International Journal of Database Theory and Application Vol. 5, No. 3, September, 2012 73 Implementing A Data Mining Solution To Customer Segmentation For Decayable Products – A Case Study For A Textile Firm Vahid Golmah and Golsa Mirhashemi Department of Computer Engineering, Islamic Azad University, Neyshabur Branch, Neyshabu, 6621901, Iran v.golmah@in.iut.ac.ir, mirhashemi.golsa@gmail.com Abstract In this paper is developed a model to cluster customers who need to production change continuously. Mere mathematical models to cluster customers lead to ignoring the environment factors in model. Therefore, for Adjusting model to reality, we should use environment factors in model building. The proposed model is flexible against changes of environment and it causes to resulted model can use for clustering the customers of decay able productions. Proposed model perform on customer's information of a textile firm. Key words: Customer segmentation, RFM model, Fuzzy Analytical Network process (FANP), Self organization Map (SOM) 1. Introduction E-business is a new business model that transforms key business processes between customers, suppliers, employees, business patterns, provoking radical changes in the way in which businesses operate [1]. For most firms, becoming an e-business is an evolutionary journey from initial to final stages. This kind of transformation may involve adopting new technologies, redesigning business processes and restructuring management. To reduce the turbulence caused by change and enable firms to transform themselves into e-business, change must be supported by a critical mass of stakeholders, including employees, patterns and especially, customers [2]. From the perspective of niche marketing, all customers are not equal (they have different lifetime value or purchase behaviors). Therefore, Managers should carefully analysis customer behavior to better discriminate and more effectively allocate resources to the most profitable group of customers through the cycle of customer identification, customer attraction, customer retention, and customer development. However, instead of targeting all customers equally or providing the same incentive offers to all customers, enterprises can select only those customers who meet certain profitability criteria based on their individual needs or purchasing behaviors [3]. In this regard, Customer relationship management (CRM) is an important business approach to manage the customer's relationship (Marketing, Sales, Services, and Support). The Concept of customer lifetime value (CLV) or customer loyalty in CRM is the present value of all future profits generated from a customer and it is important for helping decision-makers target markets more clearly in fiercely competitive environments[4]. Several studies have done to calculate of CLV and use it [4-6]. Generally, Recency, Frequency, and Monetary (RFM) analysis have been used to measure the CLV [7-9]. This study uses sales transaction data of a textile manufacturing as the basis for work of knowledge discovery in database (KDD). It applies group decision-making to regard environmental effects on model by weighting RFM variables and data mining to segment customers. RFM weighting is performed using the analytical network process (ANP), which