我是 Onsing，My Skill Set: B2B Growth | China Market Entry & GEO (AI Search) | Content Marketing
Generic GEO methodologies — built around user prompts as demand input, visibility and SOV as core metrics, and content distribution as deliverables — fit B2C exposure but map poorly to vertical industries. In pharmaceuticals, industrial goods, finance, and law, demand is better read from market data than from lagging prompts, and competition happens on the intermediate "semantic bridges" AI must traverse rather than at the endpoint of recommendation. Vertical GEO therefore requires different deliverables (authoritative-content restructuring, bridge coverage), different evaluation standards (accuracy, authority, scenario match over mention frequency), and a different supply chain (domain expertise plus AI-visibility technology, not a transplant of generic GEO skills).
This study investigates laptop brand visibility across Baidu AI, DeepSeek, and Doubao. It finds that Lenovo and ASUS dominate visibility, while other brands perform variably. Platforms rely on different sources, such as Toutiao for Doubao and tech media for DeepSeek. The research highlights that GEO strategies must focus on content structure, source distribution, and aligning with user intent to improve visibility and ranking.
This article discusses the importance of prompt design in AI search analysis, noting that keyword-based approaches are often incomplete. The author proposes a semantic network framework consisting of four layers: Contextual Framework, Subject Attributes, Solutions & Actions, and Value Stack. By combining these layers with specific prompt templates—such as scenario-driven or problem-solving structures—businesses can more accurately capture user intent and monitor brand visibility. This method is crucial for analyzing AI responses and optimizing strategies in the Chinese market.