Abstract :
As the digital marketing landscape evolves towards a cookieless future, advertisers must pivot to new technologies for user tracking and ad personalization. This white paper delves into Conversions API and other signal resilient advertising products that promise to maintain and enhance ad targeting capabilities without infringing on user privacy. We explore the technical underpinnings, industry applications, and best practices for integrating these tools, presenting a comprehensive guide for businesses looking to thrive in a post-cookie world. Through expert insights and actionable strategies, this paper offers a blueprint for advertisers to navigate this paradigm shift and leverage emerging technologies to gain a competitive edge.
Keywords :
Ad Targeting, Advertisers, Conversions API, Cookieless Future, Digital Marketing Landscape, Signal Resilient Advertising Products, User Privacy.References :
- Hormozi, A. M. (2005). Cookies and Privacy. Information Systems Security, 13(6), 51–59. [CrossRef].
- Queiroz, A., & De Queiroz, R. J. G. B. (2010). Breach of internet privacy through the use of cookies. Association for Computing Machinery New York, NY, United States. [CrossRef]
- Svantesson, D. (2007). Protecting privacy on the ‘borderless’ internet: Some thoughts on extraterritoriality and transborder data flow. The Bond Law Review, 19, 168-187. [CrossRef]
- Kristol, D. (2001). HTTP Cookies: Standards, privacy, and politics. ACM Trans. Internet Techn., 1, 151-198. [CrossRef]
- Gervais, N. (2014). Governmental internet information collection: Cookies placing personal privacy at risk. , 40, 27-31.[CrossRef]
- Whitman, M., Perez, J., & Beise, C. (2001). A Study of User Attitudes toward Persistent Cookies. Journal of Computer Information Systems, 41, 1 – 7. [CrossRef]
- Wagner, P. (2020). Cookies: Privacy Risks, Attacks, and Recommendations. Social Science Research Network. [CrossRef]
- Coventry, L., Jeske, D., Blythe, J., Turland, J., & Briggs, P. (2016). Personality and Social Framing in Privacy Decision-Making: A Study on Cookie Acceptance. Frontiers in Psychology, 7. [CrossRef]
- Dwork, C., & Roth, A. (2014). The Algorithmic Foundations of Differential Privacy. Trends Theor. Comput. Sci., 9, 211-407. [CrossRef]
- Facebook for Business. (n.d.). Conversions API.[CrossRef]
- Facebook Developers. (n.d.). Conversions API Overview. [CrossRef]
- Facebook Developers. (n.d.). Conversions API Reference. [CrossRef]
- Grewal, J., & Kharif, O. (2020, September 20). Facebook Says Apple’s iOS 14 Changes Could Hurt Audience Network. Bloomberg. [CrossRef]
- Peterson, T. (2020, October 6). How Facebook’s Conversions API could be used to track ad performance. Digiday. [CrossRef]
- Statt, N. (2020, September 21). Facebook warns advertisers Apple’s iOS 14 update will halve Audience Network revenue. The Verge. [CrossRef]
- Cahn, A., Alfeld, S., Barford, P., & Muthukrishnan, S. (2016, April 11). An Empirical Study of Web Cookies. [CrossRef]
- Karaj, A., Macbeth, S., Berson, R., & Pujol, J M. (2018, January 1). WhoTracks .Me: Shedding light on the opaque world of online tracking. Cornell University. [CrossRef]
- Nikiforakis, N., Kapravelos, A., Joosen, W., Kruegel, C., Piessens, F., & Vigna, G. (2013, May 1). Cookieless Monster: Exploring the Ecosystem of Web-Based Device Fingerprinting.[CrossRef]
- Saito, T., & Koshiba, R. (2019, June 19). Examination and Comparison of Countermeasures Against Web Tracking Technologies. Springer Nature, 477-489. [CrossRef]
- (2020, June 25). The Future of Measurement in a Cookieless World. [CrossRef]
- Estrada-Jiménez, J., Parra-Arnau, J., Rodríguez-Hoyos, A., & Forné, J. (2017, March 5). Online advertising. [CrossRef]
- Schmelzer, R. (2020, June 18). AI Makes A Splash In Advertising. [CrossRef]
- Han, Q., Lucas, C., Aguiar, E C., Macedo, P., & Wu, Z. (2023, June 12). Towards privacy-preserving digital marketing: an integrated framework for user modeling using deep learning on a data monetization platform. [CrossRef]
- Balayan, A A., & Томин, Л В. (2020, April 1). The Transformation of the Advertising Industry in the Age of “Platform Capitalism”. [CrossRef]
- Johnson, G., Runge, J., & Seufert, E B. (2022, March 30). Privacy-Centric Digital Advertising: Implications for Research. [CrossRef]
- Diemert, E., Fabre, R., Gilotte, A., Jia, F., Leparmentier, B., Mary, J., Qu, Z., Tanielian, U., & Ye, H. (2022, January 1). Lessons from the AdKDD’21 Privacy-Preserving ML Challenge. [CrossRef]
- Becker, D., Guajardo, J., & Zimmermann, K. (2017, October 1). Towards a new privacy-preserving social media advertising architecture (invited position paper). [CrossRef]
- Privacy and Tracking in a Post-Cookie World.(2014, January) [CrossRef]
- O’Brien, C C., Thiagarajan, A., Das, S., Barreto, R F., Verma, C., Hsu, T., Neufield, J., & Hunt, J J. (2022, January 1). Challenges and approaches to privacy preserving post-click conversion prediction. [CrossRef]
- Ullah, I., Boreli, R., & Kanhere, S S. (2020, November 4). Privacy in targeted advertising: A survey. [CrossRef]
- Estrada-Jiménez, J., Parra‐Arnau, J., Rodríguez-Hoyos, A., & Forné, J. (2017, March 1). Online advertising: Analysis of privacy threats and protection approaches. [CrossRef]
- Pooranian, Z., Conti, M., Haddadi, H., & Tafazolli, R. (2021, January 1). Online Advertising Security: Issues, Taxonomy, and Future Directions. [CrossRef]
- Hardt, M., & Nath, S. (2012, October 16). Privacy-aware personalization for mobile advertising.[CrossRef]
- Puglisi, S., Rebollo‐Monedero, D., & Forné, J. (2017, February 19). On web user tracking of browsing patterns for personalised advertising. [CrossRef]
- Chester, J., & Montgomery, K. (2017, December 31). The role of digital marketing in political campaigns. [CrossRef]
- Greengard, S. (2012, August 1). Advertising gets personal. [CrossRef]