Marketing analytics has increasingly embraced multi-method approaches to capture the complexity of consumer behavior and organizational phenomena. Among the most prominent are Structural Equation Modeling (SEM), Qualitative Comparative Analysis (QCA), and Artificial Neural Network (ANN), three methodological approaches each grounded in a distinct logic of exploration/theory testing, explanation, and prediction. Despite their complementary strengths, researchers have lacked systematic guidance on how and why their combined application would be beneficial. This research addresses this gap by providing comprehensive guidelines for integrating SEM, QCA, and ANN in a single analytical project. In this article we summarize the theoretical foundations, assumptions, and practical considerations of each method. More specifically, using a synthetic dataset, we demonstrate how SEM is effective for identifying linear and global relationships in a proposed theoretical model. We then explain how QCA can be applied to uncover critical segment-specific “causal recipes” which often remain hidden in SEM. Finally, ANN searches for interactions as well as nonlinear relationships among predictors and which may lead to superior predictive accuracy. By combining these analytical approaches under a single unified analytical framework, this research enhances methodological rigor, mitigates risks of data overfitting and p-hacking, and supports identification of more nuanced insights into complex marketing phenomena. Recommendations for future research directions are also discussed.