[First Letter Analysis Survey Results] "Why are those DMs read?" Visualizing the 'Patterns' of Professional Sales Letters and Emails

Cognitee, Inc. (Headquarters: Shinagawa-ku, Tokyo; CEO: Ria Kono; hereinafter 'Cognitee'), which uses knowledge representation AI to visualize organizational issues from conversation and text data, has released its first structural analysis content for 'sales letters and sales emails' as part of its 'Kaitai Shinsho' (Dissection) video series. This installment features sales letters and emails by Tomoaki Kikuhara, Representative Director of Sales Support Consulting Co., Ltd. In this project, the practical knowledge of 'selling without visiting,' which has been supported in sales settings for many years, is visualized from the perspective of text structure and organized into concrete points that sales representatives can apply to their daily work.

Reasons why you feel 'I'm writing to be understood, but it's not leading to results' In sales activities, text such as emails, letters, and messages are important touchpoints, but many people face challenges such as 'not knowing if they are being read,' 'not feeling that the message is getting across,' or 'not building trust.' The cause lies not in 'what is written,' but in the fact that the structure—such as text length, flow, and tone—is not reproducible. Therefore, Cognitee has visualized and analyzed 'sales letters,' a skill that tends to be highly personalized, as a structure. ■ Video Information ・[Part 5] Sales Dissection (Sales Support Consulting Edition) https://youtu.be/9XMwgejJXT0 ・Kaitai Shinsho Series List https://www.youtube.com/playlist?list=PLkvIVIN1NyRkLjQ4t_GfFI1BBXz1UJJRI ■ First Sales Letter Analysis: Dissecting Text as a Structure The analysis uses 'CogStructure,' a knowledge representation AI uniquely developed by Cognitee. This technology was developed approximately 13 years ago and is based on a different approach than the generative AI (LLM) that has become widespread in recent years. By reading people's talk and text, it captures the relationship between topics as a structure and diagrams them to analyze the flow of logic and the characteristics of topic composition. This makes it possible to grasp communication, which was previously evaluated intuitively, in a structural and objective manner. While general AI is strong in generating and predicting words and context, this technology is characterized by its focus on 'topic relationships' and 'structure.' It is an approach positioned in a different field from generative AI within the AI technology domains organized by the Japanese Society for Artificial Intelligence. Furthermore, by using this technology, communication data existing within an organization can be analyzed at various granularities—such as by department, scene, or individual—to clarify 'where, what, and to whom it is functioning.' This enables evidence-based decision-making for measures that were previously ambiguous. <ambiguous.