Mining Call Center Dialog Data
Author(s)
A. Gilman, B. Narayanan & S. Paul
Abstract
We consider the problem of mining conversations between customers and call
center representatives for automatically classifying calls into predefined
categories. We analyze the conversations for speaker dependent information
content using several multi-class classification technologies. The data consists of
539 manually transcribed conversations belonging to 15 categories. Classifiers
were built using Support Vector Machines, Naïve Bayes, Latent Semantic
Analysis, Vector Space and K-Nearest Neighbor technologies. SVM classifiers
were found to perform consistently well giving an accuracy of about 74% on the
entire data and about 92% when considering only the 4 largest classes. It is
observed that very high weightage to either the customer part of the dialog or
that of the agent results in poor accuracy. Nearly equal weightage to the
customer and agent provides the best results consistently. This approach has
potential to identify cross sell and up-sell opportunities in real-time.
Keywords: text mining, classification, support vector machines, dialog mining,
customer relationship management.
1 Introduction
Customer Relationship Management (CRM) strategy and implementation are
essential for a business to retain its most valuable customers and boost overall
profitability. Information related to the products, customers and transactions is
aggregated in a CRM data warehouse. This data is then analyzed using
traditional analytics to build models for customer segmentation, propensity for
products, and cross sell opportunities. It is also recognized that a lot of
information about customer behavior and propensities may be gleaned from the
conversations between call center service representatives and customers. Dialog
Mining of these conversations – that may be transcribed to text automatically
Keywords
text mining, classification, support vector machines, dialog mining, customer relationship management.
Related Book
Other papers in this volume
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