Applying Revenue Management to the Last Mile Delivery Industry

Abstract: The understanding of what motivates a customer to pay more for a product or service has al-ways been a fundamental question in business. To the end of answering this question, revenue management is a business practice that revolves around using analytics to predict consumer behavior and willingness-to-pay. It has been a common practice within the commercial airline and hospitality industries for over 30 years, allowing adopters to reach their service capacity with increased profit margins. In this thesis, we investigated the possibility to apply revenue management to the last mile delivery industry, an industry that provides the service of delivering goods from e-commerce companies to the consumer’s front door. To achieve this objective, a revenue management framework was conceived, detailing the interaction between the customer and a dynamic pricing model. The model itself was a product of a machine learning model, intended to segment the customers and predict the willingness-to-pay of each customer segment. The performance of this model was tested through a quantitative study on synthetic buyers, subject to parameters that influence their willingness-to-pay. It was observed that the model was able to distinguish between different types of customers, yielding a pricing policy that increased profits by 7.5% in comparison to fixed price policies. It was concluded that several factors may impact the customer’s willingness-to-pay within the last mile delivery industry. Amongst these, the convenience that the service provides and the disparity between the price of the product and the price of the service were the most notable. However, the magnitude of considering these parameters was never determined. Finally, em-ploying dynamic pricing has the potential to increase the availability of the service, enabling a wider audience to afford the service.