If you’re spending money and time reaching out to leads, you can’t afford to get contact data wrong. Bad info means wasted campaigns, missed sales, and frustrated teams. This guide is for anyone using BetterContact and tired of chasing ghosts in their CRM. I’ll walk you through practical steps to actually improve contact data enrichment—no buzzwords, just what works.
Why Data Enrichment Matters (and Where it Goes Wrong)
Let’s be clear: enrichment isn’t just about stuffing your contacts with more fields. It’s about filling in the blanks that actually help you target and segment. The problem? Most enrichment tools (including the ones built into CRMs) overpromise. You end up with half-complete job titles, outdated company info, or “insights” that don’t help you prioritize leads.
If you want accurate targeting, you need a process that’s more than just flipping a feature toggle. Here’s how to get it right.
Step 1: Audit Your Current Contact Data
Before you enrich anything, figure out what you’re working with. Otherwise, you’ll just be piling questionable data on top of a messy foundation.
- Export a sample of your contacts—don’t just look in-app, actually pull the data.
- Scan for gaps: Which fields are empty or full of junk? Pay close attention to emails, job titles, company names, and any custom fields you use for targeting.
- Find duplicates and weird entries: Think: “Firstname: CEO” or “Company: Home.”
Pro tip: Don’t assume your sales or marketing team is entering data the same way. Check for inconsistent formats (“VP, Sales” vs. “Vice President Sales”).
Step 2: Decide What Data Actually Matters
Not every field is worth enriching. More data isn’t always better—it just makes things harder to maintain.
- List out your targeting criteria. Are you segmenting by industry, company size, seniority, location, or something else?
- Cut the noise. If you’re never going to use “Twitter handle” or “Fax number,” don’t bother enriching it.
- Pick your “must-have” fields. Usually, you want accurate emails, job title/role, company name, and maybe a couple of vertical-specific fields.
What to skip: Fluffy “intent” signals, personality scores, or anything you can’t actually act on. These are usually guesses—don’t pay extra for them.
Step 3: Set Up Data Enrichment in BetterContact
BetterContact has built-in enrichment features, but they’re only as good as your setup. Here’s how to get the most out of them:
- Connect trusted data providers.
- If BetterContact lets you choose enrichment sources, pick reputable ones. Don’t just click every integration—check reviews and, if possible, test sample data.
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Watch out: Cheap or “free” data providers often have outdated or scraped info that’ll pollute your CRM.
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Configure enrichment rules.
- Decide which fields get enriched and when (on import, on update, or manually).
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Set rules to not overwrite verified data. You don’t want a bad enrichment source overwriting a correct phone number with a generic company switchboard.
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Test with a small batch.
- Don’t run enrichment on your whole database out of the gate. Run it on 100–500 records, then check the results.
- Look for obvious mismatches or embarrassing errors.
Pro tip: Disable auto-overwrite for critical fields unless you’re 100% confident in the data source.
Step 4: Clean Up Before and After Enriching
The best enrichment in the world won’t fix a dirty database. Do a quick clean-up before and after the process:
- Deduplicate contacts. Use BetterContact’s merge tools or a third-party deduplication service. Manual checks for big accounts are worth it.
- Standardize formats. Make sure names, job titles, and phone numbers follow a consistent style. “VP, Sales” and “Vice President, Sales” should mean the same thing in your segments.
- Remove dead contacts. If an email bounces or a contact hasn’t engaged in forever, archive them. Enriching these is a waste of money.
What not to worry about: Don’t obsess over every minor typo. Focus on the fields that matter for segmentation and outreach.
Step 5: Automate—But Don’t Set and Forget
Automating enrichment saves time, but it’s not a Ronco rotisserie—you can’t just “set it and forget it.”
- Schedule enrichment runs for new contacts, but review results regularly. Bad data slips through even the best APIs.
- Flag and review exceptions. If the system can’t enrich a field, decide if it’s worth manual research or just leave it blank.
- Build a feedback loop: If sales or marketing finds a bad record, make it easy for them to flag and fix it. Even a shared Google Sheet beats nothing.
Caution: Automation amplifies mistakes. One bad mapping rule can ruin thousands of records overnight if you’re not paying attention.
Step 6: Use Enriched Data for Smarter Targeting
Now that your contact data is cleaner and richer, actually put it to work:
- Segment with confidence: Build lists based on your must-have fields. Don’t pretend you have perfect data—use “likely” matches and test your segments.
- Personalize outreach: Use accurate job titles and company names for better open and reply rates.
- Score and prioritize: If you have enough data, use it to score leads (just keep your scoring rules simple and transparent).
What to ignore: Overly complex lead scoring models, “AI-powered” segmentation that you can’t explain, or any targeting that relies on data you don’t trust.
Troubleshooting Common Issues
Even with the best process, you’ll hit snags. Here’s how to handle them:
- Low match rates? Your data provider might be weak, or your contact info is too spotty. Try filling in emails or LinkedIn URLs first—they’re the best keys for enrichment.
- Conflicting data? When two sources say different things, default to the one with the most recent update, or the one you trust more. Document your logic.
- Field bloat? If you end up with dozens of fields you never use, prune aggressively. Too much clutter just slows everyone down.
Real Talk: What Works, What Doesn’t
What works: - Keeping your data simple and focused on what actually drives revenue. - Regular, small-batch enrichment and cleanup—not once-a-year “data days.” - Letting real users (sales, marketing, support) flag problems.
What doesn’t: - Trusting any vendor who claims 95%+ accuracy. That’s fantasy. - Enriching every possible field “just in case.” - Ignoring cleanup because “the tool will fix it for me.”
Ignore the hype: There’s no such thing as a fully “automated, AI-driven data enrichment engine” that gets it right 100% of the time. Good enrichment is part process, part tooling, part common sense.
Keep It Simple—and Iterate
Don’t get paralyzed trying to make your database perfect. Start with the fields that matter, clean them up, enrich from sources you trust, and sanity-check the results. Every quarter or so, revisit your enrichment rules and see what’s actually helping your team target better.
If you keep it simple and improve in small steps, you’ll get far better results than chasing the latest “AI-powered” add-on. Clean data beats clever tools every time.