• 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%.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.29 no.4
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• pp.603-609
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• 2019

Objectives: This case report aims to contribute to the enhancement of training quality for occupational accident prevention by conducting surveys on customers' satisfaction with the training course management by the Occupational Safety and Health Training Institute (OSHTI) of KOSHA. Methods: Surveys were conducted through phone calls, customer service documents, and questionnaires from January 1, 2018 to December 31, 2018. Results: The surveys showed an answer rate of 97.36% and handling rate of 97.47% in 2018, an increase of 1.15% compared to 2017. The number of monthly inbound calls in 2018 was 5,902, rising 0.10% year-on-year, and the average inbound calls per day in 2018 was 289, a decline from 291 the year before. The number of provisions of customer service in 2018 was 68,952, increasing 1.89% year-on-year. The number of inquiries on the training curriculum was 58,744 in 2018, an increase of 3.98% compared to the 56,498 recorded in 2017. Inquiries on job training centers were the most common, numbering 27,114 (39.32%), followed by e-learning 18,470 (26.79%) and expert courses 13,160 (19.09%). Of the 149 answers to the customer service survey, 'Nothing to complain about' accounted for 86 (56.72%) and 'Diversifying training time and programs' numbered 22 (14.77%). Conclusions: Customer satisfaction in 2018 increased compared to that in 2017. However, there is a need to reflect the demands of customers for diversifying training time and programs, offer practice-centered training, and collect opinions on providing information in order to maintain high quality training course management.

• International Journal of Costume and Fashion
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• v.10 no.2
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• pp.73-88
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• 2010

Study aims to identify customer equity drivers and their relative importance, to represent customer lifetime value (CLV) distribution, and to investigate the effect of customer equity drivers and demographics on CLV when shopping apparels at the four big department stores in Seoul. Recently, Korean department stores marked significant decrease in sales volume and it calls for more focus on customer orientation. Customer equity is a managerial concept which considers customers as a valuable asset for business success. Sustainable competitive advantage is attainable when customer equity drivers and CLV are measured, managed and enhanced. results identified four dimensions of customer equity drivers such as 'retail brand equity: 'relationship equity', 'retail service equity', and 'price value equity'. Among them, 'relationship equity' was proved to be the most influencing factor on the customer's store patronage intention. The CLV distribution represented unique characteristics of each department store. The level of CLV depended on such demographics as age and income. Marital status influenced the relationship between perceived customer equity drivers and CLV. It also analyzed competitive structure of the four big department stores in Seoul and offered managerial suggestions. This study provided conceptual framework for the future study of customer equity related to apparel shopping at the department stores as well as managerial implications.

• Journal of Agricultural Extension & Community Development
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• v.18 no.4
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• pp.709-728
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• 2011

It might be considered as difficult process to apply current researching technology to the fields of agriculture. Before the 80's, distributing brochures and pamphlet and direct visits to farmers were the only means used for research. But as society evolved, so did the methods. Media began to its place in the process. This study's main objective was to identify how farmers solve the problem on farming itself and also on how they improve customer-satisfaction. The Service Consultation Center of Rural Development Administration totaled up 19,666 cases of farmers's inquiries on 2010 by internet surveys, phone-calls and by post. Customer satisfaction scored highest on horticulture (39.5%), being followed by rice(23.2%) and other crops.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.

• Asia-Pacific Journal of Business
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• v.14 no.4
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• pp.289-303
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• 2023

Purpose - The purpose of this study is to investigate the types and forms of various marketing activities actually used in pharmaceutical companies and to empirically analyze the impact of these marketing activities on sales. Design/methodology/approach - This study categorize five years' worth of marketing activity data from a foreign pharmaceutical company 'A' which operates in South Korea into five categories. Multiple regression analysis and interaction effects are employed for data analysis. Findings - First, CRM calls, Detail calls, GP, and Web events have a positive impact on sales, but SoV does not show significant differences. Second, in the comparison between HQ1 and HQ2 based on patent ownership, Detail calls and Web events had a stronger impact on sales in HQ2, where the patent period is still in effect, compared to HQ1. However, SoV showed no difference between HQ1 and HQ2. Research implications or Originality - First, Detail Calls are more effective for drugs with active patents, while CRM Calls work better for drugs with expired patents. This emphasizes the need to customize call strategies based on patent status. Second, the significant impact of Web Events on sales in HQ2 compared to HQ1 suggests that online information access is crucial, indicating that customer receptivity varies based on product nature. Third, these insights, derived from data analysis, call for a shift in pharmaceutical marketing analysis methods away from traditional approaches. Finally, this study holds significance as one of the first empirical analyses using actual marketing data from pharmaceutical companies in South Korea.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.115-131
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• 1998

Customer service process is one of the most important processes in today's competitive business environment. Among the various activities of customer service process, equipment malfunction diagnosis activity should be performed fast and accurately. When a customer calls the service center and reports the observed symptoms, he/she describes them in layman's terms. Therefore, the customer-reported symptoms have not been considered helpful information for service representatives. However, in order to perform diagnosis activity fast and accurately, we need to make use of the customer-reported symptoms actively. In this research, we developed three systems called R-EMD (Rule-based Equipment Malfunction Diagnostic system), C-EMD (Case-based Equipment Malfunction Diagnostic system) and R&C-EMD (Rule & Case-based Equipment Malfunction Diagnostic system), each of which diagnoses equipment malfunctions using the customer-reported symptoms. R&C-EMD is a hybrid system that utilizes both rule-based and case-based technologies. The diagnosis rules used in R&C-EMD and R-EMD were not acquired from service manuals or interviews with service representatives. Rater, we extracted them directly from the past diagnosis cases based on symptoms' frequencies. By this way, we were able to overcome the knowledge acquisition bottleneck. Using the real 100 malfunction diagnosis cases, we evaluated the performances of R&C-EMC, R-EMD and C-EMD in terms of speed and accuracy. In diagnosis time, R&C-EMD took longer than R-EMD and shorter than C-EMD. However, R&C-EMC was the best in accuracy.

• Journal of the Korea Society for Simulation
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• v.27 no.2
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• pp.91-100
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• 2018

Recently, the traditional call center, which has an important role as the customer's primary contact point, has been transformed into the omni-channel contact center which supports an additional variety of access channels in addition to phone calls in order to improve customer accessibility and convenience. The omni-channel contact center has agents on each channel which are vital in determining the response rate and waiting time for the customer. In traditional call centers, agents can be assigned to a call based on the characteristics of the call, however in omni-channel contact centers, the characteristics of issues vary and come in through multiple channels, each with their own characteristics, making it difficult to assign the appropriate agent for the issue. Customers must also be processed at the same time, adding an additional layer of complexity to the issue. This paper analyzes and simulates an omni-channel contact center that accommodates email, chat, voice calls and video consultation, and investigates how to optimize the allocation of agents.

• Journal of Distribution Research
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• v.9 no.2
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• pp.29-43
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• 2004

This research analyzed multi-channel strategy from the viewpoint of the customer relationship management. We hypothesized that the purchase frequency, monetary, purchase quantity of the existing customers should have increased after they used multiple channels of a company for shopping, All the hypothesis were supported in an empirical test using the customer database of a Korean TV home-shopping company, The result showed that the multi-channel strategy can be used to increase the life-time value of existing customers. Still there were a lot of customers who insists using traditional channel, which calls for a new strategy inducing them to use multi- channel for shopping.

• Journal of Korean Society for Quality Management
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• v.22 no.2
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• pp.79-88
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• 1994

Delay has influenced customer satisfaction while using a telephone. It has been ranked second after transmission impairments in annoyance to customers. Therefore, it is important to analyze its behavior not only to increase the quality of service for the customers, but to provide its information to network planners and administrators and to designers of equipment and systems. In this paper, we consider a log-normal model as a candidate for describing the statistical behavior of the post dialling delay, i.e., the time to connect a call to the telephone network after dialling. Based on the real data, the log-normalness of the post dialling delays are convinced for the long distance calls, but not supported for the local calls. Uses of graphical plotting paper and statistical test of fit art discussed.