Research on Dynamic Prediction Model of Brand Marketing Content ROI Based on Machine Learning
Keywords:
Brand Marketing Content ROI, Machine Learning, Dynamic Prediction Model, XGBoost Algorithm, SHAP Value Analysis, Feature Engineering, Three-Dimensional and Nine-Subdimension, Dynamic Optimization Mechanism, Hierarchical Adaptation, Brand Marketing Decision-MakingAbstract
Addressing the industry pain points of high blindness in brand marketing content investment, uncontrollable ROI, and lack of dynamic adaptability, this study aims to construct a dynamic ROI prediction model for marketing content with both prediction accuracy and interpretability. Firstly, the Delphi method was adopted to screen 16 key influencing factors from 28 potential factors, establishing a "content-channel-user" three-dimensional and nine-subdimension influence system. Subsequently, data from 600 cross-industry marketing cases were collected, and after feature engineering processing, the performance of algorithms such as linear regression, random forest, and XGBoost was compared to determine XGBoost as the optimal model. SHAP value analysis was introduced to solve the model's "black box" problem. Finally, a dynamic optimization mechanism of "real-time collection-deviation detection-strategy adjustment-model iteration" was designed. Empirical results show that the model's R² on the test set reaches 0.82 with an average deviation rate of 7.8%, which decreases to 4.2% after dynamic optimization. In corporate practice, the ROI compliance rate of FMCG brands increased from 52% to 84%, and the ROI of cross-border e-commerce live streaming rose from 1:1.8 to 1:2.9. The innovations of this study lie in proposing a three-dimensional driving theoretical framework, constructing a trinity method system, and developing a dynamic optimization mechanism. It provides a data-driven tool for scientific decision-making of brand marketing content and enriches the research achievements in the interdisciplinary field of machine learning and marketing.
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