LRFMV: an efficient customer segmentation model for superstores
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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| 語言: | English |
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Brac University
2021
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| 在線閱讀: | http://hdl.handle.net/10361/15437 |
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10361-15437 |
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10361-154372022-01-26T10:21:41Z LRFMV: an efficient customer segmentation model for superstores Toyeb, Md. Mahfuza, Rezwana Islam, Nafisa Emon, Md Asaduzzaman Faisal Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Customer segmentation RFM analysis LRFMV analysis K-means K-Medoids Mini Batch K-means Volume Silhouette Elbow Traits This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 71-75). In superstore business, the recency, frequency, and monetary (RFM) based on cus tomers’ purchase results is preferred to categorize valuable customers in order to increase profit margins. This paper develops an enhanced RFM (recency, fre quency, monetary) and LRFM (length, recency, frequency, monetary) model, namely LRFMV (length, recency, frequency, monetary, and volume), and then clusters the data using the standard K-means, K-medoids and Mini Batch K-means algorithms. The results obtained from the three algorithms are compared and the K-means al gorithm is chosen for the superstore dataset of the proposed LRFMV model. All clusters created using these three algorithms are evaluated in the LRFMV model and a close relationship between profit and volume is observed. A clear profit-quantity relationship of items has yet not been seen in any prior study on the RFM and LRFM models. Grouping customers aiming at the profit maximization existed previously but there were no clear and direct depiction of profit and quantity of sold items. To establish a relationship between volume and profit, this study applied unsuper vised machine learning to investigate the patterns, trends, and correlations between these two variables. The traits of all the clusters are analyzed by the Customer Classification Matrix. The values of LRFMV variables that are larger or less than the overall average for each cluster are identified and utilised as their traits. The RFM model, the LRFM model and the suggested LRFMV model are compared, and the outcome indicates that the LRFMV model may create more segments with the same number of customers while maintaining a greater profit per head. Md.Toyeb Rezwana Mahfuza Nafisa Islam Md Asaduzzaman Faisal Emon B. Computer Science 2021-10-19T06:49:30Z 2021-10-19T06:49:30Z 2021 2021-06 Thesis ID 17101399 ID 17301016 ID 17101448 ID 17301188 http://hdl.handle.net/10361/15437 en Brac 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. 75 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Customer segmentation RFM analysis LRFMV analysis K-means K-Medoids Mini Batch K-means Volume Silhouette Elbow Traits |
| spellingShingle |
Customer segmentation RFM analysis LRFMV analysis K-means K-Medoids Mini Batch K-means Volume Silhouette Elbow Traits Toyeb, Md. Mahfuza, Rezwana Islam, Nafisa Emon, Md Asaduzzaman Faisal LRFMV: an efficient customer segmentation model for superstores |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Alam, Md. Golam Rabiul |
| author_facet |
Alam, Md. Golam Rabiul Toyeb, Md. Mahfuza, Rezwana Islam, Nafisa Emon, Md Asaduzzaman Faisal |
| format |
Thesis |
| author |
Toyeb, Md. Mahfuza, Rezwana Islam, Nafisa Emon, Md Asaduzzaman Faisal |
| author_sort |
Toyeb, Md. |
| title |
LRFMV: an efficient customer segmentation model for superstores |
| title_short |
LRFMV: an efficient customer segmentation model for superstores |
| title_full |
LRFMV: an efficient customer segmentation model for superstores |
| title_fullStr |
LRFMV: an efficient customer segmentation model for superstores |
| title_full_unstemmed |
LRFMV: an efficient customer segmentation model for superstores |
| title_sort |
lrfmv: an efficient customer segmentation model for superstores |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15437 |
| work_keys_str_mv |
AT toyebmd lrfmvanefficientcustomersegmentationmodelforsuperstores AT mahfuzarezwana lrfmvanefficientcustomersegmentationmodelforsuperstores AT islamnafisa lrfmvanefficientcustomersegmentationmodelforsuperstores AT emonmdasaduzzamanfaisal lrfmvanefficientcustomersegmentationmodelforsuperstores |
| _version_ |
1814309286709297152 |