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.
