Referral, Learning and Inventory Decision Making in a Social Network

Guangwen Kong (), Ankur Mani () and Yuanchen Su ()
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Guangwen Kong: University of Minnesota, Department of Industrial Engineering, 111 Church Street SE, Minneapolis, MN 55455
Ankur Mani: University of Minnesota, Department of Industrial Engineering, 111 Church Street SE, Minneapolis, MN 55455
Yuanchen Su: University of Minnesota, Department of Marketing, 321 19th Avenue South Minneapolis, MN 55455

No 18-15, Working Papers from NET Institute

Abstract: 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.

Keywords: referral program; social learning; inventory (search for similar items in EconPapers)
JEL-codes: L13 L43 L96 (search for similar items in EconPapers)
Pages: 56 pages
Date: 2018-10
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