Customer's class transformation for profit maximization in multi-class setting of Telecom industry using probability estimation decision trees.

Telecom sector is hugely losing profits in different degrees due to various undesired classes of its customers. Churners, a certain class of customers shifting to the competitors, are the most undesired class of customers who are the predominant reason for the losses. Still, there are other classes of customers in this business who stay with the enterprise, but they are inactive in using the services and leading to uncertainty and an insignificant amount of profits. When data mining techniques are applied to such applications they produce customer models in the form of decision trees, etc. and provide customer's class label only such as churner/non-churner. Furthermore, they only focus on improving the technical interestingness measures of prediction models. Thus, very limited research has been carried out on turning the prediction results into useful decision making actions. Consequently, some manual work by domain expert has to be done to postprocess the model to obtain the actionable knowledge for changing the customer from undesired class to the desired one. However, some of the existing works are suggesting the actions to convert the class of the customer from one category to another, but they have limitations in that they do not generalize to more than two classes. In this paper, a novel algorithm, which aptly fits the multi-class setting of Telecom sector, is presented that suggest actions to change the customer from an undesired class to a desirable one with maximum net profit. We explain our proposed method with the help of a case study of the Telecom sector. Empirical tests are conducted on the case study problem and also on UCI benchmark data and shown that our method is effective and scalable. With the help of comparison with state-of-the-art methods and substantial experiments, we demonstrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]