Published Online:September 2025
Product Name:The IUP Journal of Information Technology
Product Type:Article
Product Code:IJIT020925
DOI:10.71329/IUPJIT/2025.21.3.26-40
Author Name:Kaushik Bar
Availability:YES
Subject/Domain:Engineering
Download Format:PDF
Pages:26-40
Understanding the heterogeneous impact of marketing interventions across customer segments is a fundamental challenge in customer analytics. Uplift modeling, which estimates the incremental effect of a treatment (such as a marketing campaign) on individual behavior, is central to personalized decision-making in business applications. While conventional models such as S-Learner, T-Learner, and X-Learner provide strong baselines in estimating Individual Treatment Effects (ITEs), they often suffer from overfitting or unreliable uncertainty quantification in data-scarce or noisy environments. The paper proposes a Bayesian uplift modeling approach that leverages hierarchical Bayesian regression to model uplift at both individual and group levels. The proposed method incorporates prior beliefs and posterior distributions to yield calibrated uplift estimates and facilitate probabilistic reasoning. Unlike existing deterministic methods, the proposed approach provides calibrated uncertainty estimates for treatment effects, which is critical for risk-aware decision-making in marketing strategies. The paper evaluates the model on a publicly available marketing dataset and compares its performance against a comprehensive set of baselines, including logistic regression with interaction terms, causal forests, and the standard meta-learners. The results show that the proposed Bayesian method outperforms competitors in uplift accuracy, Qini coefficient, and uncertainty calibration. These findings highlight the potential of probabilistic modeling for more interpretable and personalized customer-level interventions.
In today’s competitive business environment, the ability to personalize marketing efforts has become a key differentiator for customer-centric organizations. Traditional customer response models focus on predicting the likelihood of an event (e.g., purchase, churn, click, etc.) under a specific condition (typically, after receiving a marketing intervention).