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In recent years following the growing interest in online social network analysis, the notion of viral marketing has been proposed in both literature and practice. However, in order to find the opinion leader in the social network, marketers need to have a sound analytic tool to rank potential buyers. In order to tackle this issue, this research proposes the UserRank and BuyerRank algorithms, both Social Network Analysis models that aim to assist marketers to rank potential buyers based on their future influence estimated from their past auctions/purchase behaviours on eBay.The experiments carried out using simulated data that is similar to that of which is obtained from real world environments were applied to the UserRank and BuyerRank algorithms. The research finding shows that it is possible to determine the most connected user who also has the highest buying capabilities or buying power'.