Customer Relationship Management technology groups customers based on their transaction details. The focus of the paper is finding groups of clients with similar buying patterns in a department store using these data for targeted marketing. The performance of K-means, K-Medoids, Agglomerative Clustering and DBSCAN is compared on a real retail dataset of a department store. Results show that the K-Medoids algorithm is more robust in case of noise and outliers in data. K-Means and DBSCAN perform better in terms of time, especially for large retails datasets.