Text Mining to Identify Customers Likely to Respond to Cross-Selling Campaigns: Reading Notes from Your Customers

Gregory Ramsey and Sanjay Bapna
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Gregory Ramsey: Department of Information Science and Systems, Morgan State University, Baltimore, MD, USA
Sanjay Bapna: Department of Information Science and Systems, Morgan State University, Baltimore, MD, USA

International Journal of Business Analytics (IJBAN), 2016, vol. 3, issue 2, 33-49

Abstract: This paper reports on the results of extracting useful information from text notes captured within a Customer Relationship Management (CRM) system to segment and thus target groups of customers likely to respond to cross-selling campaigns. These notes often contain text that is indicative of customer intentions. The results indicate that the notes are meaningful in classifying customers who are likely to respond to purchase multiple communication devices. A Naïve Bayes classifier outperformed a Support Vector Machine classifier for this task. When combined with structured information, the classifier performed only marginally better. Thus, customer service notes can be an important source of predictive data in CRM systems.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jban00:v:3:y:2016:i:2:p:33-49

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