A lift curve, with the true positive rate on the y-axis and the customer pull (or contact) rate on the x-axis, is often used to depict the model performance in many data mining applications, especially in the area of customer relationship management (CRM). Typically, these applications concern only the model accuracy at a relatively small pull or contact/intervention rate of the whole customer base, which is predetermined by a budget constraint for the project, e.g., how many customers can be contacted every month. In this paper, we address the important problem of enhancing the lift (true positive rate) at a specified pull rate. We propose two distinct algorithms, which are applicable to different scenarios. In particular, when the binary class label of the training set is extracted from a continuous variable, we can optimize a training objective which takes into account the specified pull rate rather than the class prior, based on the often ignored continuous variable. In those cases where onlythe binary class label is available during training, we propose a constrained optimization algorithm to maximize the true positive rate related to a specific decision threshold at which the specified pull rate is achieved. We applied both algorithms to our projects of predicting defection (decline in account value) of mutual fund accounts for two major U.S. mutual fund companies and achieved substantial enhancement of the lift at the specified pull rate.
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