Selection Rate

Selection Rate is a ai visibility metric used in AI SEO. How often a model selects a brand/source from the candidate set it could use when generating an answer. The term or framework is attributed to Dan Petrovic / DEJAN and is documented in 2025-2026.^[1]

AI SEO Wiki records the attribution as Explicit creator concept, with a confidence rating of High. This rating concerns the documentary evidence for the term's origin; it does not imply that the underlying idea is unique to one practitioner or that the terminology has become an industry standard.^[2]

Definition

How often a model selects a brand/source from the candidate set it could use when generating an answer. In practical usage, the concept is most useful when its measurement unit, observation window, source set, or decision context is stated explicitly. This is particularly important in AI-mediated search, where answers can vary between models, prompts, retrieval systems, and repeated runs.

Origin and attribution

Selection Rate is attributed in the available documentary record to Dan Petrovic / DEJAN. The registry dates the term to 2025-2026 and classifies the evidence as Explicit creator concept.^[1]

Machine analogue of CTR in Petrovic's framing. This qualification is retained because AI-search terminology often spreads rapidly through conference talks, social posts, vendor material, and practitioner communities before a stable publication record exists.

An additional source provides independent or supporting evidence for the terminology or attribution.^[2]

Role in AI SEO

Selection Rate belongs to the measurement layer of AI SEO. Conventional ranking reports do not fully describe systems that can mention a brand without linking to it, cite one source while using another, or change their answer across repeated generations. The concept therefore treats AI visibility as a time series or repeated-observation problem rather than a single fixed ranking.

It is best interpreted alongside other measures rather than as a standalone score. Related diagnostics include Selection Rate, Citation RSI, and Visibility Drawdown.

Application and interpretation

Application normally begins with a defined prompt set, model or engine, observation cadence, and counting rule. A practitioner can then compare Selection Rate across time, categories, competitors, or source types. Changes should be interpreted against an entity's own baseline and validated with underlying citations, mentions, retrieval evidence, or prompt-level outputs.

Because AI outputs are stochastic, a single run is rarely sufficient evidence of a trend. Repeated measurements and a documented sampling method reduce the risk of treating random answer variation as a meaningful visibility change.

Related concepts

Limitations and attribution notes

The attribution evidence for Selection Rate is comparatively strong, but the existence of a documented term origin does not establish that every underlying mechanism was first discovered by the named creator. Similar ideas can arise independently under different terminology.

The field itself changes rapidly. Model behaviour, retrieval systems, citation interfaces, and measurement tooling can change without the terminology changing, so operational claims should be re-tested against current systems.

See also

• Selection Rate Optimization
• Primary Bias
• Generative Engine Optimization
• Micro-AEO Ranking
• AI SEO

References