records:
- Pick rules for which value to keep when fields conflict (most recent, most complete, etc.).
- Fill missing fields using info from the best version of each contact.
- Output a single, deduplicated list.
Review the merged file—spot check for weird merges or missing info.
Ignore the Noise: Don’t panic about every minor inconsistency. The goal is “good enough for outreach,” not “perfect forever.”
Step 6: Export and Integrate
You’ve got your clean, deduplicated list. Now what?
- Export as CSV, Excel, or push directly to your CRM if Tamr supports it.
- Document what you did—note the date, rules used, and which sources were included.
- Set a reminder to do this again in a few months. No list stays clean for long.
Pro Tip: Keep the raw, pre-Tamr files around for reference, just in case you need to trace back a missing contact.
What to Watch Out For
A few honest takes from the trenches:
- False positives: Sometimes Tamr will merge two people who aren’t the same. That’s why review matters.
- Data drift: The more sources you add, the more you’ll find new edge cases. Don’t expect to set your rules once and be done forever.
- Cost and complexity: Tamr is overkill for small lists (under 10,000 rows) or one-off cleanups. It shines with big, hairy datasets and recurring needs.
Quick FAQ
Can’t I just use Excel or dedupe scripts?
Sure, for small lists and simple cases. But for big, messy B2B data, you’ll hit limits fast.
Is Tamr totally hands-off?
Nope. You still need to review matches and tune the rules. Think of it as a power tool, not a robot butler.
What about GDPR and privacy?
Cleaning data is usually fine, but be careful with exports and storing sensitive info. Tamr itself doesn’t “magically” make you compliant.
Keep It Simple, Iterate Often
Cleaning big contact lists is never truly “done.” The trick is to set up a process that’s good enough to be useful, then revisit as your data changes. Don’t chase perfection—aim for a list you can trust for your next campaign, and build from there. Tamr can save you a ton of time, but only if you keep things simple and keep an eye on the results. Happy cleaning.