Missing Is Not Zero: A Data Quality Checklist for AI Visibility Tests
Your dashboard says a brand appeared in 25% of AI answers. Before interpreting that number, ask a less exciting question: what counted as an answer? If a test failed, timed out or never reached the intended question, recording a zero turns a collection problem into an apparent visibility problem. Here is a practical way to keep those outcomes separate. 1. Separate absence from missing information…
One aspect to consider adding to the scoring tests is the handling of ambiguous or conflicting brand mentions. For example, if multiple sources mention the brand but with varying levels of relevance or accuracy, how should the system score and report those instances? Should the highest relevance mention be prioritized, or should all mentions be evaluated equally? This scenario tests the system's ability to discern and differentiate between meaningful and misleading brand references.
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