Customer Segmentation Analysis Using K-Means Clustering (Machine Learning Algorithm in case of tackling sales issues in the field of economy)

Rukhsora Mardieva

GREEN ECONOMY AND DEVELOPMENT, 2024, vol. 2

Abstract: This study investigates customer segmentation using the K-means clustering algorithm to analyze annualincome and spending scores. The elbow method identified five optimal clusters, each characterized by unique spendingand income patterns. Key findings reveal distinct customer groups, such as “High Income, High Spending” and “LowIncome, Low Spending,” with additional analysis of age and gender distributions. The research provides actionableinsights for businesses to develop targeted marketing strategies and improve customer retention. By leveraging K-meansclustering, companies can optimize resource allocation and better understand their customer base, driving data-informeddecision-making in a competitive market place.

Keywords: Customer segmentation; K-means clustering; elbow method; marketing strategies; spending behavior; annual income; data visualization. (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:teu:ged000:v:2:y:2024:id:4248

DOI: 10.5281/zenodo.14741552

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