38 Mining the “Voice of the Customer” for Business Prioritization WEI PENG, TONG SUN, and SHRIRAM REVANKAR, Xerox Corporation TAO LI, Florida International University To gain competitiveness and sustained growth in the 21st century, most businesses are on a mission to become more customer-centric. In order to succeed in this endeavor, it is crucial not only to synthesize and analyze the VOC (the VOice of the Customer) data (i.e., the feedbacks or requirements raised by customers), but also to quickly turn these data into actionable knowledge. Although there are many technologies being developed in this complex problem space, most existing approaches in analyzing customer requests are ad hoc, time-consuming, error-prone, people-based processes which hardly scale well as the quantity of customer information explodes. This often results in the slow response to customer requests. In this article, in order to mine VOC to extract useful knowledge for the best product or service quality, we develop a hybrid framework that integrates domain knowledge with data-driven approaches to analyze the semi- structured customer requests. The framework consists of capturing functional features, discovering the overlap or correlation among the features, and identifying the evolving feature trend by using the knowledge transformation model. In addition, since understanding the relative importance of the individual customer request is very critical and has a direct impact on the effective prioritization in the development process, we develop a novel semantic enhanced link-based ranking (SELRank) algorithm for relatively rating/ranking both customer requests and products. The framework has been successfully applied on Xerox Office Group Feature Enhancement Requirements (XOG FER) datasets to analyze customer requests. Categories and Subject Descriptors: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval; I.2.6 [Artificial Intelligence]: Learning General Terms: Algorithms, Experimentation, Performance Additional Key Words and Phrases: Voice of the customer, business prioritization, text mining, ranking ACM Reference Format: Peng, W., Sun, T., Revankar, S., and Li, T. 2012. Mining the “voice of the customer” for business prioritiza- tion. ACM Trans. Intell. Syst. Technol. 3, 2, Article 38 (February 2012), 17 pages. DOI = 10.1145/2089094.2089114 http://doi.acm.org/10.1145/2089094.2089114 1. INTRODUCTION The goal of mining the “Voice of the Customer” (VOC) [Griffin and Hauser 1993] is to understand the needs of customers and transform them into key functional re- quirements. Instead of diving into product development directly by assuming that customers’ requests are known, we let customers speak their own concerns. To gain competitiveness and sustained growth in the 21st century, most businesses are on a mission to become more customer-centric. In order to succeed in this work, it is crucial The work is partially supported by a Xerox University Affair Committee (UAC) Award and by U.S. National Science Foundation (NSF) under grant IIS-0546280. Authors’ addresses: W. Peng, T. Sun, and S. Revankar, Xerox Innovation Group, Xerox Corporation, Webster, NY 14580; T. Li (corresponding author), School of Computer Science, Florida International University, 11200 SW 8th St., Miami, FL 33199; email: taoli@cs.flu.edu. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permit- ted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from the Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701, USA, fax +1 (212) 869-0481, or permissions@acm.org. c  2012 ACM 2157-6904/2012/02-ART38 $10.00 DOI 10.1145/2089094.2089114 http://doi.acm.org/10.1145/2089094.2089114 ACM Transactions on Intelligent Systems and Technology, Vol. 3, No. 2, Article 38, Publication date: February 2012.