Actual problems of innovative economy and law

Journal "Actual problems of innovative economy and law" is included in category B for specialties: in the field of knowledge "Management and administration": 073, 076 (order of the Ministry of Education and Science of Ukraine dated 23.08.2023 No. 1035) and 071, 072, 075 (order of the Ministry of Education and Science of Ukraine dated 12.20.2023 No. 1543); in the field of knowledge "Social and behavioral sciences" 051 (order of the Ministry of Education and Science of Ukraine dated August 23, 2023 No. 1035); in the field of knowledge "Law" - 081 and "Public management and administration" - 281 (order of the Ministry of Education and Science of Ukraine dated 12.20.2023 No. 1543).
Registration of an entity in print media: Decision of the National Council of Ukraine on Television and Radio Broadcasting No. 1390 dated 11/16/2023. Media identifier: R30-02018
The journal is indexed in the International Scientific Center of Index Copernicus International

Mechanisms for optimizing the marketing budget of international IT companies based on programmatic solutions and PPC advertising

УДК: 339.138:004

DOI: https://doi.org/10.36887/2524-0455-2026-2-53

Volikov Volodymyr V,
PhD in Economics,
Institute for Single Crystals of National Academy of Sciences of Ukraine, Kharkiv, Ukraine,
https:// orcid.org/0000-0002-4107-6277
Shumilkina Kateryna Yu.,
Leading expert,
Kitrum LLC, Tampa, FL, USA,
https:// orcid.org/0009-0003-6941-4367

Published: 31.05.2026


The purpose of this article is to develop and provide a scientific rationale for the author’s mechanism for optimizing the marketing budget of international IT companies, based on the integration of SEO, content marketing, landing pages, PPC advertising, programmatic solutions, CRM, and a system for evaluating the effectiveness of marketing communications. A study of theoretical approaches to optimizing the marketing budgets of international IT companies has shown that the specific nature of the industry (a long B2B cycle, high customer acquisition costs, the use of targeted marketing for key clients, geographic expansion, and omnichannel strategies) requires a shift toward a systematic allocation of investments across digital channels. Such allocation should be based on end-to-end analytical data and automated traffic management algorithms. The synergy between using PPC advertising to quickly attract controlled, targeted traffic and programmatic technologies for automated media buying management and algorithmic real-time bid optimization has been demonstrated. The comprehensive integration of these tools significantly increases the effectiveness of international IT companies’ marketing budgets. The synergy between using PPC advertising to quickly attract targeted traffic and programmatic technologies for automated media buying and real-time algorithmic bid optimization has been proven. The comprehensive integration of these tools significantly increases the effectiveness of international IT companies’ marketing budgets. Based on an analysis of Kitrum’s open-architecture website, the company’s digital presence and content structure were evaluated. It was determined that well-developed expert content, practical case studies, differentiation by industry segments, and optimized landing pages create favorable conditions for integrating SEO, PPC, content marketing, and programmatic technologies. The main scientific result is the development of a mechanism for optimizing the marketing budgets of international IT companies, which—unlike existing approaches—ensures end-to-end integration of the stages of the digital conversion funnel and automated media buying tools into a single system of adaptive resource management. The practical value of this work lies in the formulation of applied recommendations for integrating digital tools (SEO, PPC, Programmatic, CRM) into a unified system for adaptive management of the marketing budgets of international IT companies based on the results of continuous monitoring of key performance indicators. A limitation of this work is the use of only publicly available company data without access to its internal CRM and advertising analytics, which limits the ability to accurately quantify the economic impact of the proposed mechanism. Further research will focus on validating the mechanism using internal company data, mathematically modeling the adaptive allocation of budgets across digital channels, and implementing artificial intelligence tools to automate ROAS forecasting.

Keywords: digital marketing, online traffic, conversion, programmatic, PPC.

References.

  1. Alessandro Nuara, F., Trovò, F., Gatti, N., & Restelli, M. (2022). Online joint bid/daily budget optimization of Internet advertising campaigns. Artificial Intelligence, 305, Article 103663. https://doi.org/10.1016/j.artint.2022.103663
  2. Ghoshal, A., Mookerjee, R., & Sun, Z. (2023). Serving two masters? Optimizing mobile ad contracts with heterogeneous advertisers. Production and Operations Management, 32(2), 618–636. https://doi.org/10.1111/poms.13890
  3. Yang, Y., & Zhai, P. (2022). Click-through rate prediction in online advertising: A literature review. Information Processing & Management, 59(2), Article 102853. https://doi.org/10.1016/j.ipm.2021.102853
  4. Hosseini, L., Tang, S., & Mookerjee, V. (2024). When Is More Merrier? A Cloud-Based Architecture to Procure Impressions from Multiple Ad Exchanges. Information Systems Research, 35(1), 294–317. https://doi.org/10.1287/isre.2023.1221
  5. Choi, W. J., & Sayedi, A. (2023). Open and Private Exchanges in Display Advertising. Marketing Science, 42(3), 451–475. https://doi.org/10.1287/mksc.2022.1399
  6. Frick, T. W., Belo, R., & Telang, R. (2023). Incentive Misalignments in Programmatic Advertising: Evidence from a Randomized Field Experiment. Management Science, 69(3), 1665–1686. https://doi.org/10.1287/mnsc.2022.4438
  7. D’Annunzio, A., & Russo, A. (2024). Intermediaries in the Online Advertising Market. Marketing Science, 43(1), 33–53. https://doi.org/10.1287/mksc.2023.1435
  8. Zhu, W., Tang, S., & Mookerjee, V. (2025). Should Ad Exchanges Subsidize Advertisers to Acquire Targeting Data? Information Systems Research, 36(3), 1502–1521. https://doi.org/10.1287/isre.2023.0126
  9. Wang, T., Yang, H., Liu, Y., Yu, H., & Song, H. (2023). A multimodal approach for improving market price estimation in online advertising. Knowledge-Based Systems, 266, Article 110392. https://doi.org/10.1016/j.knosys.2023.110392
  10. Wang, X., Guo, Y., Tan, B., Yang, T., Huang, D., Xu, L., Zhou, H., & Li, X. (2024). Follow the LIBRA: Guiding Fair Policy for Unified Impression Allocation via Adversarial Rewarding. Proceedings of the 17th ACM International Conference on Web Search and Data Mining (WSDM ’24), 750–759. https://doi.org/10.1145/3616855.3635756
  11. Zhyhalkevych, Zh. M., & Plysenko, H. P. (2025). Metryky efektyvnosti investytsii u marketynh: vid ROAS do NPV [Marketing investment performance metrics: From ROAS to NPV]. Ekonomichnyi Visnyk NTUU “Kyivskyi Politekhnichnyi Instytut”, (32), 119–123. https://doi.org/10.20535/2307-5651.32.2025.328552
  12. Grigas, P., Lobos, A., Wen, Z., & Lee, K.-C. (2026). Optimal Bidding, Allocation, and Budget Spending for a Demand-Side Platform With Generic Auctions. Production and Operations Management, 35(3), 817–835.
  13. Agrawal, N., Najafi-Asadolahi, S., & Smith, S. A. (2023). A Markov Decision Model for Managing Display-Advertising Campaigns. Manufacturing & Service Operations Management, 25(2), 489–507. https://doi.org/10.1287/msom.2022.1142
  14. Agrawal, N., Najafi-Asadolahi, S., & Smith, S. A. (2025). Dynamic Pricing and Bidding for Display Advertising Campaigns. Manufacturing & Service Operations Management, 27(3), 843–861. https://doi.org/10.1287/msom.2023.0600
  15. Li, X., Rong, Y., Zhang, R., & Zheng, H. (2025). Online Advertisement Allocation Under Customer Choices and Algorithmic Fairness. Management Science, 71(1), 825–843. https://doi.org/10.1287/mnsc.2021.04091
  16. Moorthy, S., & Shahrokhi, S. (2023). Targeting Advertising Spending and Price on the Hotelling Line. Marketing Science, 42. https://doi.org/10.1287/mksc.2022.1422
  17. Ye, Z., Zhang, D. J., Zhang, H., Zhang, R., Chen, X., & Xu, Z. (2023). Cold Start to Improve Market Thickness on Online Advertising Platforms: Data-Driven Algorithms and Field Experiments. Management Science, 69(7), 3838–3860. https://doi.org/10.1287/mnsc.2022.4550
  18. Terho, H., Mero, J., Siutla, L., & Jaakkola, E. (2022). Digital content marketing in business markets: Activities, consequences, and contingencies along the customer journey. Industrial Marketing Management, 105, 294–310. https://doi.org/10.1016/j.indmarman.2022.06.006
  19. Hayes, Ó., & Kelliher, F. (2022). The emergence of B2B omni-channel marketing in the digital era: A systematic literature review. Journal of Business & Industrial Marketing, 37(11), 2156–2168. https://doi.org/10.1108/JBIM-02-2021-0127
  20. Mora Cortez, R., & Hidalgo, P. (2022). Prioritizing B2B marketing capabilities: Crossvergence in advanced and emerging economies. Industrial Marketing Management, 105, 422–438. https://doi.org/10.1016/j.indmarman.2022.07.002
  21. Severini, S., Terho, H., Mero, J., & Cardinali, S. (2026). Unpacking account-based marketing: Conceptualization, key activities, and performance outcomes. Industrial Marketing Management, 133, 148–161. https://doi.org/10.1016/j.indmarman.2026.02.006
  22. Alonso-Garcia, J., Pablo-Marti, F., Núñez-Barriopedro, E., & Cuesta-Valiño, P. (2023). Digitalization in B2B marketing: Omnichannel management from a PLS-SEM approach. Journal of Business & Industrial Marketing, 38(2), 317–336. https://doi.org/10.1108/JBIM-09-2021-0421
  23. Volikov, V. V., & Shumilkina, K. Yu. (2025). Marketynhove obhruntuvannia roli UX/UI-dyzainu u formuvanni konversiinoi spromozhnosti veb-resursiv mizhnarodnykh IT-kompanii [Marketing justification of the role of UX/UI design in forming the conversion capacity of web resources of international IT companies]. Business Inform, (12), 527–536.
  24. Volikov, V., & Shumilkina, K. (2026). Vplyv stratehichnoho SEO na tsyfrovu vydymist mizhnarodnykh IT-kompanii u systemi marketynhovykh komunikatsii [The impact of strategic SEO on the digital visibility of international IT companies in the marketing communications system]. Herald of Khmelnytskyi National University. Economic Sciences, 350(1), 579–588. https://doi.org/10.31891/2307-5740-2026-350-79
  25. Volikov, V. V., & Shumilkina, K. Yu. (2026). Stratehichne upravlinnia kontent-marketynhom yak chynnyk pidvyshchennia rynkovoi vartosti brendu: ekonomichnyi aspekt [Strategic management of content marketing as a factor in increasing the market value of a brand: Economic aspect]. Ukrainian Journal of Applied Economics and Technology, (1), 304–310. https://doi.org/10.36887/2415-8453-2026-1-57
  26. (2026). Microsoft Ads (Bing) Statistics 2026: CPC, CTR, Market Share & Benchmarks. Searchlab. https://searchlab.nl/en/statistics/microsoft-ads-statistics-2026
  27. Mironov, D. (2026). Bing Ads vs. Google Ads: The Ultimate 2026 Comparison Guide. Improvado. https://improvado.io/blog/bing-ads-vs-google-ads
  28. (2026). Kitrum – Custom Software Development Company. https://kitrum.com

Quote article, APA style

Volikov V. V., Shumilkina K. Y. Mechanisms for optimizing the marketing budget of international IT companies based on programmatic solutions and PPC advertising. Actual problems of innovative economy and law. 2026. №2. 244-255 pp. https://doi.org/10.36887/2524-0455-2026-2-53

Quote article, MLA style

Volikov V. V., Shumilkina K. Y. Mechanisms for optimizing the marketing budget of international IT companies based on programmatic solutions and PPC advertising. Actual problems of innovative economy and law. https://doi.org/10.36887/2524-0455-2026-2-53