• Journal of Information Technology Applications and Management
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• v.20 no.2
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• pp.15-37
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• 2013

In this study, the validity of CRM introductory is defined as a driving force for the introduction of technology and concepts such as competence factors of CRM. Effect on the ability to verify the information technology CRM using this concept, we examined the effect of force CRM information technology has on the outcome from the point of view of the customer interaction. And we have tested the moderate effect for size of the company and the industry shape to the relationship between the adequacy and implementation of CRM. As a result, technical adequacy and competence of CRM implementation CRM, has a significant causal relationship to CRM information technology capability. Competence of CRM implementation has a causal relationship with care for the outcome of the interaction of the customer, shows the validity of the introduction of CRM companies are seeking Modulatory effect was verified using the company's size and industry classification, was significant only for the classification of industries. This result shows that must find ways to introduce the CRM industry depending on the form of different.

• 한국경영정보학회:학술대회논문집
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• 2007.11a
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• pp.527-532
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• 2007

Differentiation strategies have been suggested as the critical sources of competitive advantage in B2C industry where customers can switch internet shopping mall with one click with virtually no transaction cost. Indeed, competition on low pricing cannot be a viable strategy in B2C industry. Moreover, cultivating customer loyalty to attain profitability is still a challenging task for most internet shopping mall. In this study, we provide empirical analysis results on key managerial variables that indicate the difference between the product categories in terms of customer perception on relative value importance. We first identified comprehensive managerial variables and organized them in terms of customer decision stage. Next, with reference to extant literatures on product characteristics based e-commerce strategy, hypotheses are developed to formalize the customer value differences on the key managerial variables. Empirical testing results indicated that there are significant differences on customer perceived value of the key managerial variables between the product groups. The findings provide useful insight for further study on e-commerce differentiation strategy.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.26-30
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• 2008

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed our approach consists of three stages: (i) data pre-processing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.

• The Journal of Industrial Distribution & Business
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• v.13 no.10
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• pp.1-8
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• 2022

Purpose: Support vector machines (SVMs) ensemble has been proposed to improve classification performance of Credit risk recently. However, currently used fusion strategies do not evaluate the importance degree of the output of individual component SVM classifier when combining the component predictions to the final decision. To deal with this problem, this paper designs a support vector machines (SVMs) ensemble method based on fuzzy integral, which aggregates the outputs of separate component SVMs with importance of each component SVM. Research design, data, and methodology: This paper designs a personal credit risk evaluation index system including 16 indicators and discusses a support vector machines (SVMs) ensemble method based on fuzzy integral for designing a credit risk assessment system to discriminate good creditors from bad ones. This paper randomly selects 1500 sample data of personal loan customers of a commercial bank in China 2015-2020 for simulation experiments. Results: By comparing the experimental result SVMs ensemble with the single SVM, the neural network ensemble, the proposed method outperforms the single SVM, and neural network ensemble in terms of classification accuracy. Conclusions: The results show that the method proposed in this paper has higher classification accuracy than other classification methods, which confirms the feasibility and effectiveness of this method.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.231-234
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• 2008

Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in load demand data. The main aim of our work is to forecast customers' contract information from capacity of daily power consumption patterns. According to the result, we try to evaluate the contract information's suitability. The proposed our approach consists of three stages: (i) data preprocessing: noise or outlier is detected and removed (ii) cluster analysis: SOMs clustering is used to create load patterns and the representative load profiles and (iii) classification: we applied the K-NNs classifier in order to predict the customers' contract information base on power consumption patterns. According to the our proposed methodology, power load measured from AMR(automatic meter reading) system, as well as customer indexes, were used as inputs. The output was the classification of representative load profiles (or classes). Lastly, in order to evaluate KNN classification technique, the proposed methodology was applied on a set of high voltage customers of the Korea power system and the results of our experiments was presented.

• ETRI Journal
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• v.38 no.3
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• pp.494-501
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• 2016

Sentiment analysis incorporates natural language processing and artificial intelligence and has evolved as an important research area. Sentiment analysis on product reviews has been used in widespread applications to improve customer retention and business processes. In this paper, we propose a method for performing an intensified sentiment analysis on customer product reviews. The method involves the extraction of two feature sets from each of the given customer product reviews, a set of acoustic features (representing emotions) and a set of lexical features (representing sentiments). These sets are then combined and used in a supervised classifier to predict the sentiments of customers. We use an audio speech dataset prepared from Amazon product reviews and downloaded from the YouTube portal for the purposes of our experimental evaluations.

• Journal of Korean Library and Information Science Society
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• v.37 no.3
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• pp.193-208
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• 2006

The customer satisfaction. which is close with the loyalty, rate of disconnection and re-purchase and the new customer creation is important in point of the enterprise performance measurement system. There have been a number of studies that applied different models in other to assess the customer satisfaction of public and non-public area. But the general evaluation models which are existing can't consider the discrimination characteristic of different types of products or services. And these models didn't reflect the quality of the Internet environment of the public library service which appears newly. This study delved into literature of library service and customer satisfaction evaluation and suggest the classification system of public library service and the evaluation model of customer satisfaction for public library.

• Journal of the Korean Society for information Management
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• v.25 no.3
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• pp.119-141
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• 2008

This study examines firstly concept and present condition of Policy Customer Relationship Management(PCRM) system which applying in the governmental dimension Customer Relationship Management(CRM) system of enterprises(proft-making organizations), comparison of PCRM and CRM, and definition of customer and customer classification process as a key of PCRM. Next, investigates applying plan in the other special libraries of governmental institution through the case study of Information Center on North Korea(special library attached to the Ministry of Unification) and relationship with different connection systems attempted in e-government(Enterprise Architecture). Lastly, through this, considers complemental issues for developmental fixation in special libraries.

• Information Systems Review
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• v.10 no.3
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• pp.89-109
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• 2008

Korean card firms suffered harsh setbacks due to high credit defaults in 2002 and 2003, after issuing cards recklessly. Their key principle is changed to grow without damaging profitability and financial soundness. However, competition in the credit card market is heating up rapidly. Bank-affiliated card firms, having stronger sales networks and more capital than independent issuers, have increased their investments in card affiliates in a bid to develop new cash cows. Moreover, newly emerging independent card firms have waged fiercer campaigns to raise their credit card market share. In order to overcome these business conditions, S-card has settled on a strategy that focuses on stepping up marketing aimed at increasing charge card spending rather than credit card loans or cash lending services. Accordingly, S-card reformed the current business classification system of merchants, which was out-of-dated and originally built for the purpose of deciding merchant service fees only. They also drove customer segmentation planning to deliver the right customers to the right merchants. In this paper, we emphasize the problems of business classification systems of merchants with which most credit card firms have faced, and the need for reforming them not only to provide customer-tailored services but also to raise their business promotion excellence by reviewing S-card's process of customer segmentation.

• Journal of the Korea Safety Management & Science
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• v.20 no.1
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• pp.41-55
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• 2018

For several decades, attribute classification methods using the asymmetrical relationship between an attribute performance and the satisfaction of that attribute have been explored by numerous researchers. In particular, the Kano model, which classifies quality attributes into 5 elements using simple questionnaire and two-dimensional evaluation table, has gained popularity: Attractive, One-dimensional, Must-be, Indifferent, and Reverse quality. As Kano's model is well accepted, many literatures have introduced categorization methods using the Kano's evaluation table at attribute level. However, they applied different terminologies and classification criteria and this causes confusion and misunderstanding. Therefore, a criterion for quality classification at attribute level is necessary. This study is aimed to suggest a new attribute classification method that sub-categorizes quality attributes using 5-point ordinal point and Kano's two-dimensional evaluation table through an extensive literature review. For this, the current study examines the intrinsic and extrinsic problems of the well-recognized Kano model that have been used for measuring customer satisfaction of products and services. For empirical study, the author conducted a comparative study between the results of Kano's model and the proposed method for an e-learning case (33 attributes). Results show that the proposed method is better in terms of ease of use and understanding of kano's results and this result will contribute to the further development of the attractive quality theory that enables to understand both the customers explicit and implicit needs.