A Multi-attribute Online Advertising Budget Allocation Under Uncertain Preferences

Anu Gupta Aggarwal, Aakash .


Introduction: The research "Optimal advertising planning for online medias" was conducted at University of Delhi in 2017.

Methods: Here, we rank the websites on the basis of feedback from unbiased experts. Later, we propose an integrated approach by combining ordered weighted averaging (OWA) operator with fuzzy analytic hierarchy process (FAHP) for budget allocation.

Results: A numerical illustration related with a company, which deals in consumer goods and wants to advertise on few e-commerce websites has been discussed at the end of the paper. Budget distribution is decided by solving multi-objective maximum-dispersion-minimum-variance (MDMV) OWA and FAHP method. Conclusions: The proposed methodology aids in managerial decisions making by handling multiple attribute simultaneously through industry experts' opinion, and using a simple proportional rule for allocating budget.

Originality:  The conventional methods based on maximization of reach, exposure or the profit cannot meet the budget allocation needs of the modern advertising planning. Firstly, they do not take into consideration multiple attributes of media. Secondly, they do not incorporate the expert opinion and their preferences. To address these problems, we propose a multi-attribute based advertising budget allocation method for dividing the budget into individual websites. The attributes under consideration are system quality, content quality, usage, trust, customer support, online customer feedback, and personalization.

Limitations: In this study, we have used a MDMV-OWA operator in fuzzy environment but definitely in future, it may be extended to intuitionistic fuzzy domain.


budget allocation; electronic commerce; fuzzy analytic hierarchy process; maximum-dispersion; minimum variance;


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