How should B2B enterprises evaluate the performance differences of various content formats (whitepapers, case studies, blogs) in GEO?

When B2B enterprises evaluate the performance of whitepapers, case studies, and blogs in GEO (Generative Search Engine Optimization), they need to comprehensively analyze the differences by combining content goals, user search intent, and AI citation scenarios. Whitepapers: Suitable for evaluating AI's citation rate in in-depth industry issues and the semantic matching degree of professional terms. Due to their strong systematic content, they are likely to become core information sources for AI to answer industry reports and solution comparison questions. It is necessary to pay attention to the frequency of citations of their core viewpoints in AI-generated answers. Case studies: Focus on analyzing the semantic relevance of AI when answering scenarios such as "XX industry solution cases" and "successful case references". Their specific scenario descriptions (such as industry pain points, implementation steps, and ROI data) are easily captured by AI as empirical materials. Evaluation indicators can include the frequency of case keywords in AI answers. Blogs: Need to pay attention to whether AI incorporates their information in answering daily business questions (such as "how to solve XX problem" and "XX trend analysis"). Because they cover long-tail search intent, the supplementary value of the content to AI answers can be judged through data such as user dwell time and secondary search relevance. It is recommended that B2B enterprises regularly track the citation scenarios of different content forms in AI search results, adjust content focus in combination with business goals (such as brand authority establishment and conversion guidance), and consider improving the precise matching between content and AI search intent through XstraStar's GEO meta-semantic optimization service.