Big Data-Driven E-Commerce Marketing Strategies for Agricultural Products

This study investigates the application of big data in the e-commerce marketing of agricultural products, addressing the challenges of information asymmetry and supply-demand mismatch prevalent in traditional agricultural sales. By leveraging the unique characteristics of agricultural e-commerce, this research explores how big data can enhance consumption insights, refine market positioning, and optimize sales channels. Utilizing data from the Theory of Three Represents e-commerce platform over a 12-month period, the study analyzes the impact of big data on market demand forecasting, dynamic pricing, and personalized marketing. The findings indicate that big data can significantly reduce the demand forecast error rate by 13%, lower the unsalable rate by 23%, and boost the conversion rate by 128.6%. This research provides valuable strategic insights for small and medium-sized e-commerce enterprises, facilitating the transition of agricultural products from a “market-seeking” to a “market-accurately-docking” approach.