Estudio sobre el fraude de métricas publicitariasevolución, análisis y herramientas para la mitigación

  1. Maestro-Espínola, Lidia
  2. Cordón-Benito, David
  3. Abuin-Vences, Natalia
Revista:
Revista Mediterránea de Comunicación: Mediterranean Journal of Communication

ISSN: 1989-872X

Any de publicació: 2022

Volum: 13

Número: 1

Pàgines: 347-363

Tipus: Article

DOI: 10.14198/MEDCOM.20349 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Altres publicacions en: Revista Mediterránea de Comunicación: Mediterranean Journal of Communication

Resum

This study seeks to analyse fraud in those metrics serving as a reference value in the commercialisation of digital advertising spaces. Digital media need to optimise revenue and a major recourse is the business models based on advertising facing the phenomenon of fraud. This work focuses on analysis of the aspects that deter advertising investments, especially the problems that metrics fraud entails, and measures implemented to improve the transparency and quality of media like the advertising media. It is based on the idea that the control of metric fraud makes it possible to attract the attention of advertisers, improve advertising efficiency and optimise the benefits of digital media. A qualitative methodology afforded in-depth interviews to professionals in the sector who analyse the different types of fraud and the prevention strategies carried out by digital media. The results reveal inequality in the management of investment in digital media for advertising and a conservative vision.

Referències bibliogràfiques

  • [1] Association of National Advertisers. (n.d.-a). The bot baseline: Fraud in digital advertising. Relations Government and Advocacy, ANA Driving Worth. https://cutt.ly/4bh8cJU
  • [2] Association of National Advertisers. (2015). Making measurement make sense. Relations Government and Advocacy, ANA Driving Worth. https://cutt.ly/Ebh8Qws
  • [3] Bashir, M. A.; Arshad, S.; Kirda, E.; Robertson, W. & Wilson, C. (2019). A Longitudinal Analysis of the ads. txt Standard. In Proceedings of the Internet Measurement Conference (pp. 294-307). https://doi.org/ggfwf3
  • [4] Bourgeois, T. (2017). Techniques for Combating Digital Ad Fraud, Transparency, and Viewability. https://cutt.ly/ibh8UfI
  • [5] Bounie, D.; Quinn, M. & Valérie, M. (2016). Advertising Viewability in Online Branding Campaigns. SSRN, 1-15. https://doi.org/gzx2
  • [6] Braun, M. & Moe, W. M. (2013). Online Display Advertising: Modeling the Effects of Multiple Creatives and Individual Impression Histories. Marketing Science, 32(5), 753-767. https://doi.org/b4bf
  • [7] Callanan, S.; O’sullivan, P. J.; Stern, E. H.; Weir, R. C. & Willner, B. E. (2008). U.S. Patent Application No. 11/617,127. Washington, DC: U.S. Patent and Trademark Office.
  • [8] Callejo, P. (2016). Auditing Methodology to Asses the Quality of Online Display Advertising Campaigns. Trabajo Fin de Máster, Universidad Carlos III. https://cutt.ly/Jbh8FR5
  • [9] Callejo, P.; Cuevas, A.; Cuevas, R.; Esteban-Bravo, M.; Vidal-Sanz, J. M. (2020). Tracking Fraudulent and Low-Quality Display Impressions. Journal of Advertising, 49(3), 309-319. https://doi.org/gzx4
  • [10] Callejo, P.; Cuevas, R.; Cuevas, A. & Kotila, M. (2016). Independent auditing of online display advertising campaigns. In Proceedings of the 15th ACM Workshop on Hot Topics in Networks (pp. 120-126). https://doi.org/gz3q
  • [11] Carcelén, S.; Alameda, D. y Pintado, T. (2017). Prácticas, competencias y tendencias de la comunicación publicitaria digital: una visión desde la perspectiva de los anunciantes españoles. Revista Latina de Comunicación Social, 72, 1648-1669. https://doi.org/gz3r
  • [12] Casero-Ripollés, A. (2010). Prensa en internet: nuevos modelos de negocio en el escenario de la convergencia. El profesional de la información, 19(6), 595-601. https://doi.org/c4d9gc
  • [13] Cluley, R. (2017). The construction of marketing measures: The case of viewability. Marketing Theory, 18(3), 287-305. https://doi.org/gd4sdg
  • [14] Clapp, C. L. & DeFrancesco, B. C. (2014). U.S. Patent Application No. 14/084,444. Washington, DC: U.S. Patent and Trademark Office.
  • [15] Deloitte; Asociación de Medios de Información (2019). Claves de la Información. https://www.claves2020.com
  • [16] De Pelsmacker, P.; Geuens, M. & Anckaert, P. (2002). Media Context and Advertising Effectiveness: The Role of Context Appreciation and Context/Ad Similarity. Journal of Advertising, 31(2), 49-61. https://doi.org/gz3s
  • [17] Dörnyei, K. R. (2020). Marketing Professionals’ Views on Online Advertising Fraud. Journal of Current Issues & Research in Advertising, 42(2), 1-19. https://doi.org/gz3t
  • [18] Flosi, S.; Fulgoni, G.; & Vollman, A. (2013). If an Advertisement Runs Online And No One Sees It, Is It Still an Ad?: Empirical Generalizations in Digital Advertising. Journal of Advertising Research, 53(2), 192-199. https://doi.org/gz3v
  • [19] Fulgoni, G. M. (2016). Fraud in Digital Advertising: A Multibillion-Dollar Black Hole: How Marketers Can Minimize Losses Caused by Bogus Web Traffic. Journal of Advertising Research, 56(2), 122-125. https://doi.org/gz3w
  • [20] Garcí a-Santamarí a, J. V.; Pé rez-Serrano, M. J. y Maestro, L. (2016). Los clubs de suscriptores como nuevo modelo de financiació n de la prensa españ ola. El profesional de la información, 25(3), 395-403. https://doi.org/gj8czv
  • [21] Garrido, P.; Caerols, R. y Garcí a-Huertas, J. G. (2018). Estudio Delphi sobre la evolució n y perspectivas de la compra programática de publicidad en España. Doxa Comunicación, 27, 253-271. https://doi.org/gz3z
  • [22] Ghose, A. & Todri, V. (2015). Towards a Digital Attribution Model: Measuring Display Advertising Effects on Online Search Behavior. MIS Quarterly, 40(4), 889-910.
  • [23] Goldberg, S.; Kim, S.; Morales, M.; Voloshko, A.; Zacharczuk, D. & Cohen, C. (2019). U.S. Patent Application No. 16/271,534. Washington, DC: U.S. Patent and Trademark Office.
  • [24] Goldfarb, A. & Tucker, C. (2011). Online Display Advertising: Targeting and Obtrusiveness. Marketing Science, 30(3), 389-404. https://doi.org/c493jk
  • [25] Haider, C. M. R.; Iqbal, A.; Rahman, A. H. & Rahman, M. S. (2018). An ensemble learning based approach for impression fraud detection in mobile advertising. Journal of Network and Computer Applications, 112, 126-141. https://doi.org/gz32
  • [26] Hill, D. N.; Moakler, R.; Hubbard, A. E.; Tsemekhman, V.; Provost, F. & Tsemekhman, K. (2015). Measuring causal impact of online actions via natural experiments: Application to display advertising. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1839-1847). https://doi.org/gz33
  • [27] IAB Tech Lab (2017). About Ads.txt. https://cutt.ly/Wbh8V5J
  • [28] ISBA (2020). Programmatic Supply Chain Transparency. https://cutt.ly/lbh81xq
  • [29] Iglesias, L. J. D. (2017). Soy marca: Quiero trabajar con influencers. Barcelona: Profit Editorial.
  • [30] Integral Ad Science (2021). Informe sobre la calidad de medios. Barómetro global. https://cutt.ly/9mhi6Cs
  • [31] Jansen, B. J. (2007). Click Fraud. IEEE Computer, 40(7), 85–86. https://doi.org/b5sc46
  • [32] Liu, B.; Nath, S.; Govindan, R. & Liu, J. (2014). DECAF: Detecting and characterizing ad fraud in mobile apps. In 11th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 14) (pp. 57-70). https://cutt.ly/Ybh807l
  • [33] Maestro, L.; Cordón, D. y Abuín, N. (2018). La comunicación publicitaria en entornos digitales: herramientas para garantizar la reputación corporativa. Prisma Social, 22(3), 209-228. https://bit.ly/3DsWjKn
  • [34] Mason, Z. (2008). U.S. Patent No. 7,401,130. Washington, D. C.: U.S. Patent and Trademark Office.
  • [35] Media Rating Council (2014). MRC Viewable ad impression Mesaurement Guidelines. https://cutt.ly/xbh86h8
  • [36] Merriman, D. A. & O’connor, K. (2006). U.S. Patent No. 7,039,599. Washington, D. C.: U.S. Patent and Trademark Office.
  • [37] Miralles, L. y Ponce-Espinosa, H. (2015). Predicción del CTR de los anuncios de Internet usando redes orgánicas artificiales. Res. Comput. Sci., 93, 23-32. https://doi.org/gz37
  • [38] Miralles, L.; Qureshi, M. A. & Mac Namee, B. (2019). Real-time Bidding campaigns optimization using attribute selection. arXiv, 1-24. https://cutt.ly/tbh4gnT
  • [39] Nicholas, C. (2020). Alcanzar la transparencia en programática. Integral Ad Science. https://cutt.ly/fbh4lgS
  • [40] Nandini, C. P. (2019). Detecting and Preventing Click Fraud: The Economic and Legal Aspects. IUP Law Review, 9(2). https://bit.ly/3lvduVC
  • [41] Nelson-Field, K. (2020). The Evolution of Media Buying. In The Attention Economy and How Media Works. In The Attention Economy and How Media Works (pp. 55-70). Singapur: Palgrave Macmillan.
  • [42] Otero, J. M. y Miralles, L. (2014). Fraudes en la publicidad en internet: tipología y tratamiento jurídico. Revista Aranzadi de Derecho y Nuevas Tecnologías, 34, 67-90. https://bit.ly/3jarJ0z
  • [43] Pixalate (2019). App-ads.txt & ads.txt Trends Report Q3 2019. https://cutt.ly/Pbh4WaC
  • [44] Rosen, J. (2015). Viewability: An Exaggerated Crisis. Syracuse University Honors Program Capstone Projects, 8, 1-54. https://bit.ly/3AwjOAf
  • [45] Scharber, J. & Pugh, R. (2017). U.S. Patent Application No. 14/811,319. Washington, DC: U.S. Patent and Trademark Office.
  • [46] Storey, G.; Reisman, D.; Mayer, J. & Narayanan, A. (2017). The future of ad blocking: An analytical framework and new techniques. arXiv, 1-17. https://cutt.ly/7bjrYrM
  • [47] Wang, C.; Kalra, A.; Zhou, L.; Borcea, C. & Chen, Y. (2017). Probabilistic Models for Ad Viewability Prediction on the Web. IEEE Transactions on Knowledge and Data Engineering, 29(9), 2012-2025. https://doi.org/gbr96b
  • [48] Wasef, A. (2017). Monetising digital audiences: Turning a marketing cost into a profitable business unit. Journal of Brand Strategy, 6(2), 171-180. https://cutt.ly/9bjrAxj
  • [49] Zenith. (2019). Programmatic Marketing Forecast. https://cutt.ly/mbjrDTx
  • [50] Zhang, W.; Pan, Y.; Zhou, T. & Wang, J. (2015). An Empirical Study on Display Ad Impression Viewability Measurements. arXiv. https://cutt.ly/AbjrGXD
  • [51] Zhu, X.; Tao, H.; Wu, Z.; Cao, J.; Kalish, K. & Kayne, J. (2017). Ad Ecosystems and Key Components. In Fraud Prevention in Online Digital Advertising (pp. 7-18). Nueva York: Springer.