One of the major applications of data mining is in helping companies determine which potential customers to market to If the expected profit from a customer is greater than the cost of marketing to her. The marketing action for that customer is executed. So far, work in this area has considered only the intrinsic value of the customer (I.e, the expected profit from sales to her). We propose to model also the customers network work. The expected profit from sales to other customers she may influence to buy. The customers those may influence, and so on recursively. Instead of viewing a market as a set of independent entities: we view it as a social network and model it as a Markov random field. We show the advantages of this approach using a social network mined from a colla borative filtering database. Marksting that exploits the network value of customers—also known as viral marketing—can be extremely effective, but is still a black art. Our work can be viewed as a step towards providing a more solid foundation for it, taking advantage of the availability of large relevant databases.
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