Guotai Haitong Securities released a research report stating that, unlike traditional SEO (Search Engine Optimization) which focuses on keywords and rankings, GEO (Generative Engine Optimization) centers on whether brand content can be understood and recommended by large AI models. This approach boasts higher marketing efficiency compared to traditional methods and is expected to shorten the consumer decision-making chain and cycle. The report is optimistic that the more efficient GEO will replace part of the traditional SEO market, presenting vast long-term potential. Driven by new generative AI traffic entry points and enhanced marketing efficiency, the firm views the GEO transformation favorably. E-commerce operators are expected to be the first to grasp this shift, leveraging their understanding of platforms and content marketing to actively meet brand marketing demands. In the long run, the brand side may face a marketing transformation, with proactively adapting brands likely to succeed. Guotai Haitong's main views are as follows:
The proliferation of AI recommendations is expected to usher marketing into a new GEO era. GEO refers to optimizing content so that AI can quickly extract key information and prioritize referencing brand content when generating answers. As large AI models become increasingly widespread, recommendations from generative AI are anticipated to become a significant factor in consumer decision-making, fostering new marketing scenarios and methods. Unlike traditional SEO, GEO focuses on whether brand content can be comprehended and recommended by AI models, offering higher marketing efficiency and the potential to shorten consumer decision paths. The firm believes the more efficient GEO will capture part of the traditional SEO market, with substantial long-term growth space. Factors such as venture capital funding in overseas primary markets, recent collaborations like Walmart-Google, and Elon Musk's open-sourcing of the X platform's recommendation algorithm provide multi-faceted support, potentially accelerating GEO's development.
The content and information sources preferred by generative AI differ, requiring GEO to be more specialized. The essence of GEO is to be recognized and recommended by AI. Unlike traditional marketing targeting consumers, AI prefers "more rational" content, prioritizing authoritative sources; content that is highly professional, structured, and logical is more easily recognized by AI. Furthermore, AI-generated results are derived from reasoning across multiple information channels, with different models and product categories relying on varied source media. Consequently, GEO requires separate optimization regarding the authority and coverage of information sources, as well as the expression and format of content, thereby creating corresponding marketing optimization demands.
The rise of GEO fundamentally alters traffic distribution methods, potentially benefiting the operations outsourcing segment, while brands may undergo a marketing transformation in the long term. The report identifies five types of companies likely to benefit from the GEO shift: 1) AI platform companies, becoming new traffic entry points; 2) SaaS companies, providing software operation services and optimization based on new rules to enhance traffic and efficiency; 3) Marketing service companies, offering targeted marketing services aligned with new traffic distribution methods; 4) E-commerce operators, assisting brands in maximizing sales by adapting to new traffic distribution patterns; 5) Brand companies, leveraging new traffic distribution methods to achieve a breakthrough. Among these, e-commerce operators are highly responsive to channel and traffic changes, with leading companies possessing AI system capabilities, positioning them to benefit first. In the era of AI recommendations, brand marketing strategies require optimization, and brands that transition early are expected to benefit subsequently.
Risk warnings include slower-than-expected commercialization by major model providers, low willingness for brand investment, and policy regulatory risks.
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