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

Area of Science:

• Consumer behavior analysis
• Data mining and analytics
• Spatial analysis

Background:

• Understanding customer movement within retail environments is crucial for optimizing store layout and marketing strategies.
• Traditional methods often lack the granularity to capture detailed spatiotemporal customer journeys.
• Bluetooth-based positioning systems offer a novel approach to collect high-resolution movement data.

Purpose of the Study:

• To analyze customer paths in a shopping mall using Bluetooth technology.
• To investigate gender-based differences in customer behavior and movement patterns.
• To evaluate the effectiveness of process mining for analyzing customer journey data.

Main Methods:

• Deployment of iBeacon devices for Bluetooth-based positioning within a shopping mall.
• Collection of spatiotemporal data for approximately 8000 customers over three months (December 2017 - February 2018).
• Application of process mining techniques to analyze event logs and visualize customer paths, inferring gender based on restroom usage.

Main Results:

• Significant differences observed in the paths and duration of male versus female customers.
• Customer journeys, including dwell times and movement patterns, are not similar across genders.
• Process mining provides a clear and understandable visualization of complex customer behaviors.

Conclusions:

• Bluetooth-based technology combined with process mining is a viable method for detailed customer behavior analysis.
• Gender is a significant factor influencing customer navigation and behavior within a shopping mall.
• The findings offer valuable insights for retailers seeking to personalize customer experiences.