Causal Attribution in Digital Marketing: Integrating Media Mix Modeling with Experimentation and Path Analytics

Previous analyses of attribution and performance evaluation have mainly focused on conventional metrics such as click-through rate, conversion rate and impression share, and included limited causal inference. In this study, we present a multi-method analytical framework for integrating evidence-based attribution modeling that combines structural equation modeling, regression, and analytical hierarchy process to move towards an interpretable causal understanding. The aim of this research is to identify the interrelations between marketing channels, consumer paths and their perceived effectiveness to optimize allocation, valuation and forecasting. Particularly, path analytics offers attractive insights that are heterogeneous among the diverse material touchpoints. Empirical testing shows that this heterogeneity can be attributed to asymmetry of channel effects and demonstrates how this can produce both direct and mediated pathways—and also interactions that are far too nonlinear. By introducing experimentation modules closer to the design of the media mix that offer adaptive weighting in proximity to observed behavioral responses, advertisers’ decision models can reduce multicollinearity bias and measurement error to achieve causal clarity. The results revealed that cross-channel synergy was significantly associated with greater predictive accuracy to explain purchase decisions, whereas independently defined channels were correlated with less capacity to predict these outcomes. As attribution issues are especially critical for digital marketers, a comprehensive evaluation of this framework focuses on the criteria developed for causal attribution for multi-touch models by hierarchical weighting. This approach may be applicable in environments where hybrid methods (SEM, regression, AHP) are used.

Ҳали таржима қилинмаган