Analyzing existing customers' websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing

Research highlights? Prediction of the profitability of new customers. ? Used web mining to extract latent semantic concepts from customers' websites. ? Used clustering of latent semantic concepts to identify prevalent terms. ? Used prevalent terms to identify addresses of new profitable customers. We investigate the issue of predicting new customers as profitable based on information about existing customers in a business-to-business environment. In particular, we show how latent semantic concepts from textual information of existing customers' websites can be used to uncover characteristics of websites of companies that will turn into profitable customers. Hence, the use of predictive analytics will help to identify new potential acquisition targets. Additionally, we show that a regression model based on these concepts is successful in the profitability prediction of new customers. In a case study, the acquisition process of a mail-order company is supported by creating a prioritized list of new customers generated by this approach. It is shown that the density of profitable customers in this list outperforms the density of profitable customers in traditional generated address lists (e.g. from list brokers). From a managerial point of view, this approach supports the identification of new business customers and helps to estimate the future profitability of these customers in a company. Consequently, the customer acquisition process can be targeted more effectively and efficiently. This leads to a competitive advantage for B2B companies and improves the acquisition process that is time- and cost-consuming with traditionally low conversion rates.

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