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

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

Gregory Ramsey, Sanjay Bapna
Copyright: © 2016 |Volume: 3 |Issue: 2 |Pages: 17
ISSN: 2334-4547|EISSN: 2334-4555|EISBN13: 9781466693876|DOI: 10.4018/IJBAN.2016040102
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MLA

Ramsey, Gregory, and Sanjay Bapna. "Text Mining to Identify Customers Likely to Respond to Cross-Selling Campaigns: Reading Notes from Your Customers." IJBAN vol.3, no.2 2016: pp.33-49. http://doi.org/10.4018/IJBAN.2016040102

APA

Ramsey, G. & Bapna, S. (2016). Text Mining to Identify Customers Likely to Respond to Cross-Selling Campaigns: Reading Notes from Your Customers. International Journal of Business Analytics (IJBAN), 3(2), 33-49. http://doi.org/10.4018/IJBAN.2016040102

Chicago

Ramsey, Gregory, and Sanjay Bapna. "Text Mining to Identify Customers Likely to Respond to Cross-Selling Campaigns: Reading Notes from Your Customers," International Journal of Business Analytics (IJBAN) 3, no.2: 33-49. http://doi.org/10.4018/IJBAN.2016040102

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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.

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