A Multi-attribute Online Advertising Budget Allocation Under Uncertain Preferences

Main Article Content

Anu Gupta Aggarwal

Article Details

Research Articles


Introduction: The present research was conducted at the University of Delhi in 2017.

Method: Websites were ranked on the basis of feedback from unbiased experts. Later, we proposed an integrated approach by combining ordered weighted averaging (owa) operator with fuzzy analytic hierarchy process (fahp) for budget allocation.

Results: A numerical example related to a company, which deals with consumer goods and wants to advertise on few e-commerce websites is 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 managerial decisions made by handling multiple attributes simultaneously through industry experts’ opinion, and using a simple proportional rule for allocating budget.

Originality:  The conventional methods based on reach maximization, exposure or 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 method based on the advertising budget allocation method to divide 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 used a mdmv-owa operator in fuzzy environment but in future occasions, it may be extended to intuitionistic fuzzy domain. 

[1] K. Sruoginis, “Digital Usage Trends: Mid Year 2016” iab. New York, United States, June 2015 to June 2016. Available: http://www.iab.com/wp-content/ uploads/2016/11/Mid-Year-2016-Digital-UsageTrends-FINAL-DRAFT.pdf.

[2] R. Sadiq, M. J. Rodríguez, and S. Tesfamariam, “Integrating indicators for performance assessment of small water utilities using ordered weighted averaging (owa) operators”, Expert Systems with Applications, vol. 37, no. 7, pp. 4881-4891, 2010. doi: https:// doi.org/10.1016/j.eswa.2009.12.027

[3] J. M. Merigó and M. Casanovas, “Induced aggregation operators in the Euclidean distance and its application in financial decision making”, Expert Systems with Applications, vol. 38, no. 6, pp. 7603-7608. 2011, doi: https://doi.org/10.1016/j.eswa.2010.12.103

[4] G. E. Fruchter and W. Dou, “Optimal budget allocation over time for keyword ads in web portals”, Journal of optimization theory and applications, vol. 124, no. 1, pp. 157-174, 2005. doi: https://doi. org/10.1007/s10957-004-6470-0

[5] P. J. Danaher, J. Lee, and L. Kerbache, “Optimal internet media selection”, Marketing Science, vol, 29, no. 2, pp. 336-347, 2009. doi: https://doi.org/10.1287/ mksc.1090.0507

[6] Y. Yang, D. Zeng, Y. Yang, and J. Zhang, “Optimal budget allocation across search advertising markets”. informs Journal on Computing, vol. 27, no. 2, pp. 285-300, 2015. doi: https://doi.org/10.1287/ ijoc.2014.0626

[7] R. R. Yager, “On ordered weighted averaging aggregation operators in multicriteria decision making”, ieee Transactions on systems, Man, and Cybernetics, vol. 18, no. 1, pp. 183-190, 1988. doi: https://doi. org/10.1016/B978-1-4832-1450-4.50011-0

[8] R. R. Yager, “Families of owa operators”, Fuzzy sets and systems, vol. 59, no. 2, pp. 125-148, 1993. doi: https://doi.org/10.1016/0165-0114(93)90194-M

[9] D. Filev and R.R. Yager, “On the issue of obtaining owa operator weights”, Fuzzy sets and systems, vol. 94, no. 2, pp. 157-169, 1998. doi: https://doi. org/10.1016/S0165-0114(96)00254-0

[10] M. O’Hagan, “Aggregating Template or Rule Antecedents In Real-time Expert Systems With Fuzzy Set Logic”, Conference Record - Asilomar Conference on Circuits, Systems & Computers. 2, 1988, pp. 681-689. doi: https://doi.org/10.1109/ACSSC.1988.754637

[11] R. Fullér and P. Majlender, “An analytic approach for obtaining maximal entropy owa operator weights”, Fuzzy Sets and Systems, vol. 124, no. 1, pp. 53-57, 2001. doi: https://doi.org/10.1016/S01650114(01)00007-0

[12] S. Sivanandam and S. Deepa, Introduction to genetic algorithms. New York, usa: Springer Science & BusinessMedia, 2007. Available: https://xa.yimg. com/kq/groups/86541084/1097235234/name/ Introduction+to+Genetic+Algorithms+Springer+(2008),+354073189X.pdf.

[13] A. C. Briza and P. C. Naval, “Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data”, Applied Soft Computing, vol. 11, no. 1, pp. 1191-1201, 2011. doi: https://doi.org/10.1016/j. asoc.2010.02.017

[14] W. C. Yeh and M. C. Chuang, “Using multi-objective genetic algorithm for partner selection in green supply chain problems”, Expert Systems with applications, vol. 38, no. 4, pp. 4244-4253, 2011. doi: https:// doi.org/10.1016/j.eswa.2010.09.091

[15] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: nsga-ii”, ieee transactions on evolutionary computation, vol. 6, no. 2, pp. 182-197, 2002. doi: https://doi. org/10.1109/4235.996017

[16] C. K. Kwong and H. Bai, “Determining the importance weights for the customer requirements in qfd using a fuzzy ahp with an extent analysis approach”, iie Transactions, vol. 35, no. 7, pp. 619-626, 2003. doi: https://doi.org/10.1080/07408170304355

[17] F. T. Bozbura, A. Beskese, and C. Kahraman, “Prioritization of human capital measurement indicators using fuzzy ahp”, Expert Systems with Applications, vol. 32, no. 4, pp. 1100-1112, 2007. doi: https://doi. org/10.1016/j.eswa.2006.02.006

[18] D. Y. Chang, “Applications of the extent analysis method on fuzzy ahp”, European Journal of Operational Research, vol. 95, no. 3, pp. 649-655, 1996. doi: https://doi.org/10.1016/0377-2217(95)00300-2

[19] J. J. Wang and D. L. Yang, “Using a hybrid multi-criteria decision aid method for information systems outsourcing”, Computers & Operations Research, vol. 34, no. 12, pp. 3691-3700, 2007. doi: https://doi.org/10.1016/j.cor.2006.01.017

[20] A. Saaty, The analytic hierarchy process, New York: Mc Graw Hill, 1980.