System Shift Risk, Measurement Error, and the Limits of Non-Experimental Marketing Measurement: An Exploratory Validation Using RCT Benchmarks

Abstract

The increasing complexity of digital advertising environments has created significant challenges in ensuring the accuracy and reliability of marketing measurement. Although randomized controlled trials (RCTs) are considered the benchmark for causal evaluation, their implementation is often constrained by cost, operational limitations, and managerial requirements, leading organizations to rely on non-experimental measurement approaches that may generate substantial estimation errors. This study aims to explore the role of system-level factors in explaining measurement error and to validate the System Shift Framework as a diagnostic approach for understanding the reliability of non-experimental marketing measurement. This research employs an exploratory quantitative design using six aggregate-coded marketing cases and compares non-experimental lift estimates with RCT benchmarks. Data analysis was conducted using descriptive statistics, correlation analysis, linear regression, regularized logistic regression, model comparison, and cluster analysis. The findings indicate that Strategy Quality and Feedback Maturity demonstrate the strongest associations with lower measurement error, higher accuracy, and improved measurement reliability, whereas the aggregate System Shift Risk Score shows limited explanatory power for continuous measurement error. The results suggest that measurement reliability is influenced not only by statistical methods but also by an organization’s capability to interpret, evaluate, and correct analytical outcomes. This study concludes that the System Shift Framework provides a complementary diagnostic perspective for improving marketing measurement practices by integrating causal inference with systems thinking.