Generative Engine Optimization and Traditional SEO: A Comparative Framework for Search Visibility, Citation Presence, and AI-Generated Answers

Abstract

Generative search systems increasingly provide synthesized answers supported by selected web sources, creating forms of visibility that cannot be measured solely through conventional rankings and organic clicks. This working paper develops a comparative framework for evaluating traditional Search Engine Optimization (SEO) and Generative Engine Optimization (GEO) without treating them as unrelated or competing disciplines. The study examines the shared foundations of technical accessibility, index eligibility, content quality, relevance, and source reliability, while identifying measurement dimensions specific to generative search environments. These dimensions include citation presence, cited-page diversity, answer-level representation, grounding-query coverage, source attribution, and referral visibility. The proposed SCOPE Framework evaluates five areas: Search Eligibility, Content Evidence, Output Presence, Platform Accessibility, and Evaluation Metrics. It distinguishes observable citation activity from unsupported claims of authority, recommendation, or inclusion in proprietary AI training data. The framework is intended for reproducible auditing across traditional search results, Google’s generative search features, ChatGPT Search, Microsoft Copilot, and comparable answer engines.