Abstract

Defining a target group for a mass marketing campaign is a non-trivial goal, which depends on the correct definition of the commercial stimuli and the selection of a customer target segment that will maximize the campaign's effectiveness. This process requires the analysis of multiple customer variables and interactions. The problem becomes even more complex if we consider a limited budget for the campaign. This research proposes a methodology based on a mixed multi-objective optimization formulation that allows us to determine a minimum continuous customer target segment for massive campaigns to maximize its effectiveness with a maximum budget constraint. The multi-objective function of the model maximizes the effectiveness of the campaign while minimizing the "broadness"of the targeted segments, allowing the detection of the most effective and homogeneous target group possible fora commercial action within a set of N continuous variables. The methodology performance was benchmarked against traditional customer clustering and greedy segmentation algorithms. The experiments were carried out in (1) simulated data environments and (2) based on real campaign information. The compared scenarios show that the proposed methodology outperforms the baseline model, the complexity of the problem scales non-linearly, increasing the number of variables, and the model increases 54% the effectiveness of a campaign without an increment in the segment range.