Predictive Modeling of Campaign Lift Through the Integration of Heterogeneous Content Features and Temporal Interaction Sequences

Abstract

The accurate forecasting of campaign lift is a fundamental challenge in digital marketing and causal inference, requiring the isolation of the incremental impact of a specific intervention on user behavior. While traditional uplift modeling techniques have heavily relied on static user demographics and handcrafted behavioral features, modern digital campaigns operate in a highly dynamic ecosystem characterized by heterogeneous content formats and complex, sequential user interactions. This paper proposes a comprehensive framework for campaign-lift forecasting that systematically integrates high-dimensional heterogeneous content features, including text, image, and metadata representations, with temporal interaction sequences. By adopting a representation learning approach, the proposed architecture conceptually maps varied ad creatives and user engagement trajectories into a unified latent space tailored for causal effect estimation. We conduct a rigorous evaluation using a large-scale, real-world e-commerce dataset to demonstrate the efficacy of our approach against traditional meta-learners and contemporary neural uplift models. The results indicate that fusing content heterogeneity with sequential behavioral data significantly enhances the predictive accuracy of the counterfactual outcomes, thereby optimizing resource allocation in digital advertising. Furthermore, ablation studies provide insights into the distinct contributions of content modalities and temporal dynamics to the overall uplift signal.