The role of shopping malls in urban areas has been evolving in response to broader social and technological changes. In this context, this study applies clustering techniques and data analysis to examine the factors that influence consumer purchasing decisions in shopping malls across five intermediate cities in Colombia. Using a dataset that includes demographic variables such as age, income, and pet ownership, along with consumer perceptions about different aspects of shopping experiences, the research provides a more detailed view of consumer behavior.

The results, obtained through unsupervised machine learning models, reveal that purchasing decisions are shaped by a combination of demographic characteristics, lifestyle preferences, and specific amenities, such as pet-friendly spaces and parking availability. Three main consumer groups were identified: urban families, young professionals, and entrepreneurs, each showing distinct preferences and shopping patterns. For example, urban families tend to value pet-friendly environments, while young professionals place greater importance on convenience and alignment with their lifestyle.

Based on these findings, the study suggests practical strategies for shopping mall management aimed at improving marketing approaches and strengthening customer loyalty. These strategies focus on tailoring services to the needs and preferences of each consumer group. The use of clustering and quantitative analysis proved especially useful in generating these insights, offering a framework that could also be applied to other retail contexts seeking to improve customer engagement and sales performance.

Overall, by combining detailed consumer data with robust analytical methods, this research contributes to a better understanding of how shopping malls can adapt their strategies and operations to remain competitive in an increasingly dynamic retail environment