Selection Rate Optimization

Selection Rate Optimization (SRO) is a optimization discipline used in AI SEO. Optimizing the probability that an AI model selects a brand/source from its candidate set. The term or framework is attributed to Dan Petrovic / DEJAN and is documented in 2026.^[1]

AI SEO Wiki records the attribution as Explicit team coinage, 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

Optimizing the probability that an AI model selects a brand/source from its candidate set. 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 Optimization is attributed in the available documentary record to Dan Petrovic / DEJAN. The registry dates the term to 2026 and classifies the evidence as Explicit team coinage.^[1]

DEJAN describes SRO as a new discipline coined by DEJAN. 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

As an optimization discipline, Selection Rate Optimization describes a particular layer of the process by which information becomes visible in AI-mediated discovery. It sits alongside Answer Engine Optimization, Generative Engine Optimization, and Search Everywhere Optimization rather than necessarily replacing conventional search engine optimization.

The practical emphasis is on improving the probability that the right entity, source, claim, or recommendation survives the sequence from discovery and retrieval through selection, synthesis, and presentation to the user.

Application and interpretation

In implementation, Selection Rate Optimization can involve changes to first-party content, technical accessibility, entity information, third-party corroboration, source authority, distribution, and measurement. Which interventions matter depends on whether the target system relies on web search, retrieval-augmented generation, model memory, structured data, or a mixture of these mechanisms.

For this reason, optimization claims should specify the engine tested and the outcome being targeted—such as citation, mention, selection, recommendation, or referral traffic.

Related concepts

Limitations and attribution notes

The attribution evidence for Selection Rate Optimization 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
• Primary Bias
• Generative Engine Optimization
• Answer Engine Optimization
• AI SEO

References