Differentiated Service Pricing on Social Networks Using Stochastic Optimization

This paper develops a combined simulation and optimization model that allows to optimize different service pricing strategies defined on the social networks under uncertainty. For a specific reference problem we consider a telecom service provider whose customers are connected in such network. Besides the service price, the acceptance of this service by a given customer depends on the popularity of this service among the customer's neighbors in the network. One strategy that the service provider can pursue in this situation is to stimulate the demand by offering the price incentives to the most connected customers whose opinion can influence many other participants in the social network. We develop a simulation model of such social network and show how this model can be integrated with stochastic optimization in order to obtain the optimal pricing strategy. Our results show that the differentiated pricing strategies can increase substantially the revenue of a service provider operating on a social network.