Abstract
Customer relationship management (CRM) leverages historical users’ behaviors to dawn effort of enhancing customer satisfaction and loyalty. Thus, constructing a successful customer profile plays a critical role in CRM. In this study, we are expected to predict the repurchase rates for the registered members at the specific category of e-shop. However, customers’ preferences change over time. To capture the preference drifts of the members, we propose a novel and simple time function to increase/decrease the weight of the old data in evaluating various members’ past behaviors. Then, we construct a repurchase index with time factor (RIT) model to effectively predict repurchase rates. The marketers of e-shop can thus target the members with high repurchase rates. Experimental results with a real dataset have demonstrated that this RIT model can be practically implemented and provide satisfactory results.
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This research was supported by National Science Council, Taiwan, under grant Nos. NSC 101-2221-E-032-050.
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Communicated by V. Loia.
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Chen, CH., Chiang, RD., Wang, YH. et al. Prediction of members’ repurchase rates with time weight function. Soft Comput 17, 1711–1723 (2013). https://doi.org/10.1007/s00500-013-0987-9
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DOI: https://doi.org/10.1007/s00500-013-0987-9