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

 
: Thorleuchter, Dirk; Poel, Dirk van den; Prinzie, Anita

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Preprint urn:nbn:de:0011-n-1910442 (212 KByte PDF)
MD5 Fingerprint: 1e3b5dd74475a1a63e060abd907e54d2
Erstellt am: 22.12.2011


Expert Systems with Applications 39 (2012), Nr.3, S.2597-2605
ISSN: 0957-4174
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer INT ()
B-to-B marketing; text mining; web mining; acquisition; SVD

Abstract
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.

: http://publica.fraunhofer.de/dokumente/N-191044.html