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. 2019 Jan 29;19(3):557.
doi: 10.3390/s19030557.

Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application

Onur Dogan  1 , Jose-Luis Bayo-Monton  2 , Carlos Fernandez-Llatas  3 , Basar Oztaysi  4
Affiliations

Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application

Onur Dogan et al. Sensors (Basel). .

Abstract

The study presents some results of customer paths' analysis in a shopping mall. Bluetooth-based technology is used to collect data. The event log containing spatiotemporal information is analyzed with process mining. Process mining is a technique that enables one to see the whole process contrary to data-centric methods. The use of process mining can provide a readily-understandable view of the customer paths. We installed iBeacon devices, a Bluetooth-based positioning system, in the shopping mall. During December 2017 and January and February 2018, close to 8000 customer data were captured. We aim to investigate customer behaviors regarding gender by using their paths. We can determine the gender of customers if they go to the men's bathroom or women's bathroom. Since the study has a comprehensive scope, we focused on male and female customers' behaviors. This study shows that male and female customers have different behaviors. Their duration and paths, in general, are not similar. In addition, the study shows that the process mining technique is a viable way to analyze customer behavior using Bluetooth-based technology.

Keywords: Bluetooth; gender behavior; indoor locations; process mining; shopping mall.

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Conflict of interest statement

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.

Figures

Figure 1
Figure 1
The parallel activity log inference algorithm (PALIA) suite steps.
Figure 2
Figure 2
The architecture of beacon technology.
Figure 3
Figure 3
Number of customers and cases.
Figure 4
Figure 4
Sample validation data for discovered customer paths.
Figure 5
Figure 5
Customers’ paths in December. (a): Male paths-1st cluster (b): Female paths-1st cluster (c): Male paths-2nd cluster (d): Female paths-2nd cluster.
Figure 6
Figure 6
Customers’ paths in January. (a): Male paths-1st cluster (b): Female paths-1st cluster (c): Male paths-2nd cluster (d): Female paths-2nd cluster.
Figure 7
Figure 7
Customers’ paths in February. (a): Male paths-1st cluster (b): Female paths-1st cluster (c): Male paths-2nd cluster (d): Female paths-2nd cluster.
Figure 8
Figure 8
Probability matrix of transitions of male customers (×10−3).
Figure 9
Figure 9
Probability matrix of transitions of female customers (×10−3).
See this image and copyright information in PMC

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