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Liburkina L. M.

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The AI-driven Assessment of Startup Marketing Network Efficiency
Serhiienko O. A., Tonieva K. V., Shvets A. D.

Serhiienko, Olena A., Tonieva, Krystyna V., and Shvets, Anastasiia D. (2026) “The AI-driven Assessment of Startup Marketing Network Efficiency.” Business Inform 1:611–620.
https://doi.org/10.32983/2222-4459-2026-1-611-620

Section: Management and Marketing

Article is written in Ukrainian
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UDC 339.138:004.8:330.43:334.012.64

Abstract:
The current stage of digital economy development demands that startups optimize costs to the fullest and make efficient use of available financial resources. In a highly competitive environment, digital marketing becomes the primary driver of business scaling; however, traditional linear attribution models (First-Touch, Last-Touch, Linear) fail to accurately capture the complex, multifactorial, and nonlinear dynamics of consumer interaction with a brand. They either overestimate the final touchpoints or undervalue the channels that generate initial interest, resulting in a highly inefficient and risky allocation of a startup’s marketing budget. The aim of the article is to develop and empirically validate an innovative methodological approach based on artificial intelligence (AI) technologies for the quantitative assessment of the centrality and true contribution of individual nodes (channels) in the marketing network complex of startups, in order to prescriptively optimize their investment strategy. To achieve this goal, deep machine learning methods (in particular, long short-term memory (LSTM) neural networks with attention mechanisms) were applied for high-precision modeling of customer touchpoint time sequences, as well as the mathematical tools of cooperative game theory, namely Shapley Values, for fair allocation of conversion value among all participating channels in the coalition. As a result of the study, a multi-criteria attribution model, AI-Shapley MTA, was developed and tested. Empirical modeling confirmed its substantial advantage over traditional heuristic methods (attribution accuracy increased from 76.5% to 81.9%). The use of the Shapley vector made it possible to determine the true network centrality of each marketing channel (CCI), acting as an efficient detector of synergy and substitution. In particular, a critical underestimation of organic search in traditional models and a high rate of conversion loss through paid social media were identified. Based on the results, a comprehensive system of integrated adaptive metrics (C-KPI) was developed, including the Channel Centrality Index (CCI), Predictive Accuracy of Campaigns (PAC), Adaptive Efficiency Index (AEI), and Optimized ROI accounting for long-term customer lifetime value (LTV). The findings confirm that implementing the developed AI-driven assessment fundamentally transforms the startup’s marketing framework, facilitating a shift from intuitive budget allocation to data-driven prescriptive optimization. The proposed methodology is capable of increasing the overall ROI of marketing efforts by 10–20% and significantly minimizing financial risks. Future research directions in this area include studying federated learning methods to ensure data privacy when applying AI analytics.

Keywords: artificial intelligence, marketing strategy, startup, marketing performance, multi-channel attribution, budget optimization, deep learning, Shapley value.

Fig.: 1. Tabl.: 2. Formulae: 3. Bibl.: 10.

Serhiienko Olena A. – Doctor of Sciences (Economics), Professor, Professor, Department of Entrepreneurship, Trade and Logistics, National Technical University «Kharkiv Polytechnic Institute» (2 Kyrpychova Str., Kharkіv, 61002, Ukraine)
Email: [email protected]
Tonieva Krystyna V. – Candidate of Sciences (Economics), Associate Professor, Associate Professor, Department of Management, Logistics and Innovations, Simon Kuznets Kharkiv National University of Economics (9a Nauky Ave., Kharkiv, 61166, Ukraine)
Email: [email protected]
Shvets Anastasiia D. – Postgraduate Student, Department of Entrepreneurship, Trade and Logistics, National Technical University «Kharkiv Polytechnic Institute» (2 Kyrpychova Str., Kharkіv, 61002, Ukraine)
Email: [email protected]

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