Classification Technique And Its Application In Customer Relationship Management

The electronic data gathering devices have been wildly used and the data storage with large volumes have become easier and cheaper, which lead to a data explosion. However, in fact, we are drowning in data but starving for knowledge, which pulls the demands of powerful data analysis tools. The emergence of data mining provides strong technical support for the urgent need. Data Mining, also known as KDD (Knowledge Discovery in Database), is an advanced process, in which we can pick up hidden information to certain users from large volumes of data.Classification is a main task of the data mining. Classification is a process of finding the common features in the same type of data object from training datasets, building model, in order to ensure which class the data belongs to. However, the data used for classification contains hundreds of attributes, most of which are irrelevant or weakly relevant to the classification, so feature selection plays an important role in classification. Attribute selection is a process of selection a best subset (according to some criteria) from the dataset.This paper proposes a new future selection algorithm based on information gain and chi-square test. This algorithm is composed of two parts. First, we select futures by information gain. We get the attribute with higher value into next step. Second, we generate the final attributes set for data mining by filtering the attributes with lower value of chi-square.In the growing competitive telecom industry, how to reduce operational cost, to offer differentiated services, and to improve customer loyalty and satisfaction is substantially important. Under this circumstance, the application of customer relationship management could help telecom service operators to retain valuable customers, explore potential customers, win customer loyalty and finally achieve long-term customer value.At the end of this paper, we introduce a Customer Relationship Manager system of Zhejiang mobile named Churning Prediction Model. The new feature selection algorithm proposed by this paper was applied in this system, which shows that the algorithm is efficient, also has a high classification accuracy rate.