Constrained optimization of data-mining problems to improve model performance: A direct-marketing application

Although most data-mining (DM) models are complex and general in nature, the implementation of such models in specific environments is often subject to practical constraints (e.g. budget constraints) or thresholds (e.g. only mail to customers with an expected profit higher than the investment cost). Typically, the DM model is calibrated neglecting those constraints/thresholds. If the implementation constraints/thresholds are known in advance, this indirect approach delivers a sub-optimal model performance. Adopting a direct approach, i.e. estimating a DM model in knowledge of the constraints/thresholds, improves model performance as the model is optimized for the given implementation environment. We illustrate the relevance of this constrained optimization of DM models on a direct-marketing case, i.e. in the field of customer relationship management. We optimize an individual-level response model for specific mailing depths (i.e. the percentage of customers of the house list that actually receives a mail given the mailing budget constraint) and compare its predictive performance with that of a traditional response model, neglecting the mailing depth during estimation. The results are in favor of the constrained-optimization approach.