• Culinary science and hospitality research
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• v.5 no.2
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• pp.447-467
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• 1999

Most published work on product quality focuses on manufactured goods. The subject of service quality has received less attention. This distinction is important because some of the quality-improving strategies avaliable to manufacturers may be inappropriate for service firms. Services are performances, not objects. They are often produced in the presence of the customer, as in the cause of hotel restaurant services, quality occurs during service delivery, usually in an interaction between the customer and contact personnel of service firm. for this reason, service quality is highly dependent on the performance of employees, an organizational resource that cannot be controlled to the degree that components of tangible goods can be engineered. The study has begun as a basic study for customer satisfaction-oriented management in understanding the service quality of food & beverage products and through a systematic analysis of it. The major purpose of the study was to examine the relationship of the customer satisfaction and service quality in consideration of reliability, empathy, responsiveness, tangibility and assurance. An empirical research was conducted based on the previous theoretical studies. 286 customer at first class hotels in Seoul were selected as samples of this study. The time period of research was from February through March 1999, and answers were processed by SAS to yield frequency analysis, multivariate statistical analysis and regression analysis. The finding of the statistical treatment are frequencies, factor analysis, multiple regression analysis, path analysis. SERVQUAL method was used the service quality evaluation methods. After factor analysis, it was resulted to 3 factors. those were factor 1(assurance.empathy.responsiveness), factor 2(reliability), factor 3(tangibility). The findings of the statistical treatment are as follows. First, the attribute measurement of performance service quality was affected by customer satisfaction. Second, the attribute measurement of performance service qualify was affected by repurchase intention. Third, The attribute measurement of performance customer satisfaction was affected by repurchase intention. The result of study model was followed, service quality was affected repurchase intention than customer satisfaction. indirected effect through, service duality and customer satisfaction was affected repurchase intention.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.3086-3101
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• 2021

To supply precise marketing and differentiated service for the electric power service department, it is very important to predict the customers with high sensitivity of electric power failure. To solve this problem, we propose a novel grouped 𝑙_1/2 sparsity constrained logistic regression method for sensitivity assessment of electric power failure. Different from the 𝑙₁ norm and k-support norm, the proposed grouped 𝑙_1/2 sparsity constrained logistic regression method simultaneously imposes the inter-class information and tighter approximation to the nonconvex 𝑙₀ sparsity to exploit multiple correlated attributions for prediction. Firstly, the attributes or factors for predicting the customer sensitivity of power failure are selected from customer sheets, such as customer information, electric consuming information, electrical bill, 95598 work sheet, power failure events, etc. Secondly, all these samples with attributes are clustered into several categories, and samples in the same category are assumed to be sharing similar properties. Then, 𝑙_1/2 norm constrained logistic regression model is built to predict the customer's sensitivity of power failure. Alternating direction of multipliers (ADMM) algorithm is finally employed to solve the problem by splitting it into several sub-problems effectively. Experimental results on power electrical dataset with about one million customer data from a province validate that the proposed method has a good prediction accuracy.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.197-206
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• 2019

Telemarketing has been used in finance due to the reduction of offline channels. In order to select telemarketing target customers, various machine learning techniques have emerged to maximize the effect of minimum cost. However, there are problems that the class imbalance, which the number of marketing success customers is smaller than the number of failed customers, and the recall rate is lower than accuracy. In this paper, we propose a method that solve the imbalanced class problem and increase the recall rate to improve the efficiency. The hybrid sampling method is applied to balance the data in the class, and the stacked deep network is applied to improve the recall and precision as well as the accuracy. The proposed method is applied to actual bank telemarketing data. As a result of the comparison experiment, the accuracy, the recall, and the precision is improved higher than that of the conventional methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.3-6
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• 2012

Due to recent rapid growth of mobile market, the modern people increasing make use of mobile contents as a means to obtain the desired information quickly by overcoming various restraints of a computer. The wide range of recommended contents, however, takes much time in selection of contents. To resolve such issues, a system that predicts the contents desired by the user and makes an accurate recommendation is necessary. In this paper, in order to provide the desired contents in line with the user demands, a method to increase select the number of recommendation using cooperative filtering is proposed. In the first step, the categories are formulated with super-classes and the similarity between the target customer and users is found, and the nearest-neighbors are constituted to find the preference predictions between super-classes, and the super-class with the highest resulting value is recommended to the target customer. In the second step, the preference predictions between sub-classes are found and the sub-class with the highest value is recommended to the target customer. In the experiment, mobile contents are recommended through super-class-based cooperative filtering, and then the mobile contents are recommended through sub-class-based cooperative filtering, and sub-class collaborative filtering method to select a high number of verification.

• Korean Institute of Interior Design Journal
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• no.34
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• pp.124-131
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• 2002

Lighting is the single most important part of the design of a retail store. Good lighting can enhance a product's appearance, accentuate a special display, balance the visual elements of a store, and create the proper mood. In shops the lighting is required to draw the customer's attention to the goods help him to appraise them, and provide an atmosphere conducive to sales. The method will vary according to the types of good displayed, the form of trading, the design of the shop, and the class of customers expected. For the all reason, this study is focused on how the lighting design effects the customer's interest to the goods and intended to prescribe the possibilities of effective lighting design.

• IE interfaces
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• v.14 no.3
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• pp.236-246
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• 2001

The purpose of the study is to identify specific tools and joint practices used in customer-supplier partnerships and to investigate performance outcomes resulting from using the identified tools and joint practices. To achieve the purpose, related literatures in the area of marketing, purchasing, and management systems engineering are reviewed. Successful and world-class supply and/or supplier management cases are examined in-depth as well. Before addressing the purpose of this study, quality experts' assertions on and historical perspective of Supply Chain Management(SCM) and general issues on customer-supplier partnerships are also mentioned.

• Journal of Distribution Research
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• v.2 no.1
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• pp.59-85
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• 1997

The automobile industry in Korea has grown to the fifth in the world in terms of production capacity. In spite of the production growth, the marketing aspects such as distribution and customer service in the auto industry are still behind the world-class. Thus, the major purposes of this paper are as follows. The first is to analyze competitive structure of the industry and to compare distribution strategies of the major auto firms(Hyundai, Daewoo, and Kia). The second is to theoretically explain the transition from the vertical marketing system to the dealer system using transaction cost analysis. The third is to compare auto distribution channels in Korea with those in the U.S. and Japan. Finally, an optimal channel stucture in the auto industry is suggested after reviewing five alternative channel structures such as corporate-owned VMS, sales-specializing firm, multiplex system(VMS+limited dealer system), dual sales channel, and advanced dealer system. In the short-run, sales-specilizing firm was suggested as an optimal channel system to enhance customer satisfaction by integrating sales and customer service. In the long-run, advanced dealer system through regional differentiation was desirable for an optimal channel structure by organically integrating new car sales, used car sales, and after service to provide total marketing service to customers.

• Journal of Korean Library and Information Science Society
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• v.35 no.3
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• pp.251-270
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• 2004

The purpose of this study is to introduce a customer relationship management(CRM) for more user satisfied information in through the relationship between an user-centered library management and library customers. The characteristics of library customer information needs and a general CRM system design are introduced. The result shows a plan for library CRM system. It is included a conceptual modeling design for the CRM system, a data dictionary, and event class.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.591-593
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• 2007

This paper is to cluster the AMR (Automatic Meter Reading) data. The load survey system has been applied to record the power consumption of sampling the contract assortment in KEPRI AMR. The effect of the contract assortment change to the customer power consumption is determined by executing the clustering on the load survey results. We can supply the power to customer according to usage to the analysis cluster. The Korea a class of the electricity supply type is less than other country. Because of the Korea electricity markets exists one electricity provider. Need to further divide of electricity supply type for more efficient supply. We are found pattern that is different from supplied type to customer. Out experiment use the Clementine which data mining tools.