AMTP Proceedings 2026

Document Type

Conference Proceeding

Abstract

Marketing organizations increasingly rely on algorithmic decision systems to guide targeting, budget allocation, and personalization. While these systems are widely deployed, evaluating their incremental impact remains challenging when traditional experimentation approaches, such as geo-based or large-scale randomized tests, are infeasible due to operational, regulatory, or interference constraints. This study examines how marketing decision systems can be rigorously evaluated under such conditions. Drawing on recent advances in causal inference and marketing analytics, we develop a decision-centric evaluation framework that integrates synthetic control methods, quasi-experimental designs, and incrementality diagnostics to approximate counterfactual performance. The framework emphasizes methodological transparency, robustness checks, and practical guardrails to mitigate common sources of bias in constrained testing environments. By shifting the focus from model accuracy to decision effectiveness, this research contributes to the marketing analytics literature and offers actionable guidance for organizations seeking credible measurement of algorithmically driven marketing decisions in complex, real-world settings.

Copyright

This work is archived and distributed under the repository's Standard Copyright and Reuse License (opens in new tab). End users may copy, store, and distribute this work without restriction. For all other uses, permission must be obtained from the copyright owners or their authorized agents.

DOI

10.20429/amtp.2026.44

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