Systematic Review of Marketing Attribution Techniques for Omnichannel Customer Acquisition Models

In the evolving landscape of digital commerce, the ability to accurately attribute marketing outcomes to specific touchpoints within the customer journey is critical for optimizing omnichannel acquisition strategies. This systematic review examines the current state of marketing attribution techniques applied in omnichannel customer acquisition models, with the goal of identifying prevailing methodologies, comparative effectiveness, and emerging trends. A comprehensive search of peer-reviewed journals, industry reports, and conference proceedings published between 2013 and 2024 was conducted using databases such as Scopus, Web of Science, and Google Scholar. The review categorizes attribution techniques into rule-based models (e.g., first-touch, last-touch, linear), algorithmic models (e.g., logistic regression, Markov chains), and advanced machine learning approaches (e.g., Shapley value models, deep learning). Findings indicate a significant shift from traditional rule-based methods to data-driven and probabilistic techniques that account for inter-channel synergies and temporal dependencies. Algorithmic models like Markov chains demonstrate superior performance in capturing the non-linear and dynamic nature of customer journeys, while machine learning approaches offer greater predictive accuracy and scalability in complex omnichannel environments. However, challenges such as data integration across disparate channels, attribution bias, model interpretability, and real-time application remain prominent. The review further highlights the importance of contextual factors such as industry type, customer segmentation, and campaign objectives in selecting appropriate attribution models. It also identifies a growing interest in hybrid frameworks that blend business rules with machine learning for enhanced transparency and decision-making support. The study concludes that while advanced attribution models hold substantial promise, successful implementation hinges on data quality, cross-functional collaboration, and technological maturity. Future research should focus on real-time adaptive attribution, causal inference techniques, and standardized evaluation metrics to further advance the field. This review provides a critical foundation for marketers, analysts, and researchers seeking to enhance omnichannel acquisition performance through informed attribution strategies.