This paper discusses different clustering techniques that can be used in sales databases. The advancement of digital data collection and build up of data in data banks as a result of modernization in sales disciplines has brought in great challenges of data processing for better and meaningful results due to mass data deposits. Clustering techniques therefore are quite necessary so that the senior management in sales department can have access to processed data as they engage themselves in decision making processes. In this paper, I focus on the retail sales data mining, classification and clustering techniques. In this study I analyze the attributes for the prediction of buyer’s behavior and purchase performance by use of various classification methods like decision trees, C4.5 algorithm and ID3 algorithm.