Harnessing Clustering Methods for Data-Driven Customer Segmentation: Strategies for Business Growth and Success

EasyChair Preprint 14445

Abstract

In today’s data-driven marketplace, effective customer segmentation is crucial for targeted marketing and business growth. This study explores the application of clustering methods to enhance data-driven customer segmentation strategies. By analyzing customer behavior, preferences, and demographics, clustering techniques such as K-means, hierarchical clustering, and DBSCAN are employed to identify distinct customer groups. The study evaluates the effectiveness of these methods in creating actionable customer profiles that drive personalized marketing strategies and improve customer engagement. Insights gained from clustering help businesses tailor their offerings, optimize resource allocation, and enhance overall customer satisfaction.

Keyphrases: Customer Segmentation, Targeted Marketing, business growth, data-driven customer segmentation, hierarchical clustering

@booklet{EasyChair:14445,
  author    = {Adeoye Ibrahim},
  title     = {Harnessing Clustering Methods for Data-Driven Customer Segmentation: Strategies for Business Growth and Success},
  howpublished = {EasyChair Preprint 14445},
  year      = {EasyChair, 2024}}