Stochastic goal programming model and satisfaction functions for media selection and planning problem

In the knowledge society characterised by audience fragmentation and new media consumption, the success of a media campaign relies more on the effectiveness of the chosen media to achieve the desired aspiration levels. A media planner has limited financial resources and aims to get the best return on investment in terms of attention and engagement with potential customers, and at the same time to minimise total costs of advertising and communication. These objectives are conflicting, incommensurable and their evaluations are generally stochastic. In this paper, we present a stochastic multi-objective approach for media planning decision-making and we propose two different goal programming formulations with satisfaction function based on scenario forecasting and deterministic equivalent. The proposed models will be illustrated through a numerical simulation based on data from the Italian market.