DATA-DRIVEN ATTRIBUTION MODELING IN DIGITAL MARKETING

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Abstract
B a c k g r o u n d . In the contemporary digital environment, marketing communications have evolved into multi-channel, personalized, and dynamic interactions, necessitating an increasingly precise quantification of the efficacy of each user touchpoint with a brand. Conventional rule-based marketing attribution models, which exclusively assign value to a singular touch-point of contact, no longer yield the requisite level of analytical granularity. In response to these methodological challenges, advanced economic and mathematical methodologies, notably models predicated on Markov chains and Shapley values, are progressively being deployed to facilitate a rigorously reasoned and quantitatively justifiable allocation of value across all contributing channels. M e t h o d s . The research methodology is based on a combination of general scientific and specialized methods. Specifically, it utilized theoretical modeling, comparative analysis, as well as stochastic modeling (Markov chains) and cooperative game theory (Shapley values). R e s u l t s . This research rigorously investigated marketing attribution in the digital environment, demonstrating the inherent limitations of traditional rule-based models and substantiating the superior efficacy of adaptive approaches, particularly Markov chains and Shapley values. The empirical implementation and comparative analysis of the Shapley value model confirmed its enhanced precision and capacity to objectively quantify each channel's contribution, leading to actionable insights for strategic marketing optimization. This study provides a robust framework for understanding multi-touch attribution, emphasizing the critical role of data-driven methodologies in contemporary marketing analytics. C o n c l u s i o n s . The work is relevant for marketing analysts and digital strategy teams, as it presents a comparative analysis of rule-based and algorithmic attribution models and offers practical solutions for campaign optimization. The Shapley Value model was implemented in Python and tested on real-world marketing data. The practical value lies in the ability to use the results for better budget allocation, identifying undervalued channels, and increasing return on marketing investment (ROMI).

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