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Issue:A reply to Madera et al.'s "A method for optimizing a bidding strategy for online advertising through the use of intuitionistic fuzzy systems"

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Title of paper: A reply to Madera et al.'s "A method for optimizing a bidding strategy for online advertising through the use of intuitionistic fuzzy systems"
Author(s):
Jan Tappé
Faculty of Law and Business Sciences, Universidad Católica San Antonio de Murcia, 30107 Guadalupe, Spain
privat@jantappe.de
Hendrik Müller
Hochschule Fresenius onlineplus, 20148 Hamburg, Germany
hendrik.mueller@hs-fresenius.de
Published in: Notes on Intuitionistic Fuzzy Sets, Volume 28 (2022), Number 1, pages 46–50
DOI: https://doi.org/10.7546/nifs.2022.28.1.46-50
Download: Download-icon.png PDF (730  Kb, Info)
Abstract: In 2016, Madera et al. tested the performance of a fuzzy inference system against the Google Ads algorithms for optimizing the number of clicks, the click through rate (CTR) and the average cost per clicks to lower the cost of an advertising campaign [7]. The results of their experiments suggested that the implementation of their fuzzy inference system outperformed the Google Ads algorithms in terms of the obtained number of clicks and cost per clicks. While the research idea is with no doubts an interesting and valuable contribution to the fields of digital marketing research, in the opinion of the authors, their experimental setup was flawed. However, applying a few adjustments can lead to valid findings. This paper reflects on the flaws and suggests enhancements to correct them.
Keywords: Fuzzy logic, Online advertising, Google ads.
AMS Classification: 03B52
References:
  1. Adchieve. (2021). Coloring the black box: a new look at managing Smart Shopping campaigns. Searchengineland.com. Available online at: https://searchengineland.com/coloring-the-black-box-a-new-look-at-managing-smart-shopping-campaigns-351350.
  2. Bruwer, J. de W., & Haydam, N.E. (1996). Reducing bias in shopping mall-intercept surveys: The time-based systematic sampling method. South African Journal of Business Management, 27(1/2), 9–16.
  3. DeFusco, R., A., McLeavey, D. W., Pinto, J. E., & Runkle, D. E. (2007). Quantitative Investment Analysis Workbook. John Wiley & Sons, Inc., Hoboken, New Jersey.
  4. Google. (2022). Google Ads Help. About ad position and Ad Rank, support.google.com. Available online at: https://support.google.com/google-ads/answer/1722122.
  5. Google. (2022). Google Ads Help. About similar keywords in a Google Ads account, support.google.com. Available online at: https://support.google.com/google-ads/answer/7502501.
  6. Google. (2022). Google Ads Help. About Smart Bidding. support.google.com. Available online at: https://support.google.com/google-ads/answer/7065882.
  7. Madera, Q., Castillo, O., García-Valdez, M., Mancilla, A., Sotirova, E., & Sotirov, S. (2016). A method for optimizing a bidding strategy for online advertising through the use of intuitionistic fuzzy systems, Notes on Intuitionistic Fuzzy Sets, 22(2), 99–107.
  8. Mouselli, S., & Massoud, H. (2016). Common Biases In: Business Research. In J. M. Gómez, & S. Mouselli (Eds.), Modernizing the Academic Teaching and Research Environment: Methodologies and Cases in Business Research, Springer, Berlin, 97–109.
  9. Newport Public Services Board. (2017). Minutes Newport Public Services Board, newport.gov.uk. Available online at: https://www.newport.gov.uk/documents/One-Newport/PSB-Full-Papers-14.03.17.pdf.
  10. Roettgerding, M. (2018). Debunking Ad Testing Part 1: Statistical Significance. PPC Epiphany Blog. Available online at: https://www.ppc-epiphany.com/2018/10/23/debunking-ad-testing-part-1-statistical-significance/.
  11. Rudin, C., & Radin, J. (2019). Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From An Explainable AI Competition. Harvard Data Science Review, 1(2). Available online at: https://doi.org/10.1162/99608f92.5a8a3a3d.
  12. Tang, J. (2020). How Regulating the Cost of Positive and Negative Reviews Affects the Online Reviews and Their Impacts on Digital Platform Performance. PhD Dissertation thesis, Design and Innovation, Weatherhead School of Management, Case Western Reserve University, Cleveland, Ohio. Available online at: http://rave.ohiolink.edu/etdc/view?acc_num=case1594725636278695.
  13. Tsai, F. (2008). The Effects on the Sarbanes–Oxley Act on U.S. Merger Activity. Carroll Round Proceedings, The Carroll Round Georgetown University, II, Washington, 189–204.
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