Abstract
Offline stores are seriously challenged by online shops. To attract more customers to compete with online shops, the patterns of customer flows and their influence factors are important knowledge. To address this issue, we collected indoor positioning data of 534,641 and 59,160 customers in two shopping malls (i.e., Dayuecheng (DYC) in Beijing and Longhu (LH) in Chongqing, China) for one week, respectively. The temporal patterns of the customer flows show that (1) total customer flows are high on weekends and low midweek and (2) peak hourly flow is related to mealtimes for LH and only on weekdays for DYC. The difference in temporal patterns between the two malls may be attributed to the difference in their locations. The customer flows to stores reveal that the customer flows to clothing, food and general stores are the highest; specifically, in DYC, the order is clothing, food and general, while in LH, it is food, clothing and general. To identify the factors influencing customer flow, we applied linear regression to the inflow density of stores (customers per square meter) of two major classes (clothing and food stores), with 10 locational and social factors as independent variables. The results indicate that flow density is significantly influenced by store location, visibility (except for food stores in DYC) and reputation. Besides, the difference between the two store classes is that clothing stores are influenced by more convenience factors, including distance to an elevator and distance to the floor center (only for LH). Overall, the two shopping malls demonstrate similar customer flow patterns and influencing factors with some obvious differences also attributed to their layout, functions and locations.
Highlights
In the e-commerce era, offline businesses have been challenged by online businesses due to high marketing costs [1,2]
In LH, we found a different phenomenon: approximately 50% of the customer flow to stores is for food, and only 15–17% is for clothing stores, on both days
The patterns of customer flows in DYC and LH in terms of the overall malls, store classes and stores were analyzed in this paper
Summary
In the e-commerce era, offline businesses have been challenged by online businesses due to high marketing costs [1,2]. Many people still prefer the offline look-and-feel and touch-and-feel buying experience [3,4], and many online retailers, such as Amazon, are starting physical stores [5]. To compete with online shops in attracting customers, shopping malls need to know the popularity of stores and what factors influence customers to visit them In this way, sales can be improved by optimizing the layout and business format of shopping malls. The objective of this paper was to determine the customer flow pattern and its influencing factors based on indoor positioning data This information may help to support the better management of shopping malls and provide a more humanistic service to customers, such as by optimizing the tenant mix, reconstructing the layout of stores and rearranging the public facilities
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