Abstract:

This study employed a robust research methodology and utilised Python programming as a comprehensive platform for analysing and visualising retail shopping trends. It addresses the pressing issue of understanding and improving shopping behaviour in dynamic retailing. Since a large portion of global consumer spending occurs in the retail industry, companies must understand the complexities of consumer behaviour to remain competitive. The research objectives are twofold: first, to examine the differences in shopping behaviour between genders, focusing on frequency, consumption amount, and product preference; second, to provide businesses and marketers with practical strategies to increase shopping participation and purchasing power. A robust research methodology was employed to achieve these goals, leveraging Python programming as a comprehensive platform for analysing and visualising retail shopping trends. The study included a large-scale survey of 4,000 respondents, with 2,652 (66%) male shoppers and 1,248 (34%) female shoppers. This extensive data set allows a detailed examination of gender-specific shopping behaviours and preferences. In addition, secondary data from annual reports and official publications supplemented the primary data collected. The research process involves various stages, including data collection, cleaning, descriptive statistics, data visualisation, statistical analysis and machine learning techniques customised to the study's specific needs. The analysis provides valuable insights into the differences in shopping behaviour between men and women and actionable recommendations for increasing shopping engagement and purchasing power. This study contributes to the existing body of knowledge on consumer behaviour in the retail industry by combining academic and industry insights in a comprehensive literature review and improving shopping behaviour by applying robust research methods and Python programming for data visualisation.