Improving Decision-Making in Retail: Employing DBSCAN Algorithm for Enhanced Customer Segmentation in Mall Environments

The study is conducted on shopping mall data and utilized DBSCAN clustering for customer segmentation for data analysis. Data is obtained from the Kaggle database. Segmentation was carried out using the DBSCAN clustering algorithm. Two distinct clusters have been identified based on spending score with respect to age and annual income. It has been identified 5 clusters for annual income based on spending scores. From analysis it has been observed that for first set there was lower spending scores for higher age groups, while the second set showed the spending score was low with high annual income. Consequently, the mall can strategically target and attract these cluster customers to increase profits.