Selection of customers for a message transmission system

The magnitude of data which has accumulated by Telecommunications Companyis so huge that manual analysis of data is not feasible. However, this data holds valuable information that can be applied for operational and strategical purposes. Therefore, in order to extract such information from this data, automatic analysis is essential, by means of advanced data mining techniques. This paper will address the question how to perform customer segmentation with data mining techniques. In our context, `customer segmentation' is a term used to describe the process of dividing customers into homogeneous groups on the basis of shared or common attributes. Having this component, managers can decide which marketing actions to take for each segment. In this paper, the customer segmentation is based on usage call behavior, i.e. the behavior of a customer measured in the amounts of incoming or outgoing communication of whichever form. A number of advanced and state-of-the-art clustering algorithms are applied for creating customer segments. An optimality criterion is used in order to measure their performance and to find the optimum algorithm. Once groups are made in segmentation, service message should not be sent to any group as unwanted customers could be harassed by such service message and they activate a `Do not Disturb' service. Sothere should be some selection procedure and from this selection procedure service message may be broadcast to one or combination of a group such that it satisfies company's strategical decision. This paper also represents such proper selection procedure.