Ensemble Coordination Mechanisms for Aligning Marketing Attribution, Revenue Recovery, and Platform Cost Optimization

Abstract

Digital platforms operate under a persistent tension between aggressive marketing investment, disciplined revenue recovery, and strict platform cost control. Marketing attribution pipelines seek to explain which channels and touchpoints drive incremental performance, while revenue recovery systems concentrate on detecting leakage, fraud, and uncollectable exposure. At the same time, platform cost optimizers emphasize infrastructure, bidding, and operational costs that are often invisible to attribution and recovery stakeholders. In many organizations these three functions are implemented as distinct models, optimized on different horizons, trained on different samples, and governed by inconsistent constraints. This separation frequently produces contradictory incentives, unstable budgets, and nontransparent trade-offs. This study examines ensemble coordination mechanisms that explicitly couple attribution, revenue recovery, and cost optimization into a single optimization layer. The central idea is to treat each domain model as a base learner whose outputs are harmonized by a shared coordination variable and a multi-objective linear optimizer. The paper develops a formal system representation, a linearized objective integrating business and technical constraints, and a class of distributed algorithms capable of operating with incomplete and delayed information. An illustrative experimental design is used to discuss how the proposed coordination layer can stabilize budget allocation, reduce oscillations between aggressive growth and cost cutting, and expose interpretable trade-off surfaces to decision makers. The overall formulation remains compatible with existing marketing technology stacks and supports gradual adoption without requiring wholesale system redesign.