Deconstruction and Interpretation of Advertising Cost‐Conversion Efficiency: Microlevel Feature Analysis Based on Machine Learning and SHAP

ABSTRACT Precisely deconstructing the numerical composition of advertising cost‐conversion efficiency (ACTE) and uncovering its microlevel associative mechanisms are critical for enterprises to optimize marketing decisions. To overcome the limitations of existing methods regarding modeling accuracy and result interpretability, this study constructs an analytical framework integrating machine learning with interpretable artificial intelligence. Using 174,584 real‐world microlevel advertising records from a Chinese telecommunications company in July 2024, we systematically compared six representative machine learning models, rigorously evaluating them against a historical average baseline. The findings reveal that the XGBoost model achieves optimal fitting and generalization performance, with a test set coefficient of determination of 0.8236 ± 0.2061, significantly outperforming the baseline and other models. Furthermore, SHAP‐based interpretability analysis not only precisely quantifies the contribution of each feature to the ACTE value but also systematically reveals underlying nonlinear dependencies and threshold effects. Specifically, the advertising conversion rate plays a dominant role; exposure frequency shows a step‐like positive association; production cost exhibits a reversal effect demarcated by the median; click‐through rate and play volume display discrete stratified structures; and average display cost contributes minimally. Specification tests indicate that ACTE interpretability primarily stems from algebraic relationships of constitutive features, with limited independent contributions from non‐constitutive features. Theoretically, this study provides microlevel evidence for nonlinear advertising associations; methodologically, it establishes a rigorous modeling‐to‐interpretation paradigm; and practically, it offers empirically grounded decision support for advertising exposure regulation and cost optimization.