This study addresses critical pain points in pricing decision-making for SMEs, including insufficient data value mining, delayed dynamic strategy response, and high technical application barriers. We innovatively propose a hybrid intelligent dynamic pricing model integrating econometric analysis frameworks with machine learning prediction capabilities. Leveraging multi-dimensional enterprise datasets from the National Business Management Skills Competition, we systematically developed an integrated technical framework encompassing "data preprocessing—feature engineering—model construction—system implementation". Specifically, by employing panel data fixed-effects models to effectively identify causal effects in price fluctuations, combined with XGBoost's robust nonlinear fitting capabilities to handle complex variable relationships, we constructed a high-precision hybrid prediction architecture. Experimental results demonstrate that the model maintains an average absolute error below 5% and achieves a goodness-of-fit index exceeding 0.92, demonstrating excellent predictive performance. To enhance model interpretability and practicality, we introduced SHAP value analysis for feature contribution evaluation and developed a lightweight decision support system based on the Streamlit framework, enabling core functions such as price elasticity simulation and dynamic strategy generation. Empirical analysis shows the model significantly improves pricing decision efficiency and scientific rigor for SMEs. During pilot applications in manufacturing enterprises in the Yangtze River Delta region, it successfully helped companies achieve an average 15% profit growth. This study not only provides a feasible and easy-to-use digital pricing solution for small and medium-sized enterprises, but also explores a new path to transform the achievements of management discipline competition into real productivity.
Dynamic pricing; multi-dimensional data; machine learning; small and medium enterprises; decision support systems.
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