Customer segmentation using a fuzzy ahp and clustering based approach: An application in an international TV manufacturing company

Today, the most valid way to achieve sustainable competitive advantage is shifting the focus from a product oriented view to a customer oriented view. However, due to more complex nature of customer behaviors, management of a customer base has become more difficult. Therefore, both business understanding and customer database analysis become vital. In this concern, customer segmentation plays an important role in marketing strategies and product development. This study aims to divide customer base in an international TV manufacturing company into discrete customer groups that share similar characteristics and also to find relative importance of these groups. Two different approaches are used for this purpose. First approach divides customer base using a characteristic called overall score. Overall score is a combined score of eight different characteristics namely, recency, loyalty, average annual demand, average annual sales revenue, frequency, long term relationship potential?, ?average percentage change in annual demand and ?average percentage change in annual sales revenue. This score computed by taking weighted average of the characteristics where weights are obtained by using Fuzzy Analytical Hierarchy Process (AHP). Second approach groups customers according to their similarities with respect to eight characteristics that mentioned above. Agglomerative hierarchical clustering algorithms (Wards method, single linkage, complete linkage) and k-means algorithm are employed to segment the customers. Five customer segments are named as best, valuable, average, potential valuable and potential invaluable customers. The results reveal that the proposed approach can effectively be used in practice for proper customer segmentation. Keywords: Customer segmentation, data clustering, fuzzy AHP