Trading revenue, reputation and trade secrets: a stochastic control framework for business operation

In this electronic era, most businesses, especially e-businesses like IT services, business process outsourcing (BPO), online merchants etc. maintain details of daily operations and customer feedback. Relations between different business parameters can be learned from these data, which in turn can be used in decision making. In this work, we develop a stylized mathematical framework for business operations based on the knowledge gathered from past data. Our proposed framework is generic and is close to optimal in terms of long-term profitability. In optimizing long-term profit, we balance between short-term profit and long-term reputation earned based on customer satisfaction while ensuring trade secrecy. Towards this we build on stochastic control and Lyapunov techniques that have been successfully applied in communication networks.