Dynamic Pricing Strategy for Logistics Revenue Management using Data Mining Technology

In this article, we proposed a new dynamic pricing strategy for revenue management in express industry. By analysing the historical data using big data analysis methods, we found out that a volume-price model, which involves the demand-price sensitivity of customers, external market environment and multiple origin-destination pair related factors, can generate reliable results. The model is tested and well-behaved based on real historical data. In addition, forecasting models for cost and volume are introduced using machine learning technologies. Based on the output from the volume-price model, cost model, and volume prediction, an optimization algorithm is proposed to maximize the potential volume of express tickets. Finally, we implemented the result in the real market and the impact was measured, which proved that this dynamic pricing strategy succeeds in increasing the volume of tickets for express companies.