If you've spent any time inside a CRM, you know the pain of duplicate records. Multiple entries for the same company or contact, weird data conflicts, sales teams tripping over each other—it's a mess. If you’re looking for a practical way to actually fix this (not just talk about “data hygiene”), this guide’s for you. I'll walk you through how to clean up duplicates using Leadspace matching, with real talk about what works, what doesn’t, and what’s just hype.
Why Duplicates Happen—and Why They Matter
Let’s call it out: CRM deduplication is boring, but skipping it costs you. Duplicates creep in from imports, manual entry, web forms, and integrations. Here’s what happens if you ignore them:
- Sales reps waste time chasing the same lead
- Marketing campaigns misfire
- Reporting is a joke
- Customer experience tanks
You need a systematic way to find and merge duplicates, not just “spot clean” when someone complains.
What Is Leadspace Matching (and Where Does It Help)?
Leadspace is a data platform that claims to “unify and enrich” B2B data. The matching feature is supposed to help you spot and link duplicate records by comparing what’s in your CRM with its own (pretty robust) database. In theory, this means:
- Finding duplicates you’d never notice manually
- Merging records with more confidence
- Filling in missing info with cleaner, third-party data
But let’s be honest, no tool is magic. Leadspace is best at matching company data, a little less perfect on contacts, and it won’t untangle years of bad CRM habits overnight.
Step 1: Prep Your CRM—and Your Expectations
Before you start matching, get your house in order. Otherwise, you’ll just automate chaos.
Do this first:
- Audit your fields. Figure out which fields actually matter for matching—usually name, email, company, maybe phone.
- Standardize formats. Make sure emails, phone numbers, and names are at least consistently formatted. Garbage in, garbage out.
- Back up your data. Seriously, take a backup. Merges can go sideways.
- Decide the rules. Are you going to pick the oldest record? The most complete? The one with the most activity? Decide now, not mid-way through.
Pro tip: If your CRM is full of “test” records or obvious spam, delete those before you even start Leadspace matching. No sense cleaning up junk.
Step 2: Connect Leadspace to Your CRM
Leadspace integrates with most major CRMs (Salesforce, HubSpot, Dynamics, etc.), but the devil’s in the details.
- Get admin access. You’ll need it for the initial connection.
- Review API limits. Leadspace can burn through a lot of API calls, so check your CRM’s daily limits.
- Map your fields. Don’t just accept defaults—make sure Leadspace is looking at the right fields for matching.
- Test with a small batch. Never run it on your full database on day one. Pick a subset to see how the matching behaves.
Honest take: The initial integration is usually straightforward, but field mapping can be frustrating. Leadspace’s support is decent, but expect some trial and error.
Step 3: Configure Your Matching Logic
This is where most cleanup projects go off the rails. Leadspace offers options for “fuzzy” matching (close matches, typos, etc.) versus strict (only exact matches). Here’s how to think about it:
- Strict matching: Good for unique fields (email, company domain). Low risk, but you’ll miss some duplicates.
- Fuzzy matching: Catches more, but can create false positives (matching “Jon Smith” at two different companies).
Set your tolerance: - Don’t get greedy. Start with strict or moderate settings. You can always widen the net later. - Use company domain as your anchor for companies. For people, email is king—but not always present.
What to skip: Don’t bother with fuzzy matching on phone numbers or addresses. The data is usually too messy to be reliable.
Step 4: Run a Pilot Match
Now, actually run the Leadspace matching process—but only on a small, controlled sample.
- Pick a test group. 200–500 records is plenty.
- Review the matches manually. Are these real duplicates or is Leadspace getting confused?
- Check for false positives/negatives. Did it miss obvious duplicates? Is it combining unrelated records?
- Adjust your rules. Tweak the matching settings based on what you see—don’t just accept defaults.
Pro tip: Involve someone from sales or support to sanity-check the matches. They’ll know if two “John Smiths” are actually the same person.
Step 5: Merge and Clean Up (Carefully)
Once you’re happy with your settings, it’s time to actually merge duplicates.
- Start with automatic merges. Only for slam-dunk matches (exact email/domain, lots of field overlap).
- Flag the rest for review. Don’t force merges if you’re not sure—they’re a pain to undo.
- Document what you’re doing. Keep a log of merges, rules, and anything you override. You’ll thank yourself (or your team will) later.
- Handle conflicting data. Decide which fields “win” (most recent, most complete, etc.). Some CRMs let you set these preferences.
What not to do: Don’t try to merge everything in one go. Go department by department, or segment by segment. You’ll avoid chaos and angry users.
Step 6: Fill in the Gaps with Leadspace Enrichment (Optional, but Useful)
Leadspace can also enrich records—filling in missing fields like company size, industry, email, and so on. This can make your cleaned-up records more useful.
- Enrich only after deduping. Otherwise, you’re just giving duplicates more data.
- Pick only fields you actually use. Don’t clutter your CRM with data no one cares about.
- Watch for overwrites. If your sales team loves their custom notes, make sure enrichment doesn’t overwrite them.
Skeptical take: Enrichment is great for filling blanks, but don’t expect it to uncover magical new leads. It just makes your existing data a little more complete.
Step 7: Put Guardrails in Place to Prevent Future Duplicates
Cleaning up is only useful if you keep it that way. Set up some basic protections:
- Turn on duplicate alerts in your CRM, if available.
- Automate regular Leadspace matching—monthly or quarterly is realistic for most teams.
- Train your team. Make it clear how to check for existing records before adding new ones.
- Limit manual imports or mass uploads without checks.
What to ignore: Don’t waste time on “data stewardship committees” unless you’re a giant enterprise. Most teams just need a couple of clear rules and a regular cleanup schedule.
Common Pitfalls (and How to Avoid Them)
- Over-merging: Combining two different people or companies because their names are similar. If in doubt, keep them separate.
- Not testing on real data: Always pilot your process—CRM data is messier than you think.
- Ignoring user feedback: If users complain about “missing data,” listen. Sometimes the merge rules need tweaking.
- Relying only on tools: Leadspace is helpful, but no tool replaces common sense and a bit of manual review.
Wrapping Up: Keep It Simple, Keep It Going
You don’t need a PhD in data science to keep your CRM clean. Start with a small batch, use Leadspace to automate the boring parts, and review as you go. Expect a few hiccups—CRM data is never perfect. Don’t chase “perfect data;” just aim for “not embarrassing” and keep at it.
Take a breath, make a backup, run that first match, and get on with the real work. Your future self (and your sales team) will thank you.