Sports center customer segmentation: a case study

TL;DR

This paper presents an innovative customer segmentation approach for sports centers, utilizing adaptive distance functions and genetic algorithms to improve marketing strategies and customer experience.

Contribution

It introduces a robust, case-specific segmentation method combining data partitioning, adaptive distance metrics, and genetic algorithm optimization.

Findings

Enhanced dataset reliability for segmentation

Improved operational efficiency for sports centers

Better customer experience through targeted marketing

Abstract

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.