Use of Data Mining Techniques to Improve the Effectiveness of Sales and Marketing

Classification and patterns extraction from customer data is very important factors for business support and decision making. Identification of newly emerging trends is needed in business process. Sales patterns from inventory data indicate market trends and can be used in forecasting which has great potential for decision making, strategic planning and market competition. The system consists of two phases. In the first phase, we divide the stock data in three different clusters on the basis of product categories and sold quantities i.e. Dead-Stock (DS), Slow-Moving (SM) and FastMoving (FM) using K-means algorithm. In the second phase we will use Most Frequent Pattern (MFP) algorithm to find frequent patterns of item attributes in each category of products and also give sales trend in a compact form.