Enrichment Data Accuracy: How to Verify Your Data Provider's Quality

Every data provider claims 95%+ accuracy. The marketing pages all say the same things: “verified emails, validated phone numbers, fresh data”. But when you actually run campaigns on that "verified" data, the bounce rates tell a different story.

Here's the uncomfortable truth: independent testing shows most B2B data providers deliver around 50% accuracy on average, despite claiming much higher. That gap between marketing claims and real-world performance costs companies millions in wasted outreach, damaged sender reputation, and sales cycles spent chasing people who left their jobs months ago.

The providers know this. That's why they bury accuracy definitions in footnotes and make it nearly impossible to compare apples to apples. "Verified" might mean an email passed syntax checking. Or it might mean someone actually confirmed deliverability. Two very different things.

Data enrichment accuracy isn't something you can take on faith. You need to test it yourself, with your own methodology, before committing budget and campaigns to any provider.

What Accuracy Actually Means (And Why Definitions Matter)

When a provider says "95% accurate," what are they measuring?

This question matters more than most buyers realize. Accuracy can refer to wildly different things depending on who's doing the measuring and what they're counting.

Match rate measures whether the provider returns any data for a given input. You send them a name and company; they return an email. That's a match. But the email might be wrong, outdated, or for the wrong person entirely. High match rates can coexist with terrible accuracy.

Deliverability rate measures whether emails actually reach inboxes. This is closer to useful, but still incomplete. An email might deliver to a generic info@ address when you needed a decision-maker's direct contact.

Verification accuracy measures whether returned data has been validated against real-world sources. The email was tested via SMTP handshake. The phone number was confirmed active. The job title was cross-referenced against LinkedIn.

Freshness measures how recently data was validated - not just when it was first collected, but when someone last confirmed it's still accurate. B2B data decays at roughly 2% per month. Data "verified" six months ago isn't really verified anymore.

When evaluating providers, ask specifically what their accuracy claims measure. If they can't explain their methodology clearly, that's a red flag.

The Four Dimensions of Data Quality

Data enrichment accuracy isn't one metric, but it's several working together. A provider might excel at one dimension while failing at another.

Coverage

What percentage of your target contacts can the provider actually enrich? If you send 1,000 records and only 400 return with data, that's 40% coverage. High accuracy on the 400 means nothing if you can't reach the other 600.

Coverage varies dramatically by geography, company size, and industry. A provider crushing it in US enterprise might completely fail with European SMBs. Test against your actual target market, not their best-case demos.

Match Accuracy

Of the records that return data, how much is actually correct? This is where provider claims diverge most sharply from reality.

An 85% match rate with 95% accuracy beats a 95% match rate with 70% accuracy. The first scenario gives you 808 usable contacts per thousand. The second gives you 665. Always prioritize accuracy over match rate when they conflict.

Completeness

Does the provider return all the fields you need, or just the easy ones? Finding an email address is relatively straightforward. Direct dial phone numbers are much harder. Technographic data harder still.

Evaluate completeness per field. A provider might hit 90% on emails but only 40% on mobile numbers. If phone outreach is core to your motion, that 40% is the number that matters.

Freshness

How old is the data when you receive it? B2B contacts change jobs at roughly 20-25% annually. That means a quarter of any database is outdated within a year, even if it was perfect when collected.

The best providers refresh data continuously, every 30 days or better for actively used records. Others rely on stale snapshots updated quarterly or less. Ask about refresh cadence, and verify it through testing.

How to Test Provider Accuracy Yourself

Don't rely on vendor demos or case studies. Run your own tests using contacts you can independently verify.

The Known-Good Sample Test

Start with contacts you already know are accurate - current customers, recent closed deals, people you've spoken with personally. Strip out everything except name and company, then run it through the provider's enrichment.

Compare what comes back against what you know to be true. Did they get the email right? The phone number? The job title? The company details?

This test reveals real accuracy on verifiable data. If a provider can't accurately enrich contacts you've already confirmed, they won't do better on unknowns.

The Email Verification Test

Take a sample of enriched data (500-1,000 records minimum for statistical validity) and run an actual email campaign. Measure the percentage that says “undeliverable”.

Good general benchmark targets are:

• Under 1% hard bounces = excellent data quality
• 1-2% = acceptable
• 2-5% = concerning
• Over 5% = seriously problematic

Anything above 5% will damage your sender reputation and should disqualify a provider from consideration.

The Phone Connect Test

For providers claiming phone number accuracy, there's no substitute for actually calling.

Sample 50-100 numbers from enriched records. Track three outcomes: connected to the right person, connected to wrong person/company, or invalid number. Calculate the percentage that reach the intended contact.

This test is time-intensive but reveals quality that verification tools can't. A number might be "valid" (it rings) while connecting to someone who left the company two years ago.

The LinkedIn Cross-Reference

For job titles and company affiliations, manually spot-check against LinkedIn profiles. This takes 30 seconds per contact and catches obvious errors - people listed at companies they left, titles that don't match current roles, individuals who've moved to completely different industries.

Check at least 50 records across a representative sample of your target segments. If more than 10% show obvious discrepancies, the data isn't reliable enough for personalized outreach.

Measuring What Matters: Key Accuracy Metrics

Track these metrics when evaluating any data quality verification process:

Email Metrics

Hard bounce rate is your primary indicator. Measure it on actual campaigns, not verification tool outputs. Many verification tools can't check catch-all domains (20-30% of B2B domains), so their "verified" status is incomplete.

Reply rate as secondary validation. Higher quality data enables better personalization, which drives higher replies. Compare reply rates on enriched versus non-enriched outreach to isolate the data's impact.

Phone Metrics

Connect rate measures successful conversations per dial attempt. Industry average hovers around 3-5%. Quality direct dial data should push you toward 8-12%. If your connect rate doesn't improve meaningfully after enrichment, the phone data isn't adding value.

Right person rate tracks whether you reached the intended contact, not just any human. A 10% connect rate means nothing if 6% connected to the wrong person.

Firmographic Metrics

Field accuracy rate per attribute. Company size might be 95% accurate while revenue data is only 60% accurate. Measure each field you rely on for targeting or segmentation.

Currency of data, when was each field last validated? Firmographic data changes more slowly than contact data, but still requires periodic refresh. Mergers, acquisitions, and company pivots change details faster than many realize.

Why Waterfall Enrichment Improves Accuracy

Single-source enrichment has inherent accuracy ceilings. Every provider has blind spots, regions they cover poorly, company sizes with thin data, industries outside their focus.

Waterfall enrichment queries multiple providers in sequence until finding results, then cross-validates between sources. This approach improves accuracy in two ways.

First, coverage expands dramatically. Where one provider hits 60% of your targets, a waterfall across multiple sources might reach 85-95%. More coverage means more opportunities to verify data.

Second, consensus validation becomes possible. When three providers agree on a phone number, confidence increases. When they disagree, you know to flag that record for review rather than trusting any single source blindly.

Platforms like Databar aggregate 90+ data providers into a sigle system. IInstead of testing and managing multiple vendor relationships, you define the data you need and can query sources in an optimized sequence - starting with cheaper providers and escalating to premium sources only when necessary.

This approach solves the accuracy testing problem at scale. Rather than evaluating dozens of individual providers, you evaluate outcomes from an aggregated system that's already optimized for coverage and accuracy.

Red Flags When Evaluating Providers

Watch for these warning signs during your evaluation:

Vague accuracy claims without methodology explanation. If they can't tell you exactly how they measure accuracy, the number is marketing, not reality.

No free trial or testing option. Confident providers let you verify quality before committing. Reluctance to provide test access suggests they know what you'd find.

Annual contracts only. Data quality changes over time. Providers pushing long-term commitments may be locking you in before you discover degradation.

Accuracy guarantees with fine print. Read the terms carefully. "95% accuracy guaranteed" might mean "95% of emails pass syntax validation," not "95% actually deliver."

No refresh transparency. If they won't disclose how often they update data or when records were last verified, assume the worst.

Credit policies that punish testing. Some providers charge for records even when enrichment fails. Others offer credits back for bounced emails. The latter demonstrates confidence in their data.

Building Your Evaluation Framework

Structure your provider assessment around these questions:

What accuracy metrics do they report, and how do they measure them? Get specific methodology, not marketing numbers.

What's their coverage for your specific target market? Test with your actual ICP, not generic samples.

How do they handle data freshness? Continuous refresh, quarterly updates, or something else?

What happens when data is wrong? Credit policies, replacement records, or tough luck?

Can you run your own accuracy tests before committing? Free trial, sample enrichment, or paid pilot?

How do they source data? First-party collection, third-party aggregation, user-contributed, or purchased? Each has different accuracy implications.

What compliance certifications do they hold? GDPR, SOC 2, and similar certifications indicate data handling quality beyond accuracy.

Document your findings systematically. The provider with the most impressive demo isn't necessarily the one who'll deliver accurate data for your specific use case.

FAQ

What is considered good data enrichment accuracy?

Industry standards suggest 97%+ accuracy represents high-quality B2B data, though many providers actually deliver around 50% on average. For practical purposes, target email bounce rates under 1%, phone connect rates above 8%, and job title accuracy above 90% on spot-check verification. Test against your specific market - accuracy varies significantly by region and company size.

How do I measure accuracy of enriched data?

Run three tests: the known-good sample test (enrich contacts you've already verified and compare results), the campaign bounce test (send actual emails and measure hard bounce rate), and the LinkedIn cross-reference (manually spot-check job titles and company affiliations). Combined, these reveal real-world accuracy that vendor claims often obscure.

What's the difference between match rate and accuracy?

Match rate measures whether a provider returns any data for your input. Accuracy measures whether that returned data is correct. A provider might achieve 95% match rate while only delivering 70% accuracy, meaning they return data for 95% of records, but 30% of that data is wrong. Always prioritize accuracy over match rate.

How often should enriched data be re-verified?

B2B data decays at roughly 2% per month, meaning about 25% becomes outdated annually. For active campaign targets, re-enrich monthly. For broader databases, quarterly refresh catches most changes. Watch bounce rates and connect rates - when they start climbing, that's your signal to refresh sooner.

What accuracy guarantees should I expect from providers?

Look for specific, measurable guarantees with clear remediation. Credit-back policies for bounced emails demonstrate confidence. Vague "95% accuracy" claims without methodology explanation are marketing, not commitments. The best providers offer free trials or paid pilots so you can verify accuracy before committing budget.