A Study on the Development of Product Market Value through POS Data Analysis Using Semantic Cells

In modern society, supermarkets serve as an essential infrastructure for daily life, and their sales are greatly influenced by the value judgments of customers regarding products. As digital technologies advance, many companies are leveraging POS (Point of Sales) data analysis to increase their profits. This study analyses POS data using a novel metric called "diversity," defined as the variety of contexts in which a product is purchased. The discussion proceeds by testing the hypothesis that products exhibiting similar temporal changes in diversity tend to share similar characteristics. To measure diversity, the study employs Semantic Cells (SCs), a technique effective for analysing time-series data, and applies a new method that focuses on temporal changes in diversity to perform product clustering. The study found that products with similar patterns of temporal change are indeed related, thereby supporting the proposed hypothesis.