Customer Segmentation of Shopping Mall Users Using K-Means Clustering

Abstract

The most successful companies are the one that know their customers very well and can anticipate their needs. Good customer profiles at the fingertips have businesses improve marketing campaigns, targeting feature launches and product roadmaps. In this study, exploratory data analysis was done on the shopping mall data, and customer segmentation was done using k-means clustering. Two different clusters were done based on age vs. spending score and annual income vs. spending score. Four optimum clusters were obtained for age and spending scores using the elbow graph method, and five optimum clusters were obtained for annual income and spending scores. Firstly, for clusters based on age and spending score, people with higher age groups have less spending scores. Secondly, clusters based on annual income and spending scores had high annual income and very low spending scores. Thus, the mall can offer these cluster customers to attract them, thereby increasing its profits.

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