A retailer wants to segment its customers to optimize targeted marketing campaigns. Which of the following approaches would be most effective for customer segmentation based on purchasing patterns?

K-means clustering is well-suited for customer segmentation as it groups customers based on similarities in purchasing behaviors, allowing retailers to identify clusters of customers with similar shopping patterns. By analyzing customer purchasing data, K-means assigns each customer to the nearest cluster center, effectively organizing the customer base into distinct segments. This method is ideal for developing targeted marketing strategies, as each segment represents a specific customer type that can be addressed with tailored promotions. K-means is computationally efficient and works well with large datasets, making it a popular choice for customer segmentation in retail. The other options are incorrect because: • Linear Regression is used for continuous prediction, not clustering. • Principal Component Analysis (PCA) reduces dimensions but doesn’t segment data into groups. • Logistic Regression is a classification tool, not suited for unsupervised segmentation. • Naive Bayes Classifier is a supervised technique that classifies data based on probability, not clustering.