Modelling Customer Attraction Prediction in Customer Relation Management using Decision Tree: A Data Mining Approach

In Today’s quality- based competitive world, known as knowledge age, customer attraction is of ultimate importance. In respect to the slogan customer is always right , customer relation management is the core of an organizational strategy playing an important role in four aspects of customer identification, customer attraction, customer retaining, and customer satisfaction. Commercial organizations have perceived increased value of customers through analysis of customers’ life cycle. Data storing and data mining tools along with other customer relation management methods have provided new opportunities for the business. This paper tries to help organizations determine the criteria needed for the identification of potential customers in the competitive environment of their business by employing data mining in practice. It also provides a mechanism for the identification of potential customers liable to becoming real customers. Using Decision Tree tool, the main criteria are identified and their importance are determine din this paper and then assuming that each main criterion consists of several sub-criteria, their importance in turning potential customers into real ones is in turn determined. By utilizing the identified criteria and sub- criteria, organizations are able to drive selling processes in each attendance in a direction which results in attendants’ (future customers) purchase.

Keywords:

Data mining (DM) , Decision tree (DT) , Customer relationship management (CRM)