What an aggregate score hides, when a composite can still be useful, and how to build one responsibly.
One number can hide opposite problems
A score such as 74 out of 100 might combine crawlability, citation, mention, position, sentiment, traffic, structure, and authority. The convenience is obvious; the mechanism is not.
Two brands can share the same average while having opposite profiles. One may be retrieved frequently but rarely used. Another may be difficult to retrieve yet highly influential once included. Their strategies should not be the same.
Weights contain strategic value judgments
Martinez argues that a scalar score is defensible only when its weights correspond to an explicit objective. Combining a mention, accurate citation, and conversion without a utility model simply hides organizational priorities inside arithmetic.
Weights are strategic choices, not natural laws.
A composite is useful when its purpose is explicit
A summary score can support executive communication when component metrics are normalized appropriately, weights reflect real priorities, users can inspect the underlying dimensions, and the result is not presented as universal science.
An awareness model might emphasize mention rate, prominence, and share of voice. A commerce model might emphasize referral and conversion. Both can be reasonable while producing different scores for the same brand.
Questions about this topic
Is every composite GEO score invalid?+
No. A composite can be useful when it has a defined objective, transparent weights, appropriate normalization, and inspectable component metrics.
Why can two brands with the same score need different strategies?+
Their underlying profiles may differ: one can have an upstream discovery problem while the other has a downstream answer-participation problem.
What should always accompany a GEO score?+
The component metrics, weights, objective, denominators, and measurement conditions should remain visible.
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
- 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