Intelligent attribution modeling for enhanced digital marketing performance

Analyzing the effectiveness of digital marketing campaigns can be challenging due to the large number of customer interactions across various online channels. Attribution modeling is a widely used method for evaluating the performance of different channels and adjusting budgets accordingly. However, current models lack the level of sophistication that marketers need to trust the results. In this study, in partnership with MASS Analytics, we use real-world data from a B2C e-commerce store in the Middle East and North Africa region, including data on 348,078 customer journeys over 6 months and 2,683 journeys over 2 months. We develop data-driven Bayesian network attribution models that (1) properly address the natural imbalance in the datasets; (2) can be used to predict the conversion probabilities in real-time within an acceptable accuracy level of 0.9537; and (3) measure the channel attributions using a novel negative observation propagation technique within the Bayesian network model. Empirical results using a real-world dataset of an e-commerce website are quite promising. Furthermore, our principal contributions consist of the following: As a categorization and prediction tool, we present a Bayesian network model that gives marketers the power to identify the most effective channels for future consumer engagement and conversions. We use a novel method for channel attribution in the Bayesian network model that makes use of negative observation propagation. We also provide a new algorithm that aims to maximize attribution results and give marketers more insightful data to assess and refine their tactics. •Bayesian network model for classification and prediction of customer interactions and conversions.•Novel approach for channel attribution using negative observation propagation.•Optimized algorithmic model for deeper insights and improved decision-making.•Empirical success demonstrated using real-world e-commerce data.