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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.17467 |
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| _version_ | 1866916262661062656 |
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| author | Soto, Juan Carmenaty, Ramón Lastra, Miguel Fernández-Luna, Juan M. Benítez, José M. |
| author_facet | Soto, Juan Carmenaty, Ramón Lastra, Miguel Fernández-Luna, Juan M. Benítez, José M. |
| contents | Customer segmentation is a fundamental process to develop effective marketing strategies, personalize customer experience and boost their retention and loyalty. This problem has been widely addressed in the scientific literature, yet no definitive solution for every case is available. A specific case study characterized by several individualizing features is thoroughly analyzed and discussed in this paper. Because of the case properties a robust and innovative approach to both data handling and analytical processes is required. The study led to a sound proposal for customer segmentation. The highlights of the proposal include a convenient data partition to decompose the problem, an adaptive distance function definition and its optimization through genetic algorithms. These comprehensive data handling strategies not only enhance the dataset reliability for segmentation analysis but also support the operational efficiency and marketing strategies of sports centers, ultimately improving the customer experience. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17467 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sports center customer segmentation: a case study Soto, Juan Carmenaty, Ramón Lastra, Miguel Fernández-Luna, Juan M. Benítez, José M. Machine Learning Neural and Evolutionary Computing Customer segmentation is a fundamental process to develop effective marketing strategies, personalize customer experience and boost their retention and loyalty. This problem has been widely addressed in the scientific literature, yet no definitive solution for every case is available. A specific case study characterized by several individualizing features is thoroughly analyzed and discussed in this paper. Because of the case properties a robust and innovative approach to both data handling and analytical processes is required. The study led to a sound proposal for customer segmentation. The highlights of the proposal include a convenient data partition to decompose the problem, an adaptive distance function definition and its optimization through genetic algorithms. These comprehensive data handling strategies not only enhance the dataset reliability for segmentation analysis but also support the operational efficiency and marketing strategies of sports centers, ultimately improving the customer experience. |
| title | Sports center customer segmentation: a case study |
| topic | Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2405.17467 |