Insights into e-shopper preferences: Interactive visual analytics of Malaysian online shopping behavioural data

Abstract

With advancements in internet technology, secure online payment systems, and faster delivery methods, consumers are increasingly turning to online shopping as their preferred purchasing method. To remain competitive in this global industry, businesses must understand customer behaviour to meet the growing demand for online purchasing. While previous studies on online shopping behaviour primarily focused on empirical, statistical, and regression analyses, visual analytics has received less attention. This paper addresses this gap by developing an interactive dashboard that models online shopping behavioural data. Following the data visualisation pipeline, a four-step methodology was implemented to develop the dashboard consisting of data selection and pre-processing, data transformation, visual mapping and visualisation generation. The dashboard supports tasks such as data aggregation, clustering, and filtering, offering a comprehensive view of shopping behaviours. Users can interact with various tabs to explore visualisations of product categories by income level, age, and gender, as well as average shopping time by demographics, aiding decision-makers in gaining valuable insights into e-shopper preferences.