• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.141-148
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• 2003

Customer retention is a common concern for many industries and a critical issue for the survival in today's greatly compressed marketplace. Current customer retention models only focus on detection of potential defectors based on the likelihood of defection by using demographic and customer profile information. In this paper, we propose a dynamic procedure for defection detection and prevention using past and current customer behavior by utilizing SOM and Markov chain. The basic idea originates from the observation that a customer has a tendency to change his behavior (i.e. trim-out his usage volumes) before his eventual withdrawal. This gradual pulling out process offers the company the opportunity to detect the defection signals. With this approach, we have two significant benefits compared with existing defection detection studies. First, our procedure can predict when the potential defectors could withdraw and this feature helps to give marketing managers ample lead-time for preparing defection prevention plans. The second benefit is that our approach can provide a procedure for not only defection detection but also defection prevention, which could suggest the desirable behavior state for the next period so as to lower the likelihood of defection. We applied our dynamic procedure for defection detection and prevention to the online gaming industry. Our suggested procedure could predict potential defectors without deterioration of prediction accuracy compared to that of the MLP neural network and DT.

• The Journal of Information Systems
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• v.21 no.4
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• pp.155-183
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• 2012

Means-Ends Network model was used to identify factors of means objective(means supplied by vendor) and fundamental objectives(purchasing motivations) for purchasing decision-making structure and dimensions of customer values on purchasers of internet shopping mall in Korea and China. In Means-Ends Network 6 factors(shopping travel, shipping assurance, vendor trust, online payment, product choice, and recommender systems) were found as a means objectives and 3 factors(shopping convenience, internet environment, customer support) as a fundamental objectives of shopping. However the results of hypotheses test for Means-Ends Network show some important differences between two countries. Something important to notice here is that Chinese customers shopping in China recognize shipping assurance factor and vendor trust factor as important factors satisfying all fundamental objectives unlike as in the case of our country. As these two factors are attribution factors responsible to the sellers, it is identified that customers do not trust the sellers and sellers have not met the expectations of customers. Therefore, these results show that the seller efforts assuring the reliability of the seller themselves, such as conducting its own compensation scheme are more important rather than the establishment of the guarantee institution to guarantee reliability and delivery assurance of sellers and implementation of legal and institutional apparatus such as the settlement of e-commerce licence system. Though this study presents such an important marketing implications, it can be pointed out that the limits are this research was done on the general Internet shopping malls without considering the Internet shopping mall types of diversity, the survey was designed around the student samples for convenience of the investigation because it was an international survey and the collected data has been limited to the western coast cities, such as China's Beijing, Shanghai, and Dalian.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.71
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• pp.325-350
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• 2016

As the new competitive environment become more global, technologically oriented and customer driven, as customers continually demand higher quality, faster response, and greater reliability of products and services, the new global market demands a more customer responsive behaviour by companies and firms have responded with innovative products and improved manufacturing processes to manufacture products. Further, the shift from traditional manufacturing and purchasing to JIT manufacturing and purchasing requires customers and suppliers to shift from adversarial relationships to strategic partnerships, and information sharing, so as to attain flexibility, reliability, and speed. SCM practices such as supplier collaboration and information sharing is considered as a key to attaining supplier network responsiveness and enhancing the product innovation and the market access time. The current research investigates the effect of SCM practices on supplier network responsiveness, the product innovation and the market access time of export companies. Thus by providing empirical evidence of the said relationships, this study offers useful guidelines for measuring and improving the supplier network responsiveness of a firm, facilitating further research in the area.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.10
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• pp.1368-1375
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• 2013

The Smart Grid, which can monitor or diagnose the power grid in real time to operate efficiently, has been pushed ahead systematically as one of alternatives to solve these issues by combining the advanced Information Communication Technology and the electrical network. Hence, the electric company which introduces smart grid technology can read remotely the electrical meter readings by means of two-way communication between the meter and the central system. This enabled the customer and the utility to take part in reasonable electrical energy utilization. AMI became one of the core foundations in realizing the Smart Grid. It is hard to test the entire process of AMI system before the full deployment because it covers the broad objects from the customer to the utility operation system and requires mass data handling and management. Therefore, we design an efficient AMI network model and a simulator for performance evaluation required to simulate the network model similar to the real environment. This tool supports to evaluate the efficiency of the AMI network equipments and deployment. Additionally, it estimates the appropriate number of deployments and the proper capabilities.

• Journal of Communications and Networks
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• v.9 no.3
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• pp.319-329
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• 2007

This paper presents a price-based quality-of-service (QoS) control framework for two-class network services, in which circuit-switched and packet-switched services are defined as "premium service class" and "best-effort service class," respectively. Given the service model, a customer may decide to use the other class as a perfect or an imperfect substitute when he or she perceives the higher utility of the class. Given the framework, fixed-point problems are solved numerically to investigate how static pricing can be used to control the demand and the QoS of each class. The rationale behind this is as follows: For a network service provider to determine the optimal prices that maximize its total revenue, the interactions between the QoS-dependent demand and the demand-dependent QoS should be thoroughly analyzed. To test the robustness of the proposed model, simulations were performed with gradually increasing customer demands or network workloads. The simulation results show that even with substantial demands or workloads, self-adjustment mechanism of the model works and it is feasible to obtain fixed points in equilibrium. This paper also presents a numerical example of guaranteeing the QoS statistically in the short term-that is, through the implementation of pricing strategies.

• The Korean Journal of Franchise Management
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• v.10 no.2
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• pp.59-70
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• 2019

Purpose - Every company studies how to attract and retain new customers to increase competitiveness and profitability. Companies establish strategies to attract customers, secure competitive advantage and generate revenue. Businesses are looking for newer and better ways to differentiate themselves in the marketplace. One of the requirements for service differentiation is to make it a prerequisite for an engaging customer experience. Customer experience can be attained through service experience. Satisfaction determine whether to reuse the food service franchise. The purpose of this study is to investigate the effect of customer experience on the satisfaction and revisit intention of food service franchise. In this study, customer experience consists of three attributes such as service environment, food quality, and price fairness. Also, this study is to identify the importance of three service experience attributes of customer satisfaction and revisit intention using ANN (artificial neural network) analysis. Research design, data, methodology - The survey was conducted on customers who have visited franchise restaurants in one month in order to examine how service environment, food quality, and price fairness have been influenced customer satisfaction and revisit intention through online survey company (SM culture & contents). A total of 300 representative surveys were collected. Of those collected surveys, 26 were not used due to missing information, resulting in 274 as the final sample size. The sample size was more than 10 times more than the number of variables used in the structural model analysis. Results - The findings of this study are as follows: Service environment and price fairness have a significant effect on satisfaction. However, food quality did not have a significant effect on satisfaction. Finally, it was found that satisfaction had a significant effect on revisit intention. Meanwhile, according to the results of ANN analysis, satisfaction as a dependent variable was found to be the most important in male price fairness and service environment in female. Also, when the revisit intention is used as a dependent variable, both male and female price fairness are important. Also, when the intention to revisit is used as a dependent variable, both male and female price processes are important. Conclusions - First, a restaurant franchise enterprise needs to manage customer service experience. Customers should strive to eat and enjoy at a dining franchise store. Second, it is necessary to design a food service franchise shop as a customer-oriented service environment. Franchise companies need to improve the environment so that customers can use the store conveniently. Third, the restaurant franchise menu price needs to be cheaper than the alternative menu. The restaurant franchise menu needs to be constructed with a popular menu that can be used continuously by the customer, so that it can be set at a reasonable price.

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

• Journal of the Korean Operations Research and Management Science Society
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• v.15 no.1
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• pp.117-133
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• 1990

This paper is concerned with determining a minisum location with jockeying for a server on a probabilistic network in which each customer type enters the network system permitting with jockeying through a specified node and a nonpreemptive service policy is in effect. An algorithm to locate a single Fixed Priority Queue Median with Jockeying (FPQMJ) on acyclic networks is developed by using the Generalized Benders' Decomposition technique. The results are then extended to a general network.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.412-414
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• 2005

Multimeda service is very big capacity and use not a little network for provide cots. Because in paper introduce new method adaptive to merit of Unicast and Multicast. Propose method service possibility that now Multicast have merit which live broadcasting and Unicast have merit which can provide individually customer manage and good quality by data statistics. And network use to efficient

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.167-174
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• 2002

Computer network provides a powerful infrastructure for information sharing and the development of the statistical software with new concepts. In this paper, we discuss the design concepts of the statistical software in the network environment.