A new framework for predicting customer behavior in terms of RFM by considering the temporal aspect based on time series techniques

Data mining and in particular forecasting tools and techniques are being increasingly exploited by businesses to predict customer behavior and to formulate effective marketing programs. Conventionally, customer segmentation approaches are utilized when dealing with a large population of customers. Inspired by this idea, a new methodology is proposed in this study to perform segment-level customer behavior forecasting. To keep the dynamic nature of customer behavior, customer behavior is represented as a time series. Therefore, customer behavior forecasting is changed into a time series forecasting problem. The proposed methodology contains two main components i.e. clustering and forecasting. In the clustering phase, time series are clustered using time series clustering algorithms, and then, in the forecasting phase, the behavior of each segment is predicted via time series forecasting techniques. The main objective is to predict future behavior at segment level. The forecasting component also consists of a combined method exploiting the concept of forecast fusion. The combined method employs a pool of forecasters both from traditional time series forecasting and computational intelligence methods. To test the usefulness of the proposed method, a case study is carried out using the data of customers’ point of sale (POS) in a bank. The results of the experiments demonstrate that the combined method outperforms all other individual forecasters in terms of symmetric mean absolute percentage error (SMAPE). The proposed methodology can be correspondingly applied in other areas and applications of time series forecasting.