• Journal of the Korea Safety Management & Science
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• v.7 no.1
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• pp.199-210
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• 2005

Customer segmentation prediction has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. However, ANN approaches have suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learning technique, support vector machines (SVM), to the customer segmentation prediction problem in an attempt to provide a model with better explanatory power. To evaluate the prediction accuracy of SVM, we compare its performance with logistic regression analysis and ANN. The experiment results with real data of insurance company show that SVM superiors to them.

• Asia Marketing Journal
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• v.10 no.4
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• pp.99-121
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• 2009

Social networks can be a powerful force in marketing because they provide new ways to market to young generation. Though many studies on evaluation of customer lifetime value have been conducted, it is not clear how to assess the value of members within social networks. The purpose of this study is to evaluate members based on customer intangible value as well as customer lifetime value. Customer network value in terms of the power and influence within a network is analyzed through network structure analysis. Using Cyworld log file data, this study have shown that high percentage of members are very influential in terms of spreading or withholding information even though their CLV is low. It is expected that the findings of this research contribute to understand the interactive behaviors of members within networks and to provide valuable implications on new product launching and customer management strategies to marketers.

• The Journal of Information Systems
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• v.18 no.3
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• pp.375-399
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• 2009

In this study, we propose a integrated model of logistic regression, artificial neural networks, support vector machines(SVM), with case-based reasoning(CBR). To predict respondents in the direct marketing is the binary classification problem as like bankruptcy prediction, IDS, churn management and so on. To solve the binary problems, we employed logistic regression, artificial neural networks, SVM. and CBR. CBR is a problem-solving technique and shows significant promise for improving the effectiveness of complex and unstructured decision making, and we can obtain excellent results through CBR in this study. Experimental results show that the classification accuracy of integration model using CBR is superior to logistic regression, artificial neural networks and SVM. When we apply the customer response model to predict respondents in the direct marketing, we have to consider from the view point of profit/cost about the misclassification.

• Health Policy and Management
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• v.13 no.3
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• pp.21-34
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• 2003

Patient revisit to used hospital is a key factor in determining a health care organization's competitive advantage and survival. This article examines the relationship between customer's satisfaction and his/her revisit associated with three different methods which are the Chi Square Automatic Interaction Detection(CHAID) for segmenting the outpatient group, logistic regression and neural networks for addressing the outpatient's revisit. The main findings indicate that the important factors on outpatient's revisit are physician's kindness, nurse's skill, overall level of satisfaction, hospital reputation, recommendation, level of diagnoses and outpatient's age. Among these ones, physician's kindness is the most important factor as guidelines for decision of their revisit. The decision maker of hospital should select the strategy containing the variable amount of the level of revisit and size of outpatient's group under the constraint on the hospital's time, budget and manpower given. Finally, this study shows that neural networks, as non-parametric technique, appear to more correctly predict revisit than does logistic regression as a parametric estimation technique.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.245-248
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• 1998

In this paper, as a forecasting method, the market survey for forecasting demand is introduced for the estimation of subscriber line demand in the optical access networks. The market survey method for the new multimedia services is attempted to collect information directly from customers using the questionnaires for home-users and business-users in local loops. Analysis rationale of questionnaires is suggested to estimate the number of subscriber lines. Also, two measures are presented to quantify the credibility on survey responses; one is the probability that the customer will use the multimedia services and the other is the rate that the subscriber line demand will be actually realized. The former measure is calculated based on the information on customers and the Logit analysis. The latter is obtained by the degree of customer's knowledge about specific services and the customer's willingness to use the services. Based on the values of two measures, the number of subscriber line demand can be developed for installing the optical access networks.

• Proceedings of the Optical Society of Korea Conference
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• 2006.07a
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• pp.525-526
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• 2006

Ethernet Passive Optical Networks (EPON) are an emerging access network technology that provide a low-cost method of deploying optical access lines between a central office (CO) and a customer site. This paper shows that subscriber unit manages traffic data between a central office (CO) and a customer site in economic solution.

• Journal of Intelligence and Information Systems
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• v.10 no.3
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• pp.115-132
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• 2004

Recently, as most companies recognize the importance of the customer relationship management, they strongly believe that they must know who their customers are. The job of a customer is very important information for us to understand the customer. However, since most customers are reluctant to reveal them-selves, they do not let us know their jobs, and even provide false information about their jobs. The target domain of our research is mobile telecommunication. In this research, we developed a system that identifies the customer's job by utilizing the Call Detail Record. Using artificial neural networks, we developed a two-step Job Identification System. In the first step, it identifies the four job classes, then in the second step, it subdivides these four job classes into seven jobs. The accuracy of identifying the seven jobs was $71.9\%$.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1387-1390
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• 2005

Recently, foreign utilities emphasize the importance of the value-added services based on Information Technology(IT) as one of the strategic technologies for establishing a new power system in the future digital society. They develop many different types of the value-added services and apply the systems for customer. In domestic case, the data from the Automatic Meter Reading (AMR) System is used only for calculating the tariffs. Data from the AMR system can be strategic assets for utilities to provide the value-added services for customer. Development of the value-added services for utilities and customer needs processing and managing the AMR data. In this paper, the energy consulting service, which is adequate to new power system environment, is suggested for development of the value-added services. The application of the suggested service will bring the effect of reducing the monthly bill for customer. Also the service will give not only the effective demand side management(DSM) and load control, but also reduction of the investment for utilities.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.243-252
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• 2001

Recently, as most companies recognize the importance of the customer relationship management, they strongly believe that they must know who their customers are. The job of a customer is very important information for us to understand the customer. However, since most customers are reluctant to reveal themselves, they do not let us know their jobs, and even provide false information about their jobs. The target domain of our research is mobile telecommunication. In this research, we developed a system that identifies the customer's job by utilizing the Call Detailed Record. From the Call Detailed Record, we extracted such variables as 'Average Monthly Payment'and 'Age of the Customer'and so forth. Moreover, we generated many summary variables and derived variables such as 'Number of Calls during Work Hours in Weekday', and 'Ratio of Calls from other Mobile Telephone'. Using artificial neural networks, we developed a two-step Job Identification System. In the first step, it identifies the four job classes then in the second step, it subdivides these four job classes into seven jobs. The accuracy of identifying the seven jobs was 69.1%.

• Proceedings of the Korea Database Society Conference
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• 1994.09a
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• pp.147-161
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• 1994

Information Superhighway $\textbullet$ Definition - Communication of any type, anytime, anywhere - A material interactive, multimedia highway that is open, interconnected, and accessible to all-- a "netework of networks" $\textbullet$ Key Principles o "network of networks" o customer choice throught competition o fair and open access o open standard(omitted)