Comparing Rule Based and Algorithmic Multi Touch Attribution Approaches for Enterprise Level Marketing Performance Measurement

Abstract

Digital marketing programs in large organizations span multiple channels, devices, and customer touchpoints, creating complex journeys that challenge traditional measurement practices. As budgets shift toward performance oriented investment, the ability to attribute outcomes to marketing interactions has become central to planning and optimization. Multi touch attribution has emerged as one response, seeking to assign credit for conversions across entire paths rather than to a single event. Within this domain, enterprises commonly face a choice between rule based approaches and algorithmic approaches, each embodying distinct assumptions, data requirements, and governance implications. This paper examines that choice in a structured way for enterprise level marketing performance measurement. It outlines the conceptual foundations of multi touch attribution, describes representative rule based and algorithmic techniques, and evaluates them across accuracy, interpretability, operational complexity, and alignment with decision making processes. The discussion pays particular attention to issues that become more pronounced at enterprise scale, including heterogeneous product portfolios, regional and regulatory differences, and the coexistence of digital and offline touchpoints. The paper also considers practical implementation factors such as data engineering, stakeholder roles, and model monitoring. Rather than promoting a single preferred technique, the analysis highlights conditions under which each family of approaches may be more or less appropriate, and it discusses hybrid strategies that combine elements of rule based and algorithmic attribution within broader measurement frameworks.