e-Commerce companies acknowledge that customers are their most important asset and that it is imperative to estimate the potential value of this asset. In conventional marketing, one of the widely accepted methods for evaluating customer...
moree-Commerce companies acknowledge that customers are their most important asset and that it is imperative to estimate the potential value of this asset. In conventional marketing, one of the widely accepted methods for evaluating customer value uses models known as Customer Lifetime Value (CLV). However, these existing models suffer from two major shortcomings: They either do not take into account significant attributes of customer behavior unique to e-Commerce, or they do not provide a method for generating specific models from the large body of relevant historical data that can be easily collected in e-Commerce sites. This paper describes a general modeling approach, based on Markov Chain Models, for calculating customer value in the e-Commerce domain. This approach extends existing CLV models, by taking into account a new set of variables required for evaluating customers value in an e-Commerce environment. In addition, we describe how data-mining algorithms can aid in deriving such a model, thereby taking advantage of the historical customer data available in such environments. We then present an application of this modeling approach—the creation of a model for online auctions—one of the fastest-growing and most lucrative types of e-Commerce. The article also describes a case study, which demonstrates how our model provides more accurate predictions than existing conventional CLV models regarding the future income generated by customers.
