Hierarchical Bayesian Deep Learning for return on advertising spend prediction: A probabilistic approach to e-commerce advertising
Authors: Jha, A., Bhatia, A., Tiwari, K. and Pandey, H.M.
Journal: Engineering Applications of Artificial Intelligence
Volume: 164
ISSN: 0952-1976
DOI: 10.1016/j.engappai.2025.113200
Abstract:In the highly competitive landscape of e-commerce advertising, maximizing return on advertising spend (ROAS) is crucial yet inherently uncertain due to auction-based bidding dynamics and fluctuating market conditions. Traditional deterministic models struggle to capture this uncertainty, necessitating a probabilistic approach that balances predictive accuracy with interpretability. To address this challenge, the paper proposes a novel Hierarchical Bayesian Deep Learning framework. The architecture was motivated by initial exploratory analysis using a Bayesian Belief Network (BBN) to map structural dependencies, while the final deep learning model overcomes scalability limitations using self-attention mechanisms and a Mixture Density Network (MDN) for full distributional modeling of ROAS. The BBN captures dependencies among campaign variables, enhancing interpretability, while the hierarchical deep learning architecture leverages self-attention mechanisms to address scalability challenges in high-dimensional settings. Experimental results reveal that the proposed framework achieves 22.8% lower RMSE and 27.4% better Negative Log Likelihood (NLL) and up to 31.2% lower Kullback–Leibler divergence (KLD) than state-of-the-art methods (DeepAR, Prophet, NGBoost), achieving an R2 of 98% with an inference speed of 5.2 ms per campaign, confirming its feasibility for real-time bidding applications which typically require sub-10ms latency, enabling a feasible real-time bidding. Ablation studies confirm that attention-driven feature selection and calibrated uncertainty quantification significantly enhance both predictive performance and explainability, identifying key drivers of campaign success. By providing precise, uncertainty-aware, and explainable predictions, this approach enables adaptive bidding strategies, optimized budget allocation, and risk management, setting a new benchmark for intelligent decision-making in digital advertising.
Source: Scopus
Hierarchical Bayesian Deep Learning for Return on Advertising Spend Prediction: A Probabilistic Approach to E-Commerce Advertising
Authors: Pandey, H., Jha, A., Bhatia, A. and Tiwari, K.
Journal: Engineering Applications of Artificial Intelligence
Publisher: Elsevier
eISSN: 1873-6769
ISSN: 0952-1976
Abstract:In the highly competitive landscape of e-commerce advertising, maximizing Return on Advertising Spend (ROAS) is critical, yet remains inherently uncertain due to auction-based bidding dynamics and fluctuating market conditions. Traditional deterministic models fail to capture this uncertainty, necessitating a probabilistic approach that balances predictive accuracy with interpretability. To address this challenge, the paper proposes a novel Hierarchical Bayesian Deep Learning framework that integrates a Bayesian Belief Network (BBN) for structured probabilistic reasoning and a Mixture Density Network (MDN) for full distributional modeling of ROAS. The BBN models dependencies among campaign variables, offering interpretable insights, while the hierarchical deep learning architecture overcomes scalability limitations in high-dimensional settings through self-attention mechanisms. Experiments demonstrate up to 22.8% lower RMSE and 27.4% better Negative Log Likelihood (NLL) and up to 31.2% lower Kullback-Leibler divergence (KLD) than state-of-the-art methods (DeepAR, Prophet, NGBoost), achieving an R² of 98% with an inference speed of 5.2 ms per campaign, making real-time bidding feasible. Ablation studies confirm that attention-driven feature selection and calibrated uncertainty quantification significantly enhance both predictive performance and explainability, identifying key drivers of campaign success. By providing precise, uncertainty-aware, and explainable predictions, this approach enables adaptive bidding strategies, optimized budget allocation, and risk management, setting a new benchmark for intelligent decision-making in digital advertising.
https://www.sciencedirect.com/journal/engineering-applications-of-artificial-intelligence
Source: Manual