Customer Segmentation and Management Strategy Optimization for Gyms Using K-Means Clustering

Ziyu Niu

2024

Abstract

This study aims to provide a robust segmentation strategy for the gym managers by using K-Means clustering algorithms. The objective is to help gym recognise valuable customer groups that align with their marketing strategies, thereby maximizing profitability. The dataset is from Kaggle which includes 4, 000 records related to gym membership. The dataset has features such as age. Data preprocessing includes standardization which is used to ensure that each variable contributed equally to the clustering process. Using Elbow Method to determine the best number of clusters and two clusters were identified as ideal. K-Means clustering, combined with Principal Component Analysis (PCA) for dimensionality reduction, revealed clear distinctions between the customer groups. The first cluster is consisted with old customers with low fitness frequency. While the second cluster includes younger, more active and higher income customers. These findings provide valuable concept for gym management to tailor service, such as providing low-intensity fitness programs for older members and high-intensity workouts or premium membership plans for younger, higher-income customers. The study proves the effectiveness of K-Means and PCA in customer segmentation while also suggesting that future research should explore more advanced algorithms and incorporate additional data to further refine the segmentation process.

Paper Citation

in Harvard Style

Niu Z. (2024). Customer Segmentation and Management Strategy Optimization for Gyms Using K-Means Clustering. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 239-242. DOI: 10.5220/0013214000004568

in Bibtex Style

@conference{ecai24,
author={Ziyu Niu},
title={Customer Segmentation and Management Strategy Optimization for Gyms Using K-Means Clustering},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={239-242},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013214000004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Customer Segmentation and Management Strategy Optimization for Gyms Using K-Means Clustering
SN - 978-989-758-726-9
AU - Niu Z.
PY - 2024
SP - 239
EP - 242
DO - 10.5220/0013214000004568
PB - SciTePress