Summary of Sports Center Customer Segmentation: a Case Study, by Juan Soto et al.
Sports center customer segmentation: a case study
by Juan Soto, Ramón Carmenaty, Miguel Lastra, Juan M. Fernández-Luna, José M. Benítez
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to customer segmentation is proposed in this paper, which addresses a crucial problem in developing effective marketing strategies and personalizing customer experiences. By leveraging genetic algorithms and an adaptive distance function, the study offers a robust and innovative framework for data handling and analytical processes. The proposal involves partitioning data to decompose complex problems, optimizing distance functions, and enhancing dataset reliability for segmentation analysis. This comprehensive approach is shown to improve operational efficiency and marketing strategies in sports centers, ultimately leading to enhanced customer experiences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are trying to find a better way to group customers based on their characteristics. They studied a specific case where the data was complex and needed special handling. To solve this problem, they developed a new approach that combines genetic algorithms with an adaptive distance function. This method helps make sure the data is reliable for analysis and also makes it easier to use the results in real-life marketing strategies. |
