Construction of a Model for Estimating Sales Thinking Processes by Learning Tacit Knowledge

Abstract

In BtoB sales, the tacit knowledge possessed by sales personnel is a source of competitive advantage; however, passing on tacit knowledge is difficult, making transfer to junior sales personnel a critical issue. Existing sales support systems often merely recommend the next action without showing the rationale behind the judgment. This study aims to construct a model that learns and estimates the thinking process based on tacit knowledge of experts to provide educational support. First, we extracted specific tacit knowledge through interviews with experts and defined the thinking process in sales activities as a four-stage structure consisting of Phase, Focal Information, Recalled Knowledge, and Decision Making. Second, we conducted a scenario-based questionnaire survey and collected 365 valid responses from experienced sales personnel. We constructed a machine learning model that sequentially estimates these four elements, using the output of the previous stage as the input for the next. In the model evaluation, we selected models prioritizing Recall for the estimation of Information and Knowledge from a training perspective, and Weighted Precision considering importance for Decision Making. Results showed that the integrated model can learn the context from previous stages and simulate the thinking process. Furthermore, we implemented this model into an AI agent. This agent structures and presents the recommended action along with its rationale (Phase, Information, and Knowledge) following the thinking process. This goes beyond merely presenting the correct answer; it is expected to contribute to the development of sales personnel who can make autonomous judgments and execute projects smoothly.

Keywords: Tacit Knowledge, Thinking Process, Machine Learning, Decision Support, Sales Enablement

DOI: 10.54941/ahfe1007584