Analysis of Generative Engine Optimisation (Geo) Strategies in Public Relations: Evaluating the Impact of Brand Media Relations on AI Search Visibility and Source Citation Probability

Abstract

This study evaluated the impact of public relations media relations strategies on brand visibility and source citation probability within generative artificial intelligence search engines. Anchored in Information Foraging Theory, the investigation examined how algorithmic retrieval mechanisms navigate information patches using semantic cues. A quantitative content analysis design was executed across thirty standardized industry search queries tested across OpenAI ChatGPT Search, Perplexity AI, and Google Gemini. The target population comprised all cited digital web sources, yielding a sample size of 285 total cited links (N=285) and 101 unique brand mentions selected via systematic experimental audit sampling. Data were analyzed using frequency distributions, percentage shares, and mean query metrics across defined structural and domain authority variables. Major findings revealed that structured Generative Engine Optimization elements accounted for 86.7 percent of cited media, with direct numerical data leading at 37.8 percent, whereas unstructured text comprised only 13.3 percent. Sourcing volume varied significantly across engines, led by Perplexity AI with 4.20 citations per query, while high-authority earned media (Domain Authority 80 to 100) generated 56.8 percent of all citations. The study concluded that generative search engines systematically prioritize third-party earned media structured for rapid factual extraction over self-published corporate content. It is recommended that public relations practitioners re-architect press materials into modular, structured layouts with verified statistical metrics to maximise algorithmic citation selection.