EMPIRICAL VALIDATION OF VALUE-BASED ADVERTISING AUTOMATION AND GENERATIVE ENGINE OPTIMIZATION (GEO): OVERCOMING THE "PERFORMANCE TRAP" AND THE "OBSCURITY TAX"

Authors

  • Vladyslav Bilinchuk Founder, ILION DIGITAL LLC, USA, Expert in Performance Marketing and Ecosystem Integration, Master of Business Management, Kyiv National University of Trade and Economics, Ukraine Author ORCID Icon https://orcid.org/0009-0009-8032-7639
  • Oleksandr Korogovnyi Managing Partner at iLION Digital Agency, USA, Expert in Digital Marketing, Master of Business Management, Kyiv National University of Trade and Economics, MIM-Kyiv Pre-MBA, Ukraine Author ORCID Icon https://orcid.org/0009-0009-9177-9892

DOI:

https://doi.org/10.69635/mssl.2026.2.3.52

Keywords:

Value-Based Advertising Automation, Generative Engine Optimization

Abstract

This study provides an empirical and theoretical investigation into the systemic inefficiencies confronting contemporary programmatic advertising architectures: namely, capital inflation driven by over-reliance on short-term attribution (the "Performance Trap") and the conversion friction imposed on non-established market entrants (the "Obscurity Tax"). We examine the structural realignment of information retrieval pipelines as consumer search behaviors migrate from legacy index-based search engine results pages toward conversational, Large Language Model (LLM) interfaces. Utilizing multi-channel Google Analytics 4 (GA4) telemetry, this paper quantifies the operational divergence between active brand equity and anonymous enterprises within identical commercial verticals.

To mitigate these barriers, we present a full-cycle data engineering architecture developed at iLION Digital that leverages server-side Google Tag Manager (GTM), Google BigQuery, and custom SQL identity resolution scripts to operationalize automated Value-Based Bidding (VBB) via direct API synchronization. A cross-vertical meta-analysis of five multi-market enterprise accounts across the E-commerce, Medical, and Home Services industries validates the scalability of this infrastructure, demonstrating an aggregate increase in inbound inquiries of 220.10%, a contraction in average Cost-Per-Acquisition (CPA) to 80.15% of historical baselines, a 225.42% expansion in total transactional value, and a net increase in Return on Investment (ROI) of 124.45%.

References

Acheampong, S., Pimonenko, T., & Lyulyov, O. (2023). Sustainable marketing performance of banks in the digital economy: The role of customer relationship management. Virtual Economics, 6(1), 22–39. https://doi.org/10.34021/ve.2023.06.01(2)

Aghaei, R., Kiaei, A. A., Boush, M., Vahidi, J., Zavvar, M., Barzegar, Z., & Rofoosheh, M. (2025). Harnessing the potential of large language models in modern marketing management: Applications, future directions, and strategic recommendations. arXiv. https://doi.org/10.48550/arXiv.2501.10685

Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2023). GEO: Generative engine optimization. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 412–426). https://doi.org/10.1145/3637528.3671900

Almestarihi, R., Ahmad, A., Frangieh, R. H., Abu-AlSondos, I., Nser, K. K., & Ziani, A. (2024). Measuring the ROI of paid advertising campaigns in digital marketing and its effect on business profitability. Uncertain Supply Chain Management, 12(1), 115–128. https://doi.org/10.5267/j.uscm.2023.11.009

Alwan, M., & Alshurideh, M. (2022). The effect of digital marketing on purchase intention: Moderating effect of brand equity. International Journal of Data and Network Science, 6(2), 441–452. https://doi.org/10.5267/j.ijdns.2022.2.012

Bottger, C., Khouja, T., Pohlmann, N., Demir, N., & Urban, T. (2026). From third-party to first-party: Measuring and protecting against modern web tracking mechanisms. Journal of Cyber Security and Privacy, 6(1), 89–104.

Breuer, T., Frihat, S., Fuhr, N., Lewandowski, D., Schaer, P., & Schenkel, R. (2025). Large language models for information retrieval: Challenges and chances. Datenbank-Spektrum, 25, 71–81. https://doi.org/10.1007/s13222-025-00503-x

Caramancion, K. M. (2024). Large language models vs. search engines: Evaluating user preferences across varied information retrieval scenarios. arXiv. https://doi.org/10.48550/arXiv.2401.05761

Case, B. M., Jain, R., Koshelev, A., Leiserson, A., Masny, D., Savage, B., Taubeneck, E., Thomson, M., & Yamaguchi, T. (2023). Interoperable private attribution: A distributed attribution and aggregation protocol. IACR Cryptology ePrint Archive, 2023, 437.

Chen, X., Wu, H., Bao, J., Chen, Z., Liao, Y., & Huang, H. (2025). Role-augmented intent-driven generative search engine optimization. arXiv. https://doi.org/10.48550/arXiv.2508.11158

Drobakha, A., & Zolotar, O. (2024). Psychological research in metaverses: The PersonaMatrix model. In Digitalization, metaverse, artificial intelligence in the context of human and individual rights protection in Ukraine and the world (pp. 329–342). Scientific Research Institute of Informatics and Law.

Dwivedi, Y. (2024). Revolutionizing digital marketing: The impact of generative AI automation in transforming digital marketing strategies. International Journal of Scientific Research in Engineering and Management, 8(3), 1102–1119. https://doi.org/10.55041/ijsrem37870

Fraihi, A. E., Amieur, N., Rudametkin, W., & Goga, O. (2024). Client-side and server-side tracking on Meta: Effectiveness and accuracy. Proceedings on Privacy Enhancing Technologies, 2024, 431–445. https://doi.org/10.56553/popets-2024-0086

Group, I. D. M. R., & Lead Institutional Researcher & Framework Designer. (2026). The AI-driven transformation of digital marketing: Evolving customer journeys, predictive analytics, and novel acquisition channels. Peer-Reviewed Journal of Digital Business Transformation and Marketing Science.

Gupta, R., Nair, K., Mishra, M., Ibrahim, B., & Bhardwaj, S. (2024). Adoption and impacts of generative artificial intelligence: Theoretical underpinnings and research agenda. International Journal of Information Management Data Insights, 4, 100232. https://doi.org/10.1016/j.jjimei.2024.100232

ILION DIGITAL. (2026). Proprietary architectural specification: Automated contextual advertising management infrastructure [Internal documentation].

Indrodiya, D. (2026). Generative engine optimization (GEO): A geospatial AI framework for local search discoverability. International Journal for Research in Applied Science and Engineering Technology, 14(2), 702–715. https://doi.org/10.22214/ijraset.2026.78271

Jha, A., Sharma, P., Upmanyu, R., Sharma, Y., & Tiwari, K. (2024). Machine learning-based optimization of e-commerce advertising campaigns. In Proceedings of the International Conference on Data Science and Engineering (pp. 531–541). https://doi.org/10.5220/0012456700003636

Jin, H., Chen, R., Zhang, P., Luo, Y., Luo, M., & Wang, H. (2026). Controlling output rankings in generative engines for LLM-based search. arXiv. https://doi.org/10.48550/arXiv.2602.03608

Kong, D., Shmakov, K., & Yang, J. (2022). Demystifying advertising campaign bid recommendation: A constraint target CPA goal optimization. arXiv. https://doi.org/10.48550/arXiv.2212.13915

Kostenko, O., Furashev, V., Zhuravlov, D., & Dniprov, O. (2022). Genesis of legal regulation web and the model of the electronic jurisdiction of the metaverse. Bratislava Law Review, 6(2), 21–36. https://doi.org/10.46282/blr.2022.6.2.316

Kostenko, O., Dniprov, O., Zhuravlov, D., Tykhomyrov, O., & Vladov, S. (2025). A new paradigm of metaverse philosophy: From anthropocentrism to metasubjectivity. Philosophies, 10(6), 117. https://doi.org/10.3390/philosophies10060117

Kostenko, O. V., Zhuravlov, D. V., Nikitin, V. V., Manhora, V. V., & Manhora, T. V. (2024). A typical cross-border metaverse model as a counteraction to its fragmentation. Bratislava Law Review, 8(2), 163–176. https://doi.org/10.46282/blr.2024.8.2.844

Kotler, P., & Keller, K. L. (2023). Marketing management (16th ed.). Pearson.

Kousar, I. (2025). A predictive models for advertisement campaign budget allocation. Kashmir Journal of Science, 11(2), 142–157. https://doi.org/10.63147/pyartw67

Krishnan, G. U. (2024). Engineering premium customer acquisition: A technical framework for value-based bidding implementation. International Journal for Multidisciplinary Research, 6(6), 1204–1219. https://doi.org/10.36948/ijfmr.2024.v06i06.30462

Melinevskyi, A. (2023). Digital marketing and its role in customer acquisition. Economic Affairs, 68(4), 2115–2124. https://doi.org/10.46852/0424-2513.4.2023.31

P, K., Wachasundar, S., Paulraj, K., Erudiyanathan, D., Dutta, C., & Ajaykumar, H. R. (2025). Generative AI-driven personalized ad content generation framework for e-commerce platforms. In 2025 International Conference on Recent Innovation in Science Engineering and Technology (ICRISET) (pp. 1–6). https://doi.org/10.1109/ICRISET64803.2025.11252460

Patel, C. (2026). Generative AI for personalized marketing and customer experience in e-commerce. International Journal of Emerging Research in Engineering and Technology, 7(1), 103–115. https://doi.org/10.63282/3050-922x.ijeret-v7i1p103

Pathak, M., & Musku, U. (2020). Dynamic bidding with contextual bid decision trees in digital advertisement. In Advances in Intelligent Systems and Computing (Vol. 1142, pp. 463–473). https://doi.org/10.1007/978-981-15-6634-9_42

Raji, M., Olodo, H. B., Oke, T. T., Addy, W. A., Ofodile, O. C., & Oyewole, A. (2024). E-commerce and consumer behavior: A review of AI-powered personalization and market trends. GSC Advanced Research and Reviews, 18(3), 90–105. https://doi.org/10.30574/gscarr.2024.18.3.0090

Raza, M., Jahangir, Z., Riaz, M. B., Saeed, M. J., & Sattar, M. A. (2025). Industrial applications of large language models. Scientific Reports, 15, 1102–1118. https://doi.org/10.1038/s41598-025-98483-1

Sinha, S. (2024). The new frontier of ad analytics: Privacy-centric approaches to campaign measurement and optimization. International Journal for Multidisciplinary Research, 6(6), 381–394. https://doi.org/10.36948/ijfmr.2024.v06i06.30381

Smith, A. J. (2024). The shift to conversational search: How generative AI restructures consumer intent. Journal of Interactive Marketing, 58, 112–129.

Swetha, K., Kumar, D. T., & Kanimozhi, D. P. (2025). AI-based advertisement optimization and performance analytics. Asian Journal of Applied Science and Technology, 9(2), 22–37. https://doi.org/10.38177/ajast.2025.9202

Wu, Y., Zhong, S., Kim, Y., & Xiong, C. (2025). What generative search engines like and how to optimize web content cooperatively. arXiv. https://doi.org/10.48550/arXiv.2510.11438

Yang, X., Li, Y., Wang, H., Wu, D., Tan, Q., Xu, J., & Gai, K. (2019). Bid optimization by multivariable control in display advertising. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2110–2119). https://doi.org/10.1145/3292500.3330681

Ye, F., Fang, M., Li, S., & Yilmaz, E. (2023). Enhancing conversational search: Large language model-aided informative query rewriting. arXiv. https://doi.org/10.48550/arXiv.2310.09716

Yendra, V. (2023). The role of digital marketing in improving company financial performance. Atestasi: Jurnal Ilmiah Akuntansi, 6(1), 142–156. https://doi.org/10.57178/atestasi.v6i1.867

Verma, S., Fadhil, M. K., Mahdi, S., Furaijl, H. B., Seedi, K. F. K. A., & Juad, J. (2026). Generative AI for personalized marketing content creation in e-commerce systems. In 2026 Innovations in Machine, Engineering, and Digital Conference (IMED) (pp. 1–6). https://doi.org/10.1109/IMED68921.2026.11484300

Zerhoudi, S., & Granitzer, M. (2025). SearchLab: Exploring conversational and traditional search interfaces in information retrieval. In Proceedings of the 2025 ACM SIGIR Conference on Human Information Interaction and Retrieval (pp. 112–126). https://doi.org/10.1145/3698204.3716475

Zhang, A., Deng, Y., Lin, Y., Chen, X., Wen, J., & Chua, T.-S. (2024). Large language model powered agents for information retrieval. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 551–566). https://doi.org/10.1145/3626772.3661375

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Published

2026-08-04

Issue

Section

Technological Foundations and Architectures

How to Cite

Bilinchuk, V., & Korogovnyi, O. (2026). EMPIRICAL VALIDATION OF VALUE-BASED ADVERTISING AUTOMATION AND GENERATIVE ENGINE OPTIMIZATION (GEO): OVERCOMING THE "PERFORMANCE TRAP" AND THE "OBSCURITY TAX". Metaverse Science, Society and Law, 2(3). https://doi.org/10.69635/mssl.2026.2.3.52

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