Understanding Customers' Interests in the Wild

Abstract

Today, retailers spend considerable efforts to provide a personalized shopping experience to their customers. As data-driven marketing helps to meet customer requirements, it is important to understand individual needs. However, offline stores-unlike their online counterparts-have great difficulty knowing their customers' needs due to a lack of proper context information. In this paper, we proposed a framework for estimating customer interests by using various sensor devices. The participants in our pilot study expected that recommendation services that adopt their interests would help to reduce their shopping time. As a result, shop assistants will have a stronger ability to understand, analyze, and even predict customer interests in the near future.