Understanding Customer Behaviour in Shopping Mall by Indoor Tracking and QR Identification

Abstract

The prosperity of various indoor data tracking technologies makes possible for the large collection of tracking data in indoor spaces such shopping malls. Much of the focus has been on several fundamental problems such finding the ideal location, indoor shopping mall model, products requirements and understanding the patterns of shopping behavior of customers to facilitate higher growth in sales and to analyze strategies to efficiently manage the customer data, this paper attempts to analyze customer behavior from a unique indoor tracking data, which will promote the convergence between various applications and the underlying data. In particular, this paper uses the alternative method for indoor tracking and customer data by using QR code technology which uniquely differentiates each customer, collectively stores data and provides organized purchased product data, wherein we cluster users into several groups and summarize the most characteristic behaviors of each cluster. Last but not least, we analyze customer’s individual behaviors through two aspects: 1) the K-means clustering algorithm is used to reveal concentrated region for the attributes required to analyze and 2) a summary of all the purchase data classified into categories required by user are generated.