Zaman, ShakilaNoor, JannatunSagor, Kawsar MahmudSadhin, Masrur ArefinJahan, IshratProttay, Rezwanul Karim2023-12-202023-12-2020232023-05ID 18101638ID 18101626ID 18101310ID 18101308http://hdl.handle.net/10361/22010Cataloged from PDF version of thesis.Includes bibliographical references (pages 35-39).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.Customer segmentation is a big part of the superstore industry. Traditionally, the RFM model has been used to segment customers to maximize profit. This work proposes a new customer segmentation named LRFMVD based on RFM and LRFMV models in hopes of providing a more sure-fire way of segmenting customers. The k-means clustering method will be used for the proposed model. The clusters created by K-means are then analyzed using the LRFMVD model to find a correlation between profit and volume. Many works have been done previously on customer segmentation for maximizing profit, but none of those were able to show a straightforward representation of profit, volume, and discounts on products. Unsupervised learning was used to investigate the correlations between volume, discount, and profit. Customers are then segmented using the Customer Classification Matrix, which looks at the properties of all clusters. The L, R, F, M, VD parameters’ values are compared to the cluster mean values, and based on whether these values are higher or lower than the average, customers are segmented. Comparisons among the three models reveal that the latter provides more profit per head than the other two, and is able to identify customers who cause superstores to lose money or make a loss.39 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.VolumeSilhouetteElbowRFM analysisLRFMV and LRFMVD analysisK- meansCustomer relations--Management--Data processingThesis