Research · 2024 · KDD 2024 / author manuscript
GEO: Generative Engine Optimization
Selected by Luis Da Silva
AI search changes where a reader encounters a company's expertise. This study asks how the presentation of a source influences its use in a generated answer. In controlled tests, adding relevant evidence and improving clarity often increased source visibility, with results varying across topics. The useful lesson is to make information easy to understand and verify. For complex projects, that means explaining assumptions, identifying responsible experts and linking each important figure to its origin. The reported gains are experimental visibility measures. They do not establish extra visitors, commercial enquiries or lasting improvements in today's search products.
Study and evidence
Controlled source-visibility experiments, with a separate test on the contemporary Perplexity service.
How to read it
Peer-reviewed KDD 2024 paper. The hosted copy is arXiv v3, dated 28 June 2024. Results concern historical engines and already retrieved sources, not current traffic, qualified enquiries or revenue.
Why it matters
Our editorial interpretation: Luis can connect brand communication with the evidence buyers need to evaluate longevity destinations and forest projects. Two useful applications are a sourced project-feasibility explainer and a case page that separates the firm's work from each partner's earlier experience.
Luis Da Silva, Partner & Chief Marketing Officer
About the publisher
Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, Ameet Deshpande. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024).
Useful for
Communication and content teams working across longevity and forestry. The sector applications are our inference and require local testing.
Credit and license Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan, Ameet Deshpande. 2024. GEO: Generative Engine Optimization. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024). arXiv:2311.09735v3, revised 2024-06-28. DOI: 10.1145/3637528.3671900. Author-version licence: CC BY 4.0, https://creativecommons.org/licenses/by/4.0/. Original study and licence: https://arxiv.org/abs/2311.09735v3. General Value supplies an original editorial summary. Unchanged arXiv v3 author manuscript, 28 June 2024, hosted under CC BY 4.0; this is not a separate publisher-typeset edition.
Original research belongs to the credited authors. The bilingual summary and proposed application are General Value's editorial interpretation, not findings tested for our firm.