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Abstract
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Data mining and in particular forecasting tools and techniques are being increasingly exploited by businesses to predict
customer behavior and to formulate efective 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.
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