Finding latent groups of customers via the poisson mixture regression model

Due to developments in technology, movement data tracking a customer´s movements in a supermarket in addition to conventional POS data are now available. A problem in analyzing such data is that an ordinary statistical model assuming customer homogeneity does not fit well to such data. In this article, we propose a framework for analyzing such data in a collection of supermarket departments. The framework is based on the mixture regression model assuming the customers´ heterogeneity. By the model, we find the latent homogenous groups of the customers and explain the number of items by a stationary time based on the regression model in each latent group. The method of the mixture regression model is explained in addition to the estimation method. We found that a small number of the customers buy more items by going to the supermarket departments and are more sensitive to the stationary time, while a large number of the customers buy less and are less sensitive.