Pharmaceutical E-Commerce Sales Forecasting Using GM(1,1) and SVR

With the rapid growth of China’s pharmaceutical e-commerce industry, driven by the “Internet +medicine” model, advanced data-driven techniques such as $\operatorname{GM}(1,1)$ gray prediction model and Support Vector Regression (SVR) have become crucial in enhancing business operations. This study analyzes sales from 26 pharmaceutical stores, identifying key drivers such as consumer trust and brand reputation, with a focus on Alibaba Health Pharmacy and Tmall. By applying the GM(1,1) model, we predict vitamin drug sales on Tmall for the upcoming three months, forecasting a revenue of $\mathbf{1 0 5. 2 3}$ million yuan. Moreover, SVR is utilized to model complex sales patterns, proving its superior predictive accuracy over conventional methods. The outcomes highlight the efficiency of machine learning models in sales forecasting, offering valuable insights for inventory management, pricing strategies, and promotional activities. This research emphasizes the critical role of data analytics in supporting strategic decision-making and sustaining growth in the pharmaceutical e-commerce sector.