Customer Personality Segmentation Using K-Means Clustering

Enhancing sales strategies and developing targeted marketing approaches that effectively resonate with customers are crucial for achieving success in today's highly competitive marketplace. To accomplish this, sales organizations can invest in their teams and equip them with analytical tools to examine customer personality and behavior. By employing unsupervised learning algorithms such as K-Means clustering, customer data can be efficiently analyzed, facilitating the grouping of customers into clusters based on shared characteristics. This enables the identification of patterns in customer behavior, allowing companies to create tailored marketing strategies. This study provides practical recommendations for companies to enhance their sales and marketing strategies based on the analysis of a benchmark dataset from Kaggle, revealing three primary customer clusters: new customers with lower income and spending, old customers with average income and behavior, and old customers with higher income and spending. These insights empower companies to align their efforts with customer preferences and improve.

I. Introduction

Traditional marketing and sales manually analyzed customer personality using their expertise and experience to identify common patterns and trends among customers.