Optimizing Business Success Through Data-Driven Customer Segmentation: an Analysis of Clustering Techniques

EasyChair Preprint 14444

Abstract

In today's competitive business environment, understanding customer behavior and tailoring strategies to meet their needs is crucial for optimizing success. This study explores the role of data-driven customer segmentation in enhancing business performance through advanced clustering techniques. By analyzing various clustering methods, including K-means, hierarchical clustering, and DBSCAN, this research aims to identify the most effective approach for segmenting customers based on behavioral and demographic data. The analysis leverages a comprehensive dataset comprising customer interactions, purchase history, and socio-economic factors. Key metrics such as cluster cohesion, separation, and stability are evaluated to assess the performance of each technique.

Keyphrases: Cluster Cohesion, Clustering Techniques, optimizing success, separation, stability

@booklet{EasyChair:14444,
  author    = {Adeoye Qudus},
  title     = {Optimizing Business Success Through Data-Driven Customer Segmentation: an Analysis of Clustering Techniques},
  howpublished = {EasyChair Preprint 14444},
  year      = {EasyChair, 2024}}