Müşteri ilişkileri yönetiminde veri madenciliği uygulaması : Bir perakende mağazasında market sepet analizi

In today s world, hard conditions in the market lead the companies to find new ways to compete better. With the intensive global competition and rapidly changing technological environments, meeting customers? various needs and maximizing the value of profitable customers are becoming the only viable option for many contemporary companies. Customer Relationship Management (CRM) provides organizations with the platform to obtain a competitive advantage by embracing customer needs and building value driven long-term relationships.CRM is an iterative process that turns customer data into customer loyalty. Analyzing the customer database and convert the data into information that will help company develop programs for building customer loyalty. In the analysis of this data, data mining techniques are essentially used. Association rules are one of the most frequently used methods which are the special application areas of the data mining. Association rules are the rules that include which items commonly occur together in the same transactions. The Apriori algorithm is the most popular association rule algorithm which discovers all frequent itemsets in large database of transactions. This algorithm uses iterative approach to count the frequent itemsets. Using this algorithm, candidate patterns which receive sufficient support from the database and the algorithm uses aprior gen actions join and prune to find all frequent itemsets.The aim of this study is to propose a base for the customer relationship management activities by using data mining tools and applications for a firm in retail sector. Customer master data and sales transactions of customers are converted to meaningful information that can be used for customer relationship management activities. In this concern, a market basket analysis is performed, and an application was conducted to find association rules from market datasets by using apriori algorithm.Keywords: Customer relationship management, data mining, market basket analysis.