• Journal of Korean Library and Information Science Society
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• v.46 no.1
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• pp.25-42
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• 2015

The purpose of this study is to provide users with satisfactory information services that are utilizing Voice of the Customers(VOC). Voice of the Customers(VOC) toward organizations that provide information services is important information for decision-making to improve customer satisfaction and information services. K institute manages customer requests and feedbacks for NDSL(National Digital Science Library) using the Voice of the Customer Management System(VCMS). In this study, we analyzed the total number of 1,738 VOCs and suggested improvement strategies for information services. The results can be utilized as basic information by libraries and information centers that provide information services through analysis of VOC.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.4
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• pp.13-24
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• 2015

Customer complaints handling helps organizations to retain existing customers and attract new customers, as well. As Voice of the Customer (VOC) is one of the main sources of customer complaints, many organizations utilize VOC to enhance customer satisfaction. Effective management of VOC has been proved as one of the best ways to maintain organization's brand image and reputation. In spite of its importance, little has been reported on the utilization of VOC to detect faults in a telecommunication industry. In this paper, association rule mining based on VOC is used to identify root fault causes of a telecommunications network. To do that, VOC of a Communication Service Provider has been collected first. Then, association rule mining has also been conducted with various support and confidence levels. As a result, root fault causes of the telecommunications network can be identified. It is expected that this study can be used as a basis for decisions about customer satisfaction management such as preventive maintenances or reduction of the customer maintenance cost.

• Journal of Distribution Science
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• v.7 no.2
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• pp.89-119
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• 2009

The purpose of this research is showing an appropriate way of maximizing customer service and establishing VOC system by analyzing different voices from complaining customers as well as loyal customers. This research is also aimed at figuring out how companies can implement effective service marketing methods in the field complying with customers' needs and how they can survive in the competition. The range of research is confined to 5 marketing companies and their web-sites on which customers can get logged and directly post their claims. These web-sites showed how those 5 companies cope with customer claims. A questionnaire research was made in A's store to evaluate customer satisfaction. These are conclusions drawn by this research. First, prompt reactions of sincerity to customers' claims contribute to building favorable corporate images. Second, the preference to VOC channels varies with age and sex. Marketers should implement respectively different channels for customers under age 30 and those over age 40. Women have a tendency to prefer instant phone conversations and want to have their claims well listened to. Third, a series of shift in customer trend drives companies into establishing their own interactive VOC systems based on customers' preferences. Customer-oriented management has become a key factor for survival in recent intensely competitive market situation, as the web-based e-commerce market has been rapidly growing accompanied with a dramatic advance of network marketing methods. This research suggests some practical methods to establish a customer-oriented VOC system that can be easily adopted in the field.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.197-210
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• 2016

With the advent of text analytics, VOC (Voice of Customer) data become an important resource which provides the managers and marketing practitioners with consumer's veiled opinion and requirements. In other words, making relevant use of VOC data potentially improves the customer responsiveness and satisfaction, each of which eventually improves business performance. However, unstructured data set such as customers' complaints in VOC data have seldom used in marketing practices such as predicting service time as an index of service quality. Because the VOC data which contains unstructured data is too complicated form. Also that needs convert unstructured data from structure data which difficult process. Hence, this study aims to propose a prediction model to improve the estimation accuracy of the level of customer satisfaction by combining unstructured from textmining with structured data features in VOC. Also the relationship between the unstructured, structured data and service processing time through the regression analysis. Text mining techniques, sentiment analysis, keyword extraction, classification algorithms, decision tree and multiple regression are considered and compared. For the experiment, we used actual VOC data in a company.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4706-4724
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• 2020

With various structured data, such as the company size, loan balance, and savings accounts, the voice of customer (VOC), which is text data containing contact history and counseling details was analyzed in this study. To analyze unstructured data, the term frequency-inverse document frequency (TF-IDF) analysis, semantic network analysis, sentiment analysis, and a convolutional neural network (CNN) were implemented. A performance comparison of the models revealed that the predictive model using the CNN provided the best performance with regard to predictive power, followed by the model using the TF-IDF, and then the model using semantic network analysis. In particular, a character-level CNN and a word-level CNN were developed separately, and the character-level CNN exhibited better performance, according to an analysis for the Korean language. Moreover, a systematic selection model for optimal text mining techniques was proposed, suggesting which analytical technique is appropriate for analyzing text data depending on the context. This study also provides evidence that the results of previous studies, indicating that individual customers leave when their loyalty and switching cost are low, are also applicable to corporate customers and suggests that VOC data indicating customers' needs are very effective for predicting their behavior.

• KIISE Transactions on Computing Practices
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• v.22 no.1
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• pp.50-55
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• 2016

To attain an understanding of customers' opinions or demands regarding a companies' products or service, it is important to consider VOC (Voice of Customer) data; however, it is difficult to understand contexts from VOC because segmented and duplicate sentences and a variety of dialog contexts. In this article, POS (part of speech) and morphemes were selected as language resources due to their semantic importance regarding documents, and based on these, we defined an LSP (Lexico-Semantic-Pattern) to understand the structure and semantics of the sentences and extracted summary by key sentences; furthermore the LSP was introduced to connect the segmented sentences and remove any contextual repetition. We also defined the LSP by categories and classified the documents based on those categories that comprise the main sentences matched by LSP. In the experiment, we classified the VOC-data documents for the creation of a summarization before comparing the result with the previous methodologies.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.3 s.180
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• pp.7-11
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• 2006

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.1
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• pp.66-78
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• 2008

In the past, Manufacturers used to determine the quality of products, but the trend of today's market becomesmore into customer-driven. As a result, demands from customers are becoming more diverse and complicated,and most companies are obligated to meet their needs. As one of the effort to achieve their satisfaction,companies are now emphasizing activities to find out what customers specifically want and extract voice ofcustomer(VOC). This study attempts to develop an ergonomic product development process as a method tomaximally reflect the VOC. In order to meet this goal, ergonomic design guidelines, which are possible to beclassified according that user's human characteristics, will be recommended. Even now, there are numerousdesign guidelines already existing in the ergonomics literature. However, it is not realistically feasible to reviewall of those guidelines, and some of them are even conflicting with each other. Therefore, in this paper, theproduct development process, which prioritizes the human characteristics that reflect customer needs and appliesthe design guidelines that meet the most important ones, will be suggested. Finally, the research was described toshow the validity of the product development process through an example of a mobile phone development case.

• The Research Journal of the Costume Culture
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• v.17 no.3
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• pp.484-498
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• 2009

The purpose of this study is to explore the applicability of QFD to product development of clothes by empirically applying it to development of a specific clothes, middle and elderly women’s formal knitwear. The voices of customers(VOCs) for formal knitwear was collected through in-depth interviews with 25 customers, shop masters, and designers. Also, questionnaires of which respondents were 230 customers were used to rank the importance of the items of VOC. A QFD team of 10 knitwear experts implemented the task of translating VOCs into design attributes and measuring the values of the relationships between VOCs and design attributes. Importance ranking of the items of design attributes was obtained based on Lyman's method. The results of this study were as follows. First, the customer requirements for formal knitwear were classified into five dimensions, that is, symbolism, aesthetic, fitness, usefulness, and maintenance. Second, the descending order of the necessity of improving the quality was maintenance, aesthetic, fitness, usefulness, and symbolism. Third, three-staged design attributes were obtained as a result of translating of VOCs into design attributes. Lastly, the descending order of the importance of design attributes was "sorts of yarn", "sorts of color jacquard", "color", "tone", "ease", etc.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.