About Validator Analytics
The Validator Analytics page provides insights into the performance of Validators. These analytics help Validator creators monitor effectiveness, identify friction points, and optimize validation experiences across the platform.
To access it navigate to Content Analytics > Validator Analytics.You’ll find a breakdown of overall performance as well as the ability to drill down into specific validator behavior.
Metrics
| Success Rate | The percentage of sessions where a validation attempt ended in a valid input. Formula: % (# of sessions with successful validation)/(# of sessions with validation) * 100 | |||
| Success rate breakdown |
Correct at First Attempt: Inputs that passed validation on the first try. Corrected: Initially invalid inputs that were fixed and then accepted. Invalid: Inputs that remained invalid and were never corrected. | |||
| Views
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How often the validator was triggered
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| Unique users
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Number of distinct users who saw the validator
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| Success rate over time | Line chart that success rate fluctuations over time, helping you identify trends, regressions, or improvements in validator performance.
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Best Practices
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Investigate Low Success Rates: Validators with low success or high invalid rates may have unclear instructions, overly strict rules, or UI issues.
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Monitor High Attempt Counts: Validators with high “Avg of attempts to correct” suggest users struggle to get it right. Consider reviewing the pattern, tooltip, or input format hints.
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Use Time Filters to correlate validator performance with product changes, onboarding steps, or feature launches.