Dataset of retail industry segmentation in Indonesia

To guarantee customer satisfaction, retailers need to manage their product inventory considering factors such as demand volume, frequency, consistency, and variability. By classifying products according to demand trends and inventory turnover, retailers can improve efficiency and customer contentment. Overall, the clustering approach ensures that high-demand items are readily available while reducing the surplus of less sought-after products.Post-COVID-19 pandemic, the delivery and logistics sector have much attention from researchers. Small-and-medium enterprises (SMEs) are prioritizing cost reduction in shipping by deploying streamlined strategies to lower overall logistics expenditures. One such tactic is clustering, where regions or delivery requests are grouped for transportation between distribution centers. Clustering plays a vital role in location-driven decision-making. Clustering product delivery based on customers' location will enhance operational efficiency, customer contentment, and financial returns.This case study explores customer segmentation using three-dimensional time-series data: total spending, frequency of purchases, and average basket size.