If you’re in charge of B2B lead management, you know that duplicate records are the enemy. They mess with reporting, waste sales time, and make your data look like a junk drawer. If you’re using Tamr to clean up and merge your leads, good news: it can actually help, if you set it up right. This guide cuts through the noise and shows you step-by-step how to merge duplicates in Tamr for cleaner, more reliable B2B data—without falling for buzzwords or wishful thinking.
Why bother merging? (And what happens if you don’t)
Let’s get real: ignoring duplicates means...
- Your sales team calls the same company twice—awkward.
- Marketing spends money on leads you already have.
- Reporting becomes a guessing game.
- You annoy potential customers with repeat outreach.
Bottom line: merging isn’t optional if you want to look competent.
Step 1: Get your data ready (don’t skip this)
Tamr’s matching is only as good as the data you feed it. Garbage in, garbage out. Before you even open Tamr, do a quick check:
- Standardize columns: Make sure company names, emails, and phone numbers are in the same format across sources.
- Trim whitespace: “Acme Inc” and “ Acme Inc ” might look the same to you, but not to software.
- Consistent casing: Stick to upper or lower case for fields you’ll match on.
- Remove obvious junk: Nulls, placeholders like “N/A,” or test records? Delete them.
Pro tip: If your data is a mess, Tamr won’t save you. Clean it up as much as you can first.
Step 2: Import your lead data into Tamr
Once your spreadsheets or databases are cleaned up, it’s time to get your records into Tamr.
- Use Tamr’s connectors to pull from Salesforce, HubSpot, CSVs, or wherever your leads live.
- Map your source fields to Tamr’s schema. This means making sure “Company Name” from Salesforce lines up with “Company” from your spreadsheet, etc.
- Double-check field types (string, integer, etc.)—mismatches can break matching later.
What to ignore: Fancy integrations and automation promises. Get the data in manually first, so you know what’s actually there.
Step 3: Set up your matching rules (the heart of deduplication)
This is where Tamr shines—or falls flat, if you don’t configure it right.
How Tamr does matching
Tamr uses machine learning and rules to figure out which records are duplicates. You can set it to “auto” and hope for the best, but it’s almost always better to get your hands dirty.
Key fields to use: - Company Name - Website domain - Email (especially company domain) - Phone number - Address
Building smart matching rules
- Start simple: Match on company name and website domain. If both match, it’s probably a duplicate.
- Fuzzy matching: Tamr can catch “Acme Inc” and “Acme Incorporated” as a match. But don’t crank the fuzziness up too high—or you’ll get false positives.
- Add thresholds: Require two or more fields to match before merging records. This reduces the risk of merging unrelated companies with similar names.
- Manual review buckets: Send "maybe" matches to human review instead of merging automatically.
What works: Combining exact matches with fuzzy logic on key fields.
What doesn’t: Trusting the out-of-the-box model to get it right without supervision.
Step 4: Train Tamr’s model (only as good as your feedback)
Tamr learns from your “yes, these are duplicates” and “no, these aren’t” decisions. You actually have to review and correct its guesses.
- Go through Tamr’s proposed matches. Approve true duplicates, reject false ones.
- The more you do, the better Tamr gets. But don’t expect perfection.
- Prioritize high-impact leads (the ones sales actually cares about) for review first.
Pro tip: Don’t treat model training as a one-and-done task. Plan to revisit regularly—your data and business rules will change.
Step 5: Merge records (finally!)
Once you’re satisfied with the matches, it’s time to actually merge. Here’s what to watch out for:
- Survivorship rules: Decide which values to keep if fields conflict. For example, keep the most recent phone number, or the most complete address.
- Keep source tracking: Don’t lose where each bit of info came from. Tag merged fields with their origin, so you can untangle it later if needed.
- Backup before merging: Always. Even if Tamr claims it’s safe.
After merging, export the results to wherever you manage leads—CRM, marketing platform, etc.
What to ignore: “Set it and forget it” promises. Even with good models, you’ll need to spot-check results.
Step 6: Monitor and tune over time
Duplicate management isn’t a project—it’s ongoing housekeeping.
- Schedule regular dedupe runs. Weekly or monthly is typical.
- Watch for new data sources. New lists or integrations can bring new duplicates.
- Spot-check merged records. Look for weird merges or missed duplicates.
- Tweak matching rules as your business changes. Maybe you start caring about subsidiaries, or merge on phone number instead.
What works: Small, regular adjustments. Don’t let it drift.
What Tamr does well (and where it struggles)
The good:
- Handles big, messy datasets way better than Excel or manual review.
- Flexible matching rules (once you learn the ropes).
- Can combine machine learning with human review—best of both worlds.
The not-so-good:
- Needs a decent amount of setup and babysitting.
- Out-of-the-box settings are just a starting point, not a magic bullet.
- Fuzzy matching can go haywire if you’re not careful—watch for over-merging.
- Model training takes time, and you need someone who actually knows your data.
Don’t buy the hype: No tool—including Tamr—can read your mind or fix bad source data for you.
Pro tips for success (from the trenches)
- Start with a small batch: Test your rules on 1,000 records, not a million.
- Document your rules: If you leave, someone else should be able to pick up where you left off.
- Keep sales in the loop: They’ll spot bad merges faster than any algorithm.
- Automate exports, not reviews: You can pipe clean data out; don’t automate approval of merges unless you’re really sure.
Keep it simple, iterate often
Don’t over-engineer your deduplication. Start with the basics, see what works, and tweak as you go. The goal isn’t perfection—it’s making your leads reliable and useful for the people who need them. Keep it simple, review your results, and don’t be afraid to throw out rules that don’t serve you. Your future self (and your sales team) will thank you.