Pricing and wage strategies for an on-demand service platform with heterogeneous congestion-sensitive customers

In this paper we consider an on-demand service platform connecting self-scheduling service providers with heterogeneous and congestion-sensitive customers. Based on the congestion sensitivity, customers are classified into two types. Two pricing strategies are proposed for the platform to classify the customers. We develop a model that can capture customer heterogeneous with different congestion sensitivities, in which the optimal strategies are analyzed and compared for the platform. In addition, we compare this model with the unclassified model without customer congestion-sensitivity heterogeneity from the perspective of all participants and the whole society. We show that it does not always benefit the platform to serve as many customers as possible, and the platform should switch between the two strategies according to market conditions. When the potential supply is scare, the potential demand is sufficient, the proportion of low congestion-sensitivity customers is high, or the sensitivity difference between two types of customers is significant, the platform should adopt the strategy serving only one type of customers rather than the whole market. Furthermore, we observe that, the classified model always brings more profit, consumer surplus and social welfare than the unclassified one, although it sometimes hurts the agents' labor welfare.