Beyond Heuristics: A Predictive Modeling Framework for Multi-touch Attribution in Online Marketing

Understanding the contribution of different digital marketing channels to customer conversions is crucial for effective resource allocation and campaign optimization. Traditional attribution models, such as first-touch and last-touch, are widely used but often fail to account for the complexity of multi-channel interactions. This study critically evaluates the limitations of heuristic-based attribution methods and presents a data-driven framework leveraging machine learning techniques to improve attribution accuracy. Existing literature predominantly explores rule-based and probabilistic models, such as Markov Chains and the Shapley Value approach, yet these methods often struggle with scalability and real-time adaptability. Recent advancements in machine learning offer new opportunities to enhance multi-touch attribution through predictive analytics. By analyzing clickstream data obtained from Google Analytics 360 via BigQuery, this study constructs a structured four-stage modeling process. The methodology incorporates heuristic models as baselines, a third-order Markov Chain model for probabilistic evaluation, and an XGBoost classification model for predictive accuracy assessment. The results reveal substantial inconsistencies in heuristic-based models, particularly in their allocation of credit among non-dominant channels. The probabilistic Markov Chain approach provides a more balanced distribution of attribution, yet it lacks the flexibility of machine learning-based models in capturing dynamic consumer behavior. The XGBoost model demonstrates superior predictive performance, achieving an overall accuracy of 98.4% and an AUC of 0.846. Feature importance analysis identifies organic search, direct traffic, and paid search as the most influential factors driving conversions. This research advances attribution modeling by introducing a scalable and reproducible machine learning-based framework. The findings offer valuable insights for marketers seeking to refine.