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



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
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