Customer Segmentation Based on Online Shopping Using K-Means Algorithm

Customer segmentation is a crucial strategy for businesses seeking to optimize marketing efforts and enhance
customerexperiences. This study explores the use of the K-means clustering algorithm tosegment customers based on
theironline shopping behavior. By analyzing data from a large dataset of online transactions, we identify key features such
as purchase frequency, total spending, recency of purchases, and product category preferences for clustering. Using the Kmeans algorithm, we group customers into distinct segments, each exhibiting unique shopping behaviors and preferences.
The analysis reveals valuable insights into customer profiles, enabling targeted marketing strategies and personalized
recommendations. We evaluate the clustering results using metrics such as silhouette score and visualize the segments for
a comprehensive understanding of customer groups. The findings provide actionable strategies for businesses to engage
with customers more effectively, improve customer satisfaction, and drive sales growth.
Overall, this study demonstrates the potential of leveraging the K-means algorithm for customer segmentationin the
context of online shopping behavior, offering businesses a pathway to achieve better customer engagement and optimize
marketing campaigns.