Optimal Pricing Scheme Achieving Maximum Revenue For Online Retailers

Abstract

As e-market is becoming more popular, setting a proper price to maximize profit is vital for retailers on trading platforms. Most of the online retailers choose traditional pricing methods such as average pricing and markup pricing to set their prices. These traditional methods set prices based on the costs and the profit gain only, failing to consider the demands, the consumer personal preferences, and the inter-seller competitions. This motivates us to develop a proper pricing method that solves the above problems for online retailers. In this thesis, we propose an optimal pricing scheme (OPS) which enables the online retailers to achieve maximum revenue by recommending best prices. We applied the market share, the linear weight buyer model, and the most competitive sellers to address the above problems. Based on these platforms, we construct the revenue equations and find the best price and maximum revenue for sellers at different levels. The results for both simulated market and real market show that our proposed pricing scheme achieves higher revenue than traditional methods.