RE-RFME: Real-Estate RFME Model for customer segmentation
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909182956929024 |
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| author | Pandey, Anurag Kumar Goyal, Anil Sikka, Nikhil |
| author_facet | Pandey, Anurag Kumar Goyal, Anil Sikka, Nikhil |
| contents | Marketing is one of the high-cost activities for any online platform. With the increase in the number of customers, it is crucial to understand customers based on their dynamic behaviors to design effective marketing strategies. Customer segmentation is a widely used approach to group customers into different categories and design the marketing strategy targeting each group individually. Therefore, in this paper, we propose an end-to-end pipeline RE-RFME for segmenting customers into 4 groups: high value, promising, need attention, and need activation. Concretely, we propose a novel RFME (Recency, Frequency, Monetary and Engagement) model to track behavioral features of customers and segment them into different categories. Finally, we train the K-means clustering algorithm to cluster the user into one of the 4 categories. We show the effectiveness of the proposed approach on real-world Housing.com datasets for both website and mobile application users. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17177 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RE-RFME: Real-Estate RFME Model for customer segmentation Pandey, Anurag Kumar Goyal, Anil Sikka, Nikhil Machine Learning Information Retrieval Marketing is one of the high-cost activities for any online platform. With the increase in the number of customers, it is crucial to understand customers based on their dynamic behaviors to design effective marketing strategies. Customer segmentation is a widely used approach to group customers into different categories and design the marketing strategy targeting each group individually. Therefore, in this paper, we propose an end-to-end pipeline RE-RFME for segmenting customers into 4 groups: high value, promising, need attention, and need activation. Concretely, we propose a novel RFME (Recency, Frequency, Monetary and Engagement) model to track behavioral features of customers and segment them into different categories. Finally, we train the K-means clustering algorithm to cluster the user into one of the 4 categories. We show the effectiveness of the proposed approach on real-world Housing.com datasets for both website and mobile application users. |
| title | RE-RFME: Real-Estate RFME Model for customer segmentation |
| topic | Machine Learning Information Retrieval |
| url | https://arxiv.org/abs/2404.17177 |