Optimal pricing strategies for delivery alliance in online retailing platform

Nowadays, some online retailing platforms emerge to integrate transport capacity to provide standard distribution service for sellers. Such an integrated form of service is defined as delivery alliance (DA). To have a better understanding of how to price the service, this study aims to fixate on the seller’s problems and builds a series of profit maximization models in accordance with the two-sided market pricing theory within a platform business model.

In the present study, some optimization models are built in the two-sided market type and the optimal solutions are found in a three-dimensional decision space. By using the basic model as the benchmark, some optimization problems of DA in realistic situations are discussed. Particularly, a power-law-distribution model is established to deal with the uncertainty in forecasting. Also, a price-sensitive model and a loss-aversion model are presented to describe the various reactions of sellers to charging modes. Finally, some combined situations are discussed and the strategies are compared under the mentioned models.

By selecting the basic model as the benchmark, the specific pricing strategies are found for each context to yield the optimal profits. The flexibility of pricing strategy in the basic model and rigid pricing strategies in extended models, are discussed. As a result, the guidelines for the online retailing platforms are developed on designing and pricing the DA service.

First, it would be interesting to expand the pricing plan of the platform. For instance, menu pricing and quantity discount have not been considered, which are common in practice. The time discounting has also been ignored. If the time value were calculated, the contract fees would be more critical due to the earliest of collecting money. Finally, those joiners who have huge order sizes are crucial for the ecosystem indeed, but arouse no attention. While in reality, they may have more power to bargain with the platform. Thus, how the platform competition affects the pricing strategies needs future research.

The optimal pricing strategies under these models are analytically found out, and it is shown that the presented models result in the same scale of joiners and profits in optimization. This suggests that DA works well in various behavioral contexts. This also suggests that DA is a significant controller in service quality improvement. Then, the optimal pricing strategies are compared among all the models. During this, it is discovered that the realistic contexts might reduce the profit, whereas an appropriate pricing strategy can pull this back without loss of service quality.