Müşteri segmentasyonu için hibrit bir yaklaşıma dayalı bir karar destek sistemi

With the rapid growth in information systems and technology which makes big data collection and management easier, all industries especially service industries begin to pay more attention to the analytic techniques for Customer Relationship Management (CRM). The purpose of this study is to develop a two level decision support system to derive meaningful and useful information from data and enhance decision making based on this information in CRM. To this aim, heuristic Algorithms have been integrated with Clustering techniques and Multi Criteria Decision Making Methods in this study. In the first level, to cluster the customers a hybrid approach that integrates Simulated Annealing and K Means Algorithms is used. In the second level, the highest value customers are determined using another hybrid approach that integrates Analytic Hierarchy Process to reach the weights of the criteria and TOPSIS to prioritize the clusters. The decision support system will enhance the process of planning business activities and the companies will be able to make strategic decisions considering the most important customers. Companies can also gain a significant competitive advantage by differentiation of the products and services to meet the specific needs of every single customer. To explore the applicability of the decision support system, the computational experiments are conducted that handle a tourism case, in which data regarding the city of Jakarta / Indonesia is used.