Optimizing Marketing Campaign Targeting Using UncertaintyBased Predictive Modelling Over the past decades, decision makers have increasingly relied on analytical methods in order to increase revenues and reduce costs. This is also the case for marketing, where predictive models have primarily been used to identify potential customers and measure the effects of marketing campaigns. In this paper, we introduce a methodology to optimally distribute a constrained marketing budget over a group of targets for which historical behavioral information is available, though where it is deemed infeasible to set up a trial campaign involving a control and test group as is the typical approach described by net lift modelling. Instead, so called swing clients are identified based on a notion of uncertainty following from any classification technique which can be trained over the historical data, which makes that our approach is easily applicable in most marketing environments. We report on the feasible of our approach by presenting a case study which was performed at the largest airport in Belgium.

Optimizing Customer Assignments to Direct Marketing Activities: A Binary Linear Programming Formulation Direct marketing has become a fundamental advertising method in many industries. In direct marketing, companies target specific customers with personalized product offers. By optimally assigning customers to direct marketing activities, the effectiveness of direct marketing campaigns can be greatly increased. In this paper, we study a realworld customer assignment problem of a leading telecommunications provider in Switzerland. The planning problem contains many business and customerspecific constraints that have not yet been covered in the literature. We propose a binary linear programming formulation that solves instances involving up to one million customers and over 100 direct marketing activities to optimality in short running time. The novel formulation delivers substantially better solutions in terms of expected profit than the current practice at the company.