Referral, Learning and Inventory Decision Making in a Social Network

We examine the impact of social learning in a referral program where customers' preferences are correlated in a social network. We characterize customers purchasing strategies based on their information and their types, and derive the demand distributions when customers are involved in social learning in a referral program. We find that the bias and variance trade-off in the demand distribution. The existence of uninformed customers will bring more bias to the demand. Social learning can moderate this effect at the expense of increasing demand variance. We investigate the firm's inventory decision in a referral program. We find that the stock-out of one product will influence the demand of the other product when customers are involved in social learning. Multiple referral reduces the effect of stock-out but dramatically increase the demand variance. We design the optimal referral so that it generates sufficient market exposure without incurring too much on the inventory cost.