搭平台聚合力 业界构建AI营销良性生态

Core Insights - The article discusses the transformation in AI marketing from tool application to ecosystem building, emphasizing the need for scientific and fair evaluation standards to address challenges in effect quantification and industry trust [1][2] Group 1: AI Marketing Transformation - The development of generative large models has transitioned from quantitative to qualitative changes, moving from cost-cutting tools to deeply influencing consumer decision-making processes [1] - Brands' recognition status within large models is crucial for market competitiveness, highlighting the importance of establishing a quantifiable evaluation system [1] Group 2: iMeter Quantitative Evaluation System - The iMeter system launched by Beijing Zhiyingchi Technology aims to provide brands with a measurable framework for AI recognition, utilizing a "two ends and four channels" technical approach [2] - The system monitors six core indicators: Share of Voice (SOV), First Recommendation Rate (FRR), Top Ten Recommendation Rate (T10RR), Brand Display Rate (BDR), Brand Connection Score (BCS), and Volume Balance Index (VBI) [1] Group 3: Industry Challenges and Solutions - The current mobile AI user base in China has reached 729 million, with over 500 generative AI services registered, presenting both opportunities and challenges in AI marketing [3] - There is a recognized need for an independent and fair third-party evaluation system to facilitate the industrialization of artificial intelligence [3] Group 4: Practical Applications and Impact - A case study from a law firm illustrates the effectiveness of the iMeter evaluation system, which helped the firm transition from passive inquiries to being accurately recommended by large models, enhancing user trust [3] - The mid-term monitoring provided by the ARA system visually demonstrated improvements in brand metrics and competitive landscape, allowing clients to see tangible returns on investment [3] Group 5: Future Directions - The industry is moving towards ensuring the controllability and credibility of large models, advocating for a shift from "human participation" to "human governance" within defined boundaries [4] - Collaborative efforts across the industry are aimed at guiding AI marketing towards a sustainable development phase, contributing to a new digital economy future [4]