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 (Department of Information Science and Systems, Morgan State University, Baltimore, MD, USA) and Sanjay Bapna (Department of Information Science and Systems, Morgan State University, Baltimore, MD, USA)
Copyright: © 2016 |Pages: 17
DOI: 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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Background

Companies are spending large amounts on systems for managing and servicing customers, mostly in the form of CRM systems. But, many companies are failing to see the expected returns with an estimated 55% of CRM projects failing (Rigby, Reichheld, & Schefter, 2002) . Regardless of whether success or failure is achieved, companies are using CRM systems and related technologies to become more customer-centric. Customer-oriented companies have the ability to mass customize and personalize information for individual customers (Teo, Devadoss, & Pan, 2006). CRM enables businesses to acquire, get to know, and service customers according to their specific needs. CRM systems have three primary types of functionality, which are operational, analytical, and collaborative (Teo, et al., 2006). Of greatest interest in the present study is the collaborative functionality because it enables a business to interact with its customer to better service them.

Collaborative CRM systems are a means to engage customers in relationship marketing. Relationship marketing is attracting, maintaining, and developing customer relationships (Voss & Voss, 1997). Relationship marketing requires a deep and personalized understanding of the customer’s needs and characteristics, something service industries are well suited to do because of their personalized interactions with their customers (Voss & Voss, 1997).

This study develops and tests a method for using customer service notes captured from a call center and stored within a service company’s CRM repository with the goal of predicting which customers are likely to positively respond to a cross-selling campaign. It is well recognized that companies can increase their profits by identifying and altering marketing approaches to their different customer segments (Zeithaml, Rust, & Lemon, 2001). We show that text mining free-form customer service notes as a way to segment customers can result in predictive models that are as good as or better than models developed using only structured data.

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