Category evaluation method for business intelligence using a hierarchical Bayes model

Effective category management by grocery stores requires product category evaluation. Previous studies have evaluated product categories using point-of-sales shopping behavior data. Recent developments in radio frequency identification technology facilitate the tracking of customer shopping paths within a store and aggregated stay time in sales areas. In addition to radio frequency identification data, we use product discount information from discount flie data and construct a sales area evaluation model incorporating stay time and bargain scale in sales areas. We employ a hierarchical Bayes model to predict the number of purchased products. A typical hierarchical Bayes model considers each customer as a subject; however the proposed model considers each sales area as a subject. From estimation results of the posterior parameter, we evaluate the influence of stay time and discount scale for each sales area. This provides useful performance measure for the sales areas.