• Proceedings of the Korean Society for Quality Management Conference
• /
• 2004.04a
• /
• pp.371-375
• /
• 2004

As the importance of customers has been emphasized, most companies began to operate various CRM strategies to understand and manage customers' needs. The investments that businesses are making are categorized into four areas. The first area of investments is in contact centers and channels to manage the voice of customers. The second area is in loyalty management, target marketing using segmentation, profiling, profitability analysis and targeting. The third one is involved in the measurement of customer satisfaction. The last one is planning to deliver products and services to appropriate customers. Despite the various efforts, it is lowering the efficiency of these investments and interrupting their value creation that these are being operated independently in different departments. All CRM activities of an enterprise should be processed interactively and consistently for a common goal; value creation, to overcome these shortcomings. In this research, we propose CS Road Map that systematizes the four kinds of CRM activities; VOC management, survey activities, loyalty management and planning. Under this road map, these four activities will achieve the improvement of service qualities, customer satisfaction and further value creation. This paper demonstrates the road map that is built for a service industry emphasizing the objectives and strategies of the four categories.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.3
• /
• pp.1146-1152
• /
• 2010

The Korean healthcare industry is rapidly changing along with the competition among hospitals. In the past, hospitals could make profit without designing competitive management strategies. Thus, they did not find importance in listening to customers' voices and identifying their wants. However, nowadays, the increasingly intense competition is encouraging hospitals to seriously consider competitive management strategies and Customer Relationship Management (CRM) activities to gain a competitive advantage and prosper. It tries to compare the expected satisfaction with the satisfaction of out-patient and in-patient and analysis of VOC(Voice of Customer). This survey was done from 27th, April, 2009 to 8th May and each 100 in-patients and out-patients. The paired t-test and descriptive analysis was used to analysis between before and after satisfaction. The result, the replied out-patients were the highest of I.M department, 43% and in-patients, surgery and other department are the highest each 22.0%. Nurses kindness is statistical significant in out-patients. Doctor, Nurse and staff's kindness and rounding service was statistical significant in in-patients. Totally, the satisfaction was lower than expected satisfaction, so the medical care institutions should analyst detailed the patient's satisfaction by VOC.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.4
• /
• pp.78-86
• /
• 2017

The new businesses started by the companies usually results in being unsuccessful. The main reasons for that are either aiming targeting wrong customers, unsatisfaction of customers' requesting quality standards, or taking wrong actions against the competitors in the market. Therefore, companies should aim the targets for the newly developing products based on the fulfilling values for the customers when they start the new businesses, and should take good cares for risk managements at the each step of the new business to prevent the failure in advance. In addition to that, the companies starting new businesses not only need to take the customers attributes (CA) into account, but they also should apply the new technologies as one system to initiate a new business to satisfy the basic wants of the customers. This article suggests the New Product Development Pursuing Model using the Indicative Planning methodology and the Quality Management tools. The New Product Development Pursuing Model would be completed by the following steps as below; 1. Drawing the CTQ (Critical To Quality) for setting up the new product development objectives by : i) using the VOC (Voice Of Customers) obtained by the QFD (Quality Function Deploypment) if the market is mature, ii) applying AHP (Analytic Hierarchy Process) to information in the QIS (Quality Information System) if the market is unmature to get enough need information of the customers. 2. Risk Management in NPD : The NPD pursuing model consisted of the IP (indicative planning) is suggested not by the process of top-down-way mandatory planning process, but by the tools used in the administrative science and economic fields, namely by governance. The companies could apply innovative methodology for new products development processes to fulfil the customers satisfaction in the fields, through the CA (Contingency Approach) of the NPD (New Product Development) process.

• International Journal of Quality Innovation
• /
• v.3 no.2
• /
• pp.159-173
• /
• 2002

Any organization's Six Sigma program may be at high risk without heeding the lessons learned from the past and that tries to operate without a robust business foundation. A foundation that preferably should consist of stepping-stones such as a 5-S house-keeping program, an effective Integrated Management System (IMS), which includes a strong focus on planning for quality to fully capture the Voice of the Customer (VOC), and an organization-wide training scheme, as well as a reliable Cost of Poor Quality (COPQ) system. That's the best advise I can give to any organization that wishes to embark on a Six Sigma improvement program and hope to be successful. The paper will elaborate on the above issues and provide suggested solutions based on the review of published historical information and the experiences encountered over the last four decades by the author, as a quality practitioner and consultant, in industries that produced safety-critical product. This author maintains that few fundamentally new or useful things have been created in the field of Quality during the last couple of decades. Nevertheless, this paper deliberates on a number of relatively “newer” issues including the concept of “three types of customers”, the CTC, “Critical To Customer” term, the eight Quality Management Principles of the new ISO 9000 family, the growth of industry-specific standards, the adoption of Integrated Management Systems, the rebirth of AS2561 COQ standard, the spread of Six Sigma as well as related ASQ certification and the need for a robust business foundation to ensure Six Sigma survival.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.221-241
• /
• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.