Decentralized Attribution Modeling: A Multi-Agent Approach to Enterprise-Scale Marketing Analytics

Abstract

Enterprise-scale marketing organizations increasingly rely on attribution models to allocate budgets across heterogeneous channels, yet conventional centralized approaches face structural challenges related to data silos, privacy regulations, and computational scalability. The consolidation of all interaction data into a single modeling environment is often incompatible with fragmented technology stacks, regional governance constraints, and independent experimentation agendas maintained by different business units. At the same time, organizations seek attribution estimates that are coherent at the global level, robust to heterogeneous data quality, and aligned with diverse local objectives such as revenue, profit, and long-term customer value. This paper investigates a decentralized attribution modeling framework based on a multi-agent perspective, in which autonomous agents control different subsets of data and decision variables while coordinating through constrained optimization protocols. The proposed view treats attribution as a cooperative game over a shared response surface, where each agent performs localized inference and contributes to a global allocation that satisfies conservation and consistency conditions. The paper develops a class of linear models that admit distributed estimation under communication and privacy constraints, characterizes equilibrium properties of agent interactions under convex objectives, and studies practical trade-offs between local flexibility and global coherence. A set of stylized enterprise scenarios illustrates how the framework can represent cross-region governance, channel ownership boundaries, and mixed measurement technologies. The analysis emphasizes modeling choices, algorithmic structure, and implementation considerations, with a focus on properties that can be evaluated under realistic enterprise constraints rather than on specific empirical benchmarks.