Inferring gender and age of customers in shopping malls via indoor positioning data

Abstract

Customer profiles that include gender and age information are important to businesses and can be used to promote sales and provide personalized services. This information is gathered in e-commerce by analyzing customer visit records in virtual web space. However, such practice is difficult in brick-and-mortar businesses because the data that can be utilized to infer customer profiles are limited in physical spaces. In this paper, we attempt to infer the gender and age of customers using indoor positioning data generated by the Wi-Fi engine. To achieve this, we first construct a synthesized features vector to distinguish different profiles. This vector contains both customer spatial–temporal mobility characteristics and interest preferences. A hidden Markov model group detection method is then applied to detect customers who shop together because they usually show the same shopping behavior and it is difficult to distinguish their profiles. Finally, a random forest inference model is proposed to infer profiles of customers who shop alone. The indoor positioning data collected in the Longhu Tianjie Plaza in Chongqing were used as a case study. The result shows that customer profiles are indeed inferable from indoor positioning data. The accuracy of the gender inference model reaches 73.9%, while that of the age inference model is 67.9%. This demonstrates the potential value of new “big data” for promoting precision marketing and customer management in brick-and-mortar businesses.