Optimal selection of potential customer range through the union sequential pattern by using a response model

Abstract

Direct marketing is a common data mining application. Previous studies largely adopt the approach that, as the subjects for response model predictions, the entire customer population or filtered through certain attribute values is based on recommendations made from sales professionals. However, such methods may reduce response rates due to an oversized potential customer population, thus diminishing the accuracy of the prediction model. In resolve this problem, this work presents proposes a novel forecasting method that integrates the union sequential pattern with classification algorithms to facilitate the construction of customer response models. Based on use of a union sequential pattern, the potential customer size is established by identifying attributes with a high level of association. The prediction model is then constructed using classification algorithms such as support vector machines and logistic regression. Consequently, the problem involving the setting of range for potential customers can be solved, as well as the time spent on processing extended lists of customers during prediction. Finally, predicted potential Internet-phone customers and churning mobile-phone customers of a telecommunication company in Taiwan as are taken as an illustrative example, based on the proposed prediction model. The proposed method more accurately predicts potential customers than those of previous studies.