Traditionally, the value of a product has been assessed according to the direct revenues the product creates. However, products do not exist in isolation but rather influence one another's sales. Such influence is especially evident in e-commerce environments, in which products are often presented as a collection of web pages linked by recommendation hyperlinks, creating a large-scale product network. The authors present a systematic approach to estimate products’ true value to a firm in such a product network. Their approach, which is in the spirit of the PageRank algorithm, uses available data from large-scale e-commerce sites and separates a product's value into its own intrinsic value, the value it receives from the network, and the value it contributes to the network. The authors demonstrate their approach using data collected from the product network of books on Amazon.com . Specifically, they show that the value of low sellers may be underestimated, whereas the value of best sellers may be overestimated. The authors explore the sources of this discrepancy and discuss the implications for managing products in the growing environment of product networks.
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