• Journal of Information Technology Applications and Management
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• 제20권2호
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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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• 한국경영정보학회 2007년도 추계학술대회
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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.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2008년도 한국컴퓨터종합학술대회논문집 Vol.35 No.1 (C)
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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.

• 산경연구논집
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• 제13권10호
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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.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2008년도 춘계학술발표대회
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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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• 제38권3호
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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.

• 한국도서관정보학회지
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• 제37권3호
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• pp.193-208
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• 2006

고객만족도는 고객의 충성도 이탈율, 재구매율 신규고객창출 등과 밀접한 관련을 맺고 있어서 기업의 성과측 정의 평가관점에서 중요한 의미를 지닌다. 이처럼 중요한 의미를 갖는 고객만족도 측정은 민간부분에서 이미 일반화되었으며, 다양한 평가모형들이 존재한다. 그러나 기존의 일반적인 평가모형은 업종별 차별성이 없고, 새롭게 등장한 인터넷을 기반으로 한 공공도서관의 e-서비스 환경을 반영한 고객만족도 평가를 수행하기에는 한계가 있다. 본 연구는 기존의 도서관 서비스 관련 연구와 고객만족도 평가관련연구들을 고찰하고 이를 바탕으로 공공도서관 서비스 분류체계와 공공도서관 서비스에 특화된 고객만족도 평가체계를 제시한다.

• 정보관리학회지
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• 제25권3호
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• pp.119-141
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• 2008

본 연구는 먼저 민간의 고객관계관리(CRM)시스템을 정부차원에서 적용한 정책고객관계관리(PCRM) 시스템의 개념과 현황, PCRM과 CRM의 비교, 그리고 통일부의 도입사례를 통해 PCRM의 핵심인 고객의 정의와 분류 과정에 대해 살펴본다. 다음, 통일부 소속 전문도서관인 북한자료센터의 사례를 통해 다른 정부기관 전문도서관에서의 활용 방향과 전자정부에서 시도된 다른 연계 시스템들과의 관계에 대해 논구한다. 마지막으로 이를 통해 PCRM이 전문도서관에서 발전적으로 정착하기 위해서 보안해야 할 문제에 대해서 고찰한다.

• 경영정보학연구
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• 제10권3호
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• pp.89-109
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• 2008

후발카드사들의 시장 확대 전략, 은행계 카드사의 약진 등 점차 치열해지는 경쟁 구도에 대비하기 위해 S카드사는 과거와 같이 단순 신용카드 상품이나 '고수익 고위험'의 대출서비스에 주력하는 수익모델로는 향후 생존하기 어렵다는 현실을 인식하고 신용판매 활동의 내실 강화를 통해 지속적으로 수익을 창출할 수 있는 방안을 강구하였는데 이것이 바로 가맹점 업종분류체계 정비를 통한 고객세분화이다. 즉, 기존의 수수료율 책정기준으로 만들어진 가맹점 업종분류체계를 마케팅 목적으로 재편하고 새로운 업종분류체계에 맞춰 고객의 정확한 카드 사용실적을 파악한 후 고객을 세분화하는 개념으로, 가맹점과 고객의 다양한 니즈를 연계 관리함으로써 고객에게는 맞춤 정보 및 오퍼를 제공하고, 가맹점과의 긴밀한 협력관계를 통해 가맹점 매출을 증대하며, 이로 인해 자사의 신용판매를 확대하고 수익을 극대화하는 고객, 가맹점, 자사 상호간의 Win-Win-Win 관계 형성을 목표로 하였다. 본 연구에서는 S카드사가 어떠한 방식으로 기존의 업종분류체계를 정비하여 고객세분화를 수행하였으며, 어떻게 활용하고 있는가를 살펴봄으로써 효과적인 고객세분화에 기반한 마케팅 전략수립 의 방향을 제시하고자 한다.

• 대한안전경영과학회지
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• 제20권1호
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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.