Modelling Attribution-Driven Budgeting Systems for High-Intent Consumer Acquisition
Abstract
In the increasingly fragmented digital advertising ecosystem, marketing effectiveness hinges on precision-targeted investments toward high-intent consumers. This paper develops a conceptual framework for attribution-driven budgeting systems aimed at optimizing media spend allocation for maximum consumer acquisition efficiency. Synthesizing over 100 scholarly and industry sources, we present a literature-based architecture that integrates multi-touch attribution models, predictive analytics, and AI-enhanced budget optimization mechanisms. The proposed system accounts for cross-channel synergies, consumer journey mapping, and intent scoring to support data-informed budget decisions. The paper emphasizes a literature review–centric approach due to the lack of primary data, and it explores the evolving interplay between attribution granularity, spend elasticity, and media performance outcomes. Future pathways for empirical testing, privacy-centric enhancements, and real-time budgeting automation are also highlighted.
Keywords
attribution modellingbudget optimizationhigh-intent consumersmedia mix
References
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