Bayesian Hidden Markov Model for Evaluating the Influence of In-Store Stationary Time of Customers on Their Purchase Behavior

In this paper, we propose a purchase behavior model of customers based on shopping path data acquired using radio frequency identification technology in a Japanese supermarket. The proposed model assumes that the in-store customer behavior follows the Markov process. It also assumes that the influence of customer's stay at the sales floor on customer's purchase behavior changes depending on their latent state. We employed model analysis to show that customer's stationary times have a strong positive influence on their purchase behavior at the sales areas such as Western Deli, Japanese Deli, Meat, and Fresh Produce located in the outside area of the store. The stationary time had a negative influence on the choice and weak positive influence on the quantity at the sales areas such as Household Goods, Drinks & Liquor, and Frozen Foods located in the inside area of the store. With reference to this analysis, store managers can set management policies that take into consideration the influence of different stationary times on customer's purchase behavior depending on the latent state of customers for each sales floor.