Why competitors, search-off responses, null outputs, and system failures must be modeled explicitly instead of hidden in denominators.
Maintain known and observed competitor panels
Start with direct, category-leading, regional, emerging, and substitute competitors. Also capture entities introduced by engines. Retain observed competitors that recur or resolve to real domains, and exclude unverifiable one-off names from Share-of-Voice denominators.
Search activation is an upstream outcome
An engine may answer from internal knowledge without web retrieval. No citation can therefore mean search was off rather than relevant sources were absent. Store search_activated as true, false, or unknown.
Citation probability can be decomposed into search activation, retrieval conditional on search, and citation conditional on retrieval.
System failure is not zero visibility
Distinguish success, search-off, no mention, no citation, API error, timeout, blocked response, and parse error. A timeout means no valid answer was observed; coding it as Mention = 0 creates a false business decline.
Preserve null and no-citation outcomes
Discarding search-off or no-citation responses inflates conditional visibility. Reports must identify whether denominators include all runs, successful responses, search-enabled responses, or answers containing citations.
Every missing, null, and failed state needs an explicit meaning.
Questions about this topic
Why keep observed competitors?+
Engines may repeatedly surface real competitors the original business panel overlooked.
What is search activation?+
Whether a generative system invokes web retrieval before constructing the answer.
Should an API timeout count as no mention?+
No. The engine produced no valid observation, so the run should carry an explicit failure or missing status.
Why preserve no-citation responses?+
They are part of end-to-end visibility and prevent conditional citation metrics from appearing artificially high.
References
Sources are listed in APA 7 style. Preprints are identified as such and should not be treated as peer-reviewed findings unless separately published.
- Kumar, P. (2026). Generative engine optimization at scale: Measuring brand visibility across AI search engines [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2606.20065
- Martinez, O. (2026). Optimizing visibility in generative engines: A critical survey of generative engine optimization (2023–2026) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2607.14035
- Zhang, K., He, X., & Yao, J. (2026). From citation selection to citation absorption: A measurement framework for generative engine optimization across AI search platforms [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2604.25707