Article

Identifying High Value Consumers In A Network: Social Influence Versus Individual Characteristics

01/2014;

ABSTRACT

Firms are interested in identifying customers who generate the highest revenues. Traditionally, customers are regarded as isolated individuals whose buying behavior depends solely on their own characteristics. In a social network setting, however, customer interactions can play an important role in purchase behavior. This study proposes a spatial autoregressive model that explicitly shows how network effects and individual characteristics interact in generating firm revenue. Using model output, we develop a method of identifying individuals whose purchase behavior most impacts the total revenues in the network. An empirical study using a user-level online gaming dataset demonstrates that the proposed model outperforms benchmark models in predicting revenues. Moreover, the proposed value measure outperforms a variety of benchmark measures in identifying the most valuable customers.

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Available from: Qin Zhang, Jun 19, 2014