Elbow Method for Optimal Customer Segmentation Using K-Means Clustering

Customer segmentation is a crucial task in marketing and business strategy, allowing companies to targetspecific groups effectively. This paper explores the application of the elbow method to determine the optimalnumber of clusters for customer segmentation using K-means clustering. By analyzing customer data from aretail business, we demonstrate how the elbow method helps in identifying the most meaningful segments. Ourstudy highlights the practical utility, benefits, and limitations of this approach in real-world scenarios.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-15
DOI
https://doi.org/10.5281/zenodo.16883249
Primary Topic
Customer churn and segmentation
Type
article
Field-Weighted Citation Impact
0.00
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article

Elbow Method for Optimal Customer Segmentation Using K-Means Clustering

Zenodo (CERN European Organization for Nuclear Research)
Customer churn and segmentation
article

Elbow Method for Optimal Customer Segmentation Using K-Means Clustering

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Abstract

Customer segmentation is a crucial task in marketing and business strategy, allowing companies to targetspecific groups effectively. This paper explores the application of the elbow method to determine the optimalnumber of clusters for customer segmentation using K-means clustering. By analyzing customer data from aretail business, we demonstrate how the elbow method helps in identifying the most meaningful segments. Ourstudy highlights the practical utility, benefits, and limitations of this approach in real-world scenarios.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 44%
Customer churn and segmentation
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Elbow Method for Optimal Customer Segmentation Using K-Means Clustering · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS