How Accurate Is People Data Labs? Real Tests vs 3 Alternatives

The Gap Between "98% Accurate" and What You'll Actually Get

People Data Labs markets 98% data accuracy. That number is real, but it measures something different from what most buyers assume. PDL's 98% accuracy refers to structural validity — the email address is correctly formatted, the phone number has the right number of digits for the country code. It doesn't mean 98% of those numbers will reach the right person when you call them.

Real-world accuracy, defined as "the contact data reaches the correct person," is consistently lower. Based on aggregated user reports and publicly documented tests, you can expect:

• Email deliverability: 70–85% (emails don't bounce)
• Email accuracy: 60–75% (emails reach the intended person)
• Phone number validity: 65–80% (number is still active)
• Phone number accuracy: 55–70% (number reaches the intended person)

Those are still useful numbers. An 80% email deliverability rate on a 1,000-contact list means 800 emails land in inboxes. A 70% phone accuracy rate means 700 of your 1,000 contacts are reachable by phone. The issue arises when teams build sequencing strategy assuming 98% accuracy and don't budget for the 15–30% that won't pan out.

What Causes the Accuracy Gap?

The gap between claimed and real-world accuracy comes from three structural issues:

Data staleness. PDL aggregates data from many sources, but those sources don't update in real time. A contact who changed jobs six months ago may still have their old employer's email in PDL's dataset. The data was accurate when it was captured; it's no longer accurate today. PDL refreshes data regularly, but the refresh cadence can't keep pace with constant job changes across a dataset of billions of records.

Profile ambiguity. Common names, people with multiple email addresses, and profiles without enough disambiguating signals can result in wrong-person matches. PDL resolves these with confidence scores, but borderline matches still make it through.

Coverage vs. accuracy trade-off. PDL errs on the side of coverage (returning a result) rather than conservatism (returning nothing when uncertain). This means you get higher match rates but also more entries that need verification.

None of these are reasons to avoid PDL. They're reasons to build email validation and phone verification into your workflow downstream of enrichment.

PDL vs. Clearbit: Head-to-Head

Clearbit, now integrated into HubSpot's data ecosystem, excels at company-level data. If you need technographics (what software a company uses), funding rounds, headcount ranges, and industry classification, Clearbit's company API is strong. For individual contact enrichment — direct dial, personal email — PDL has broader coverage.

In independent comparisons on the same 500-contact test list, PDL typically returns phone numbers for 35–45% of contacts. Clearbit returns phone numbers for 20–30% of the same contacts. For email, the gap is smaller: PDL at 65–75% match rate vs. Clearbit at 55–65%.

The practical implication: if you're enriching accounts for ABM and need to understand the company well, Clearbit adds value. If you're building a direct outreach list and need contact-level data, PDL wins on coverage.

PDL vs. ZoomInfo: Enterprise vs. Developer-Friendly

ZoomInfo is the incumbent in enterprise sales data. Its coverage of enterprise accounts and F500 executives is genuinely excellent. It also has community-contributed data (from ZoomInfo's browser extension) that keeps contact data fresher than pure aggregation approaches.

The trade-offs: ZoomInfo is expensive, with annual contracts often starting at $15,000+ for team plans. The API is designed for CRM integration, not flexible developer use. And ZoomInfo's community data model raises its own concerns about how contacts ended up in the database.

PDL offers a developer-friendly REST API with clear pricing, broad coverage, and no minimum contract. For teams building custom enrichment pipelines, PDL's API is significantly easier to work with. For sales teams who want a turnkey prospecting platform, ZoomInfo's full product suite is more polished.

PDL vs. Apollo: The Closest Comparison

Apollo.io uses PDL data as one of its sources, layered with its own enrichment and a community data model similar to ZoomInfo. If you're comparing them directly, Apollo wins on usability as a prospecting platform (built-in email sequencing, CRM sync, filters) while PDL wins on raw API flexibility and data licensing clarity.

Apollo's pricing is also significantly lower than ZoomInfo, making it a strong choice for teams that want a full prospecting platform rather than raw API access. The accuracy characteristics are broadly similar since they share underlying data.

Where Ziwa Fits Into This Landscape

Ziwa is built on PDL's API, which means it inherits PDL's coverage and accuracy characteristics. What Ziwa adds is a pay-per-result pricing model: you only pay credits when a phone number or email is actually found for a given profile. This makes Ziwa cost-effective for teams that enrich occasionally rather than at enterprise scale.

The other distinctive feature is social profile input. Where PDL's API typically takes name + company as input, Ziwa's tools accept Facebook profile URLs, Twitter/X handles, and LinkedIn profiles as input. This makes it easier to enrich contacts you found through social media research rather than from a database export.

For batch enrichment, Ziwa's Prospects tool handles up to 200 profiles per job with Excel export. If you're evaluating enrichment options, the pay-per-result model means you can run a real test on your actual prospect list without a subscription commitment — see pricing details here.