A practical guide to the seven dimensions that replace the idea of one universal GEO rank.
Replace one scalar with a visibility profile
Martinez represents source visibility as a vector: discoverability, context exposure, citation probability, observable prominence, source absorption, fidelity, and behavioral or economic outcome. The model forces analysts to define what “visibility” means before measuring it.
A publisher seeking referrals may emphasize behavior. A brand-awareness team may emphasize mention probability and prominence. A researcher studying source influence may focus on absorption. No theory requires every organization to optimize the same component.
Discoverability and context exposure create opportunity
Discoverability asks whether a source or entity is accessible, eligible, recognized, and relevant enough to enter retrieval. Context exposure asks whether it reaches the effective context, where it is positioned, and how many passages or tokens it receives.
These are upstream opportunity variables. A source excluded from top-k has almost no chance to shape a retrieval-grounded answer, while a source with substantial context can contribute definitions, comparisons, and evidence.
Citation, influence, accuracy, and behavior are different outcomes
Citation records explicit attribution. Prominence captures answer position, repetition, and attributed share. Absorption estimates whether the source contributes facts, language, or structure. Fidelity checks whether the answer accurately represents what the source supports. Behavior records clicks, referrals, leads, purchases, or value.
The vector is useful because a strong value in one dimension does not imply strength in the others.
Questions about this topic
What dimensions belong in the GEO visibility vector?+
Discoverability, context exposure, citation or mention probability, prominence, absorption, fidelity, and behavioral or economic outcomes.
Does every organization need the same visibility objective?+
No. The relevant components depend on whether the goal is awareness, referral traffic, accurate attribution, recommendation, lead generation, or another outcome.
Why keep vector components separate?+
Separate dimensions reveal the failing mechanism and prevent a strong result at one stage from hiding weakness elsewhere.
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.
- 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
- Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., & Narasimhan, K. (2024). GEO: Generative engine optimization. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5–16). Association for Computing Machinery. https://doi.org/10.1145/3637528.3671900
- 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