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

Content Provider	Semantic Scholar
Author	Ramsey, Gregory W. Bapna, Sanjay
Copyright Year	2016
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 Naive 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.
Starting Page	33
Ending Page	49
Page Count	17
File Format	PDF HTM / HTML
DOI	10.4018/IJBAN.2016040102
Volume Number	3
Alternate Webpage(s)	http://www.igi-global.com/pdf.aspx?tid%3D149149%26ptid%3D132221%26ctid%3D15%26t%3Dtable+of+contents
Alternate Webpage(s)	https://www.igi-global.com/viewtitlesample.aspx?id=149154&ptid=132221&t=text+mining+to+identify+customers+likely+to+respond+to+cross-selling+campaigns:+reading+notes+from+your+customers
Alternate Webpage(s)	https://doi.org/10.4018/IJBAN.2016040102
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Notes