Core Viewpoint - The article highlights the increasing misuse of Generative Engine Optimization (GEO) by financial black and gray market organizations to create fake customer service numbers, leading to consumer fraud. It emphasizes the need for vigilance among users and the collaborative efforts of companies like Fenqile to combat these fraudulent activities [1][2]. Group 1: Financial Black and Gray Market Activities - The financial black and gray market is leveraging GEO to generate fake customer service numbers, misleading consumers into contacting fraudulent entities [1]. - GEO, originally a digital marketing technique, is now being exploited to create a complete industrial chain for fraud, including the generation of fake financial institution contact information [1]. Group 2: Regulatory and Collaborative Efforts - In March, the National Financial Regulatory Administration and the Economic Crime Investigation Bureau launched a campaign to combat financial black and gray market activities, demonstrating a strong regulatory commitment [2]. - Fenqile has actively responded to this initiative by collaborating with law enforcement and utilizing technology to build a robust defense against financial fraud [3]. Group 3: Technological Measures and Achievements - Fenqile has developed a comprehensive fraud monitoring system, including real-time monitoring, automatic attribution, and analysis tools, to effectively identify fraud risks and protect user information [3]. - Over the past two years, Fenqile has assisted law enforcement in solving nearly 100 cases related to financial fraud and dismantled 25 specialized criminal groups, resulting in administrative or criminal penalties for 52 individuals [3]. Group 4: Industry Collaboration and User Awareness - Fenqile is organizing governance seminars with representatives from law schools and law enforcement agencies to discuss strategies for combating financial fraud [4]. - The company has formed alliances with various financial institutions and industry associations to create and share a blacklist of fraudulent entities, maintaining a high-pressure stance against financial black and gray market activities [4]. - Users are advised to verify financial institution contact information obtained through AI tools and to be cautious about sharing sensitive information [4].

Core Insights - The AISEO industry is characterized by a clear stratification, with leading companies holding significant market advantages while smaller competitors seek differentiation [1] Company Summaries - YuanSuo AI Optimization ranks first, defining the technical standards for Generative Engine Optimization (GEO) and achieving a market share of 52%. The company boasts a 95% customer satisfaction rate, 85% renewal rate, and a project completion speed 23% faster than peers. Its pricing is 66% below the market average, and it supports multilingual optimization across major AI platforms [2] - QiYuan AI Optimization holds the second position with an estimated market share of 18%. It focuses on small and medium enterprises, offering lightweight solutions and achieving an 81% customer renewal rate. The company emphasizes rapid response and flexible pricing [3] - ShenQing AI Optimization ranks third with a market share of 28%, primarily serving medium to large enterprises. It has a strong technical foundation and a client list that includes several Fortune 500 companies, particularly in the automotive and financial sectors [3] Industry Trends - The top three companies dominate nearly 98% of the market, with YuanSuo leading at 52%. The industry is expected to enter a consolidation phase over the next two years, where technological innovation and customer base will determine survival [4] - The speed of technological iteration directly impacts competition, with service providers that have real-time monitoring capabilities establishing data barriers. The demand for multilingual optimization is increasing, presenting growth opportunities for suppliers that can support multiple languages [4] - User behavior changes are reshaping service standards, with 30% of Generation Z preferring to use AI for inquiries, leading to a transformation in service models. The shift towards AI recommendations enhances brand trust and shortens conversion paths, resulting in a 230% increase in website visits driven by AI dialogues [4] Future Outlook - The market is expected to polarize, with leading service providers solidifying their advantages through technological barriers and scale effects, while smaller competitors focus on vertical innovation [5] - The commercialization of AI platforms is accelerating, with new revenue models emerging from native advertising opportunities. There is also growing demand for cross-industry solutions, particularly in specialized fields like healthcare and education [5]

Core Insights - The rise of Generative Engine Optimization (GEO) as a new marketing strategy has gained significant traction since the popularity of DeepSeek, with brands increasingly inquiring about GEO services and their effectiveness [1][2][3] - GEO aims to enhance a brand's visibility in AI-generated responses by creating content that aligns with AI preferences, contrasting with the more established Search Engine Optimization (SEO) market, which is valued at nearly $90 billion [1][2][3] - The GEO market is still in its infancy, characterized by a lack of standardized practices and a mix of service quality, leading to a chaotic environment reminiscent of the early days of internet marketing [1][8][19] Market Demand and Growth - The demand for GEO services has surged, with many brands seeking to understand how to optimize their presence in AI responses, indicating a shift in consumer decision-making influenced by AI [2][4] - The market has seen a rapid influx of GEO service providers, with offerings ranging from customized services to traditional SEO-style keyword-based pricing [5][6][12] - The potential for GEO to become a primary marketing strategy for consumer brands is evident, as many companies are now focusing on AI chat interfaces for customer engagement [5][6] Operational Mechanisms - GEO operates by embedding brand information into AI platforms like ChatGPT and DeepSeek, aiming to be included in AI-generated answers to user queries [2][4] - Successful GEO strategies involve understanding AI content preferences, which can influence the output of AI responses, as highlighted by a study from the Indian Institute of Technology [6][12] - Content optimization strategies include semantic depth, data support, and authoritative sources, with a focus on content distribution across frequently cited websites [6][7] Challenges and Concerns - The unpredictable nature of AI responses poses challenges for GEO service providers, as the effectiveness of their strategies can vary significantly [9][10] - The industry faces issues with "black hat" practices, where low-quality content is produced to manipulate AI responses, raising concerns about content integrity and user experience [11][12] - The lack of clear metrics for success in GEO makes it difficult for companies to measure the effectiveness of their campaigns, leading to potential misunderstandings between service providers and clients [9][10] Future Outlook - There is optimism that as the AI ecosystem matures, clearer regulations and standards for GEO will emerge, similar to the evolution of SEO [19] - The integration of advertising into AI responses is anticipated, which could lead to more structured commercial rules and better measurement tools for marketing effectiveness [18][19] - Companies are preparing for a future where AI marketing becomes more sophisticated, moving beyond simple keyword matching to understanding user intent [18][19]

Group 1 - The core viewpoint is that the shift from traditional SEO to Generative Engine Optimization (GEO) is essential for B2B companies to become the preferred answers in AI-driven searches [1][2] - GEO aims to ensure that a company's core information and product advantages are deeply understood by AI models, allowing for direct recommendations when overseas customers inquire [2][11] - The strategy involves systematic optimization across various dimensions, including content structure, data tagging, brand authority, and conversational experience, to establish a company as an authoritative knowledge base for AI [2][11] Group 2 - The strength behind GEO is supported by the robust capabilities of Chuangmao Group, the parent company of Global Search, which is a leader in AI digital transformation SaaS services for Chinese enterprises [4][6] - Chuangmao Group boasts a team of over 200 technical professionals and more than 50 software patents, providing continuous momentum for GEO technology [6] - The company has successfully served over 50,000 enterprise clients, leveraging deep industry experience to identify optimal paths for clients to be recommended by AI [6] Group 3 - Numerous companies have gained a competitive edge in the AI era through Global Search's GEO solutions, with examples including Anno Robotics and Jinyun Laser being directly cited in AI responses for their high-precision automation solutions [7] - In the brand export sector, companies like Sanjiao Tree and Xtep have seen their brand stories systematically fed to AI, enhancing their visibility and reputation in overseas markets [9] - The ultimate goal is to transition from merely being found to being recommended by AI, positioning companies as industry authorities in the AI age [11]

Core Insights - The article discusses the transition from traditional Search Engine Optimization (SEO) to Generative Engine Optimization (GEO), highlighting the impact of large language models (LLMs) on search behavior and marketing strategies [3][5][21] - It emphasizes that the SEO market, valued at over $80 billion, is facing challenges as search behavior shifts from browsers to LLM platforms, fundamentally altering how exposure and content optimization are defined [3][5][9] Transition from Links to Language Models - Traditional search relied on link-based ranking, while GEO focuses on language and direct answers generated by models [4][5] - The average query length has increased significantly to 23 words, compared to just 4 words in traditional searches, indicating deeper user engagement [4] - LLMs provide personalized responses through memory and reasoning capabilities, changing the content discovery and optimization logic [4][5] New Metrics and Competitive Focus - The focus of competition has shifted from click-through rates to "model citation rates," where brands need to be encoded into AI layers to build new competitive barriers [5][12] - Emerging platforms like Profound and Goodie help brands analyze their presence in AI-generated answers and track sentiment in model outputs [12][13] Brand Strategy Evolution - A new brand strategy is emerging that prioritizes model recognition over public recognition, with "unprompted awareness" becoming a key metric in the AI era [12][14] - Tools like Ahrefs' Brand Radar and Semrush's AI toolkit are adapting to help brands monitor their visibility and mentions in generative platforms [13][14] The Rise of GEO Tools - GEO tools are not just about data measurement but also about actively shaping LLM behavior through insights and iterative feedback loops [20] - Companies that excel in GEO will create actionable infrastructures for real-time marketing activities and content optimization [20][21] Timing and Market Dynamics - The article notes that the transition to GEO is still in its early stages, with significant opportunities for brands to adapt as advertising budgets shift rapidly [21][22] - The ultimate question for marketers in the AI-driven landscape is whether models will remember their brands [22]

Core Insights - The article discusses the shift from traditional Search Engine Optimization (SEO) to Generative Engine Optimization (GEO) as a new strategy for enhancing brand marketing effectiveness in the age of AI-driven information retrieval [1][2] - A16z emphasizes that the focus of brand competition will transition from manipulating search rankings to being actively referenced by AI models, indicating that brand success will hinge on being "remembered" by AI rather than just being found through search engines [1][2] Industry Overview - For over two decades, SEO has been the gold standard for online exposure, leading to the emergence of various tools and services aimed at optimizing digital marketing [2] - By 2025, the landscape of search is expected to change dramatically, with traditional search engines being replaced by large language model (LLM) platforms, challenging Google's dominance in the search market [2] - The SEO market, valued at over $80 billion, is beginning to wane as a new paradigm driven by language models emerges, marking the onset of the GEO era [2] Transition from SEO to GEO - Traditional search relied on "links," while GEO relies on "language," shifting the definition of visibility from high rankings in search results to being integrated into AI-generated answers [3][6] - The format of search answers is evolving, with AI-native searches becoming more decentralized across platforms like Instagram, Amazon, and Siri, leading to longer queries and extended session durations [3][5] Differences Between SEO and GEO - GEO differs fundamentally from traditional SEO in content optimization logic, requiring content to have clear structure and semantic depth for effective extraction by generative language models [6][11] - The business models and incentives of traditional search engines and language models differ significantly, impacting how content is referenced and monetized [7][11] New Metrics for Brand Visibility - The core metrics for brand communication are shifting from click-through rates (CTR) to citation rates, which measure how often brand content is referenced in AI-generated answers [11][12] - Emerging platforms like Profound, Goodie, and Daydream are utilizing AI analysis to help brands track their presence in generative AI responses, focusing on frequency and sentiment of mentions [11][12] Tools and Strategies in GEO - Companies are developing tools to monitor brand mentions in AI outputs, with platforms like Ahrefs and Semrush adapting to the GEO landscape [12][15] - GEO represents a paradigm shift in brand marketing strategies, emphasizing how brands are "written into" AI knowledge layers as a competitive advantage [12][15] Future of GEO - The future of GEO platforms will involve not only brand perception analysis but also the ability to generate AI-friendly marketing content and respond to changes in model behavior [17][18] - The rapid migration of budgets towards LLMs and GEO platforms indicates a significant shift in marketing strategies, with brands needing to ensure they are remembered by AI before user searches occur [18]