LRFS: online shoppers’ behavior based efficient customer segmentation model
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023.
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| Jezik: | English |
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Brac University
2023
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| Online pristup: | http://hdl.handle.net/10361/18680 |
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10361-186802023-07-09T21:08:29Z LRFS: online shoppers’ behavior based efficient customer segmentation model Khan, Riyo Hayat Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Customer segmentation Unsupervised machine learning K-Means K-Medoids DBSCAN RFM analysis LRFM analysis Dimensionality reduction PCA t-SNE Autoencoder Deep learning Google analytics Customer relations--Management--Data processing Machine learning Cognitive learning theory This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 54-64). The popularity of online shopping has grown significantly across the globe in recent years. This research proposes a customer segmentation model LRFS, an extended version of LRF model, built specifically for online shopping, using a dataset that includes some features taken from Google Analytics. It introduces component S, which measures the Staying Rate across the Revenue spent by the customers on a particular website, to get a better insight into the customer base. Three wellknown clustering methods K-Means, K-Medoids, and DBSCAN algorithms were incorporated along with the proposed model. For each of these algorithms, the dataset was compressed separately using three different dimensionality techniques such as PCA, t-SNE, and Autoencoder to figure out the combinations that could work well for the used dataset. A comparative analysis has also been conducted among LR, LF, LRF, and the proposed LRFS model using K-Means clustering. LRFS has outperformed the other three models in terms of better cluster assignment of the customers. For customer analysis, a combined Customer Classification and Customer Relationship Matrix was used to determine the clustered groups according to their characteristics. The combination of K-Median and t-SNE was chosen for the final combined matrix since it had the highest number of most distinct clusters with all traits of customer groups. Finally, some test cases as well as related use case scenarios have been described and visualized using LRFS along with K-Means and PCA. Riyo Hayat Khan M. Computer Science and Engineering 2023-07-09T05:43:52Z 2023-07-09T05:43:52Z 2023 2023-02 Thesis ID 21366016 http://hdl.handle.net/10361/18680 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. 77 pages application/pdf Brac University |
| institution |
Brac University |
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Institutional Repository |
| language |
English |
| topic |
Customer segmentation Unsupervised machine learning K-Means K-Medoids DBSCAN RFM analysis LRFM analysis Dimensionality reduction PCA t-SNE Autoencoder Deep learning Google analytics Customer relations--Management--Data processing Machine learning Cognitive learning theory |
| spellingShingle |
Customer segmentation Unsupervised machine learning K-Means K-Medoids DBSCAN RFM analysis LRFM analysis Dimensionality reduction PCA t-SNE Autoencoder Deep learning Google analytics Customer relations--Management--Data processing Machine learning Cognitive learning theory Khan, Riyo Hayat LRFS: online shoppers’ behavior based efficient customer segmentation model |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023. |
| author2 |
Alam, Md. Golam Rabiul |
| author_facet |
Alam, Md. Golam Rabiul Khan, Riyo Hayat |
| format |
Thesis |
| author |
Khan, Riyo Hayat |
| author_sort |
Khan, Riyo Hayat |
| title |
LRFS: online shoppers’ behavior based efficient customer segmentation model |
| title_short |
LRFS: online shoppers’ behavior based efficient customer segmentation model |
| title_full |
LRFS: online shoppers’ behavior based efficient customer segmentation model |
| title_fullStr |
LRFS: online shoppers’ behavior based efficient customer segmentation model |
| title_full_unstemmed |
LRFS: online shoppers’ behavior based efficient customer segmentation model |
| title_sort |
lrfs: online shoppers’ behavior based efficient customer segmentation model |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/18680 |
| work_keys_str_mv |
AT khanriyohayat lrfsonlineshoppersbehaviorbasedefficientcustomersegmentationmodel |
| _version_ |
1814308007704526848 |