From SEO to AEO: how ChatGPT shopping is transforming marketing in 2025

Abstract

This article explores the transformational processes taking place in digital marketing under the influence of generative artificial intelligence, with particular focus on the shopping function integrated into ChatGPT. The author highlights a fundamental shift in the architecture of search — from linear result listings to integrated answers generated by large language models (LLMs) directly in natural language. The study argues that classical SEO and content marketing tools are losing effectiveness in this new context, creating the need for a new strategic discipline — Answer Engine Optimization (AEO).

Based on an analysis of how LLMs function, the logic of generative search, and current practices of GPT assistant usage, the article proposes an updated, author-defined concept of AEO. It is framed as a strategic optimization of content, structure, reputation, and brand data to ensure algorithmic inclusion in LLM-generated answers. Unlike traditional SEO, which focuses on ranking in search results, AEO aims at being selected by AI as part of a generated response — a process that cannot be influenced through paid placements or advertising manipulation.

The article presents a systematized framework of ten core AEO principles, relevant in the context of GPT-powered shopping and LLM-based search. These include technical dimensions (such as crawlability, semantic markup, and data accuracy) as well as behavioral dimensions (such as reputational visibility, conversational relevance, and multilingual adaptability). The proposed AEO model is grounded in a deep analysis of shifts in the customer journey, the declining role of traditional clicks, the growing trust in AI-driven recommendations, and the compression of the sales funnel due to conversational interaction with AI systems.

The practical value of the study lies in forming an up-to-date toolkit for brands, marketers, digital strategists, and commercial content creators. Special attention is given to the challenges and opportunities for small and niche businesses, which can gain algorithmic visibility through quality and relevance rather than advertising budgets.

The findings hold theoretical relevance for future research in the fields of digital marketing, AI-mediated communication, and behavioral economics, as well as practical relevance for brands seeking to adapt to the emerging paradigm of hybrid information consumption. The article may also serve as a reference for academic courses in digital communications, AI marketing, UX design, and strategic brand management.