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How quality scores help you assess data reliability.

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Overview

Quality scores are available on person contact data (phones, emails, locations, socials). Each data point receives a confidence score that helps you understand data reliability and filter based on your use case.
Company data does not include per-field quality scores. Company records have a record-level num_sources count instead. See Company Data Overview for details.

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Quality Score Levels

ScoreDescription
HighHigh quality data with strong verification
ModerateModerate quality data with verification
LowLower quality data with limited verification

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How Scores Are Assigned

We evaluate multiple factors to determine quality scores. The specific methodology is confidential, but scores reflect our assessment of data reliability.

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Using Quality Scores

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Filtering Strategies

High-Stakes Operations (Sales outreach, compliance):
  • Filter for confidence: high
  • Ensures maximum accuracy for critical use cases
Full Coverage (Research, analytics):
  • Include moderate confidence data
  • Balances coverage with quality
Exploratory Analysis:
  • All confidence levels may be useful
  • Use scores to weight results, not filter

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Example: Phone Quality

{
  "type": "mobile",
  "number": "+15551234567",
  "current": true,
  "last_seen": "2026-01-15",
  "num_sources": 3,
  "confidence": "high"
}
This phone has:
  • High quality score
  • Marked as current (recommended contact method)
This is the most reliable data you can get.

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Example: Email Quality

{
  "address": "jane.doe@company.com",
  "type": "professional",
  "current": true,
  "last_seen": "2026-01-20",
  "validated": true,
  "validation_status": "valid",
  "num_sources": 2,
  "confidence": "high",
  "hash_sha256": "a1b2c3d4e5f6...",
  "hash_sha1": "a1b2c3d4e5f6...",
  "hash_md5": "a1b2c3d4e5f6..."
}
This email has:
  • Validated (deliverability confirmed)
  • Marked as current (recommended contact method)
  • High confidence score

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Common Questions

It means we’ve assessed this data point as high quality based on multiple signals. It’s a measure of our confidence in the data, not a guarantee of accuracy.
We evaluate multiple factors to determine quality scores. Some data points may receive moderate scores based on various quality indicators.
Our scoring is continuously refined based on data quality metrics. Scores reflect our best assessment of data reliability.