Fuzzy multi-objective modeling of effectiveness and user experience in online advertising

The focus placed on maximizing user engagement in online advertising negatively affects the user experience because of advertising clutter and increasing intrusiveness. An intelligent decision support system providing balance between user experience and profits from online advertising based on the fuzzy multiobjective optimization model is presented in this paper. The generalized mathematical model uses uncertain parameters for content descriptors that are difficult to be precisely defined and measured, such as the level of intrusiveness and the change in performance over time. The search for final decision solutions and the verification of the proposed model are based on experimental results from both perceptual studies, which are evaluating visibility and intrusiveness of marketing content as well as online campaigns providing interaction data for estimation of effectiveness. Surprisingly, the online response to the most noticeable advertisements with highly perceived visibility and intrusiveness was relatively low. During the field study performed in order to compute the model parameters, the best results were achieved for advertising content with moderate visual influence on web users. Simulations with the proposed model revealed that a growing level of persuasion can increase results only to a certain extent. Above a saturation point, a strategy based on extensive visual effects, such as high-frequency flashing, resulted in a very high increase of intrusiveness and a slightly better performance in terms of acquired interactions. Proposed balanced content design with the use of intelligent decision support system creates directions towards sustainable advertising and a friendlier online environment. © 2016 Elsevier Ltd. All rights reserved.