Uncertainty representation and risk management for direct segmented marketing

Abstract

Mining for truly responsive customers has become an integral part of customer portfolio management, and, combined with operational tactics to reach these customers, requires an integrated approach to meeting customer needs that often involves the application of concepts from traditionally distinct fields: marketing, statistics, and operations research. This article brings such concepts together to address customer value and revenue maximisation as well as risk minimisation for direct marketing decision-making problems under uncertainty. We focus on customer lift optimisation given the uncertainty associated with lift estimation models, and develop risk management and operational tools for the multiple treatment (recommendation) problem using stochastic and robust optimisation techniques. Results from numerical experiments are presented to illustrate the effect of incorporating uncertainty on the performance of recommendation models.

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