MAMOM: Multicriteria Attribution Model for Online Marketing

Abstract

One of the problems facing online marketing in omni-channel environments is efficient budget allocation to each channel. The complexity of this problem is greater in omni-channel environments due to the greater number of channels and the need to analyze data sources containing user interaction information. To solve this problem, different attribution models have been proposed to assign the weight that each channel has in the acquisition of a product, also known as conversion. Each of these attribution models adopts a strategy to define the weighting of channels. The decision-making strategy is established using the company’s expert knowledge, which can contain different criteria depending on the department to which the expert belongs. The aim of this research is to present a new multicriteria attribution model for online marketing (MAMOM) based on Analytical Hierarchical Process, that resolves this type of problem. MAMOM is a meta-model that takes as input information related to channel features, user interactions and even decisions from other strategies. This information is integrated to enable the integration of interdepartmental strategies as well as to obtain a dynamic attribution model based on expert interviews. The expert assessment procedure is carried out subjectively and simply with a pairwise assessment of the criteria, finally MAMOM obtains a final formulation to calculate the investment to be made in each channel if the experts’ opinions are considered. Results show that first five channels selected by the MAMOM are nearly identical to those that would be obtained with the traditional models but with a different sequence caused by experts’ knowledge. This result shows the capacity of MAMOM to factor in expert opinion and experience for making the investment outcome more aligned with the tactical and strategic objectives of a given online marketing campaign.