• 대한산업공학회지
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• 제28권3호
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• pp.274-282
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• 2002

This paper discusses a multi-dimensional analysis for Customer Relationship Management (CRM). For this, We propose a decision-making methodology which employs three analysis models. The first model is a three-dimension positioning map to derive a strategy which achieves the Process Value Line (PVL). The second model is the web-shape analysis model to visibly understand the individual based on the customer CSI (Customer Satisfactory Index) data. The third model which supports the web-shape analysis model, is the relative satisfactory analysis model. It considers a satisfaction level after purchasing against before purchasing. Then we perform overall analysis based on the three analysis models to provide marketing strategies to decision makers.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2004년도 추계학술대회
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• pp.316-319
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• 2004

Due to keen competition among companies, companies have segmented customers and they are trying to offer specially targeted customer by means of the distinguished method. In accordance, data mining techniques are noted as the effective method that extracts useful information. This paper explores customer segmentation of the home study company using data mining. The purposes of this paper are especially competitor chum in the recent home study market, to understand the characteristics of the customer group who are expected chum in case competing companies do aggressive sales promotion. In addition, this paper aims to find the influential factors of their breakaway, and to prepare practical marketing strategy to keep the existing customers. The study of chum in the home study market is conducted and the model using decision tree to predict and select valuable customer. Finally, this paper presents how the results can be incorporated and measured as a part of an overall marketing campaign process.

• 한국콘텐츠학회논문지
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• 제16권4호
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• pp.195-203
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• 2016

본 연구에서는 커피 전문점 고객들의 구매 의사 결정 메커니즘에 대해 살펴보고자 한다. 고객들의 재구매 의도를 형성하는 주요 선행 요인으로 고객 만족, 인지된 가치, 브랜드 이미지를 고려하였다. 인지된 가치는 다차원 접근법을 바탕으로 실용적 가치, 유희적 가치, 사회적 가치로 구분하여, 각 가치 요인들이 고객들의 구매 의사 결정에 미치는 영향을 살펴보았다. 제안한 연구 모형은 커피 전문점을 자주 방문한 232명의 대학생들을 대상으로 분석하였으며, LISREL을 이용하여 구조방정식 모형을 검증하였다. 연구 분석 결과, 제안한 연구 모형은 재구매 의도 분산의 67%, 고객 만족 분산의 73%를 설명하였다. 고객 만족과 브랜드 이미지는 재구매 의도 형성에 핵심적인 역할을 담당하였다. 그리고 실용적 가치와 유희적 가치는 재구매 의도에 유의한 영향을 미쳤지만, 사회적 가치는 재구매 의도에 부정적인 영향을 미쳤다.

• 보건행정학회지
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• 제13권3호
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• pp.21-34
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• 2003

Patient revisit to used hospital is a key factor in determining a health care organization's competitive advantage and survival. This article examines the relationship between customer's satisfaction and his/her revisit associated with three different methods which are the Chi Square Automatic Interaction Detection(CHAID) for segmenting the outpatient group, logistic regression and neural networks for addressing the outpatient's revisit. The main findings indicate that the important factors on outpatient's revisit are physician's kindness, nurse's skill, overall level of satisfaction, hospital reputation, recommendation, level of diagnoses and outpatient's age. Among these ones, physician's kindness is the most important factor as guidelines for decision of their revisit. The decision maker of hospital should select the strategy containing the variable amount of the level of revisit and size of outpatient's group under the constraint on the hospital's time, budget and manpower given. Finally, this study shows that neural networks, as non-parametric technique, appear to more correctly predict revisit than does logistic regression as a parametric estimation technique.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 추계학술대회-지능형 정보기술과 미래조직 Information Technology and Future Organization
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• pp.347-354
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• 1999

The credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this paper, we present a new approach to credit rating of customers based on the rough set theory. The concept of a rough set appeared to be an effective tool for the analysis of customer information systems representing knowledge gained by experience. The customer information system describes a set of customers by a set of multi-valued attributes, called condition attributes. The customers are classified into groups of risk subject to an expert's opinion, called decision attribute. A natural problem of knowledge analysis consists then in discovering relationships, in terms of decision rules, between description of customers by condition attributes and particular decisions. The rough set approach enables one to discover minimal subsets of condition attributes ensuring an acceptable quality of classification of the customers analyzed and to derive decision rules from the customer information system which can be used to support decisions about rating new customers. Using the rough set approach one analyses only facts hidden in data, it does not need any additional information about data and does not correct inconsistencies manifested in data; instead, rules produced are categorized into certain and possible. A real problem of the evaluation of the evaluation of credit rating by a department store is studied using the rough set approach.

• 품질경영학회지
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• 제48권3호
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• pp.421-431
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• 2020

Purpose: The purpose of this study is to compare and analyze the tokenizer in natural language processing for customer satisfaction in sentiment analysis. Methods: In this study, a supervised learning-based tokenizer Mecab-Ko and an unsupervised learning-based tokenizer SentencePiece were used for comparison. Three algorithms: Naïve Bayes, k-Nearest Neighbor, and Decision Tree were selected to compare the performance of each tokenizer. For performance comparison, three metrics: accuracy, precision, and recall were used in the study. Results: The results of this study are as follows; Through performance evaluation and verification, it was confirmed that SentencePiece shows better classification performance than Mecab-Ko. In order to confirm the robustness of the derived results, independent t-tests were conducted on the evaluation results for the two types of the tokenizer. As a result of the study, it was confirmed that the classification performance of the SentencePiece tokenizer was high in the k-Nearest Neighbor and Decision Tree algorithms. In addition, the Decision Tree showed slightly higher accuracy among the three classification algorithms. Conclusion: The SentencePiece tokenizer can be used to classify and interpret customer sentiment based on online reviews in Korean more accurately. In addition, it seems that it is possible to give a specific meaning to a short word or a jargon, which is often used by users when evaluating products but is not defined in advance.

• 유통과학연구
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• 제13권4호
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• pp.9-19
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• 2015

Purpose - This study focuses on the use of evaluative criteria software for imprecise market information, and product mapping relationships between design parameters and customer requirements. Research design, data, and methodology - This study involved using the product predicted value method, synthesizing design alternatives through a morphological analysis and plan, realizing the synthesis in multi-criteria decision-making (MCDM), and using its searching software capacity to obtain optimal solutions. Results - The establishment of product designs conforms to the customer demand, and promotes the optimization of several designs. In this study, the construction level analytic method and the simple multi attribute comment, or the quantity analytic method are used. Conclusions - This study provides a solution for enterprise products' multi-goals decision-making, because the product design lacks determinism, complexity, risk, conflict, and so on. In addition, the changeable factor renders the entire decision-making process more difficult. It uses Fuzzy deduction and the correlation technology for appraising the feasible method and multi-goals decision-making, to solve situations of the products' multi-goals and limited resources, and assigns resources for the best product design.

• 한국경영과학회지
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• 제29권3호
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• pp.63-78
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• 2004

This paper deals with a supplier-managed inventory(SMI) control for a two-echelon supply chain model with a service facility and a single supplier. The service facility is allocated to customers and provides a service using items of inventory that are purchased from the supplier, Assuming that the supplier knows the information of customer queue length as well as inventory position in the service facility at the time when it makes a replenishment decision, we identify an optimal replenishment policy which minimizes the total supply chain costs by reflecting these information into the replenishment decision. Numerical analysis demonstrates that the SMI strategy can be more cost-effective when the information of both customer queue length and inventory position is shared than when the information of inventory position only is shared.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2002년도 춘계공동학술대회
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• pp.734-741
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• 2002

The importance of efficient Supply Chain Management is increasing in accordance with recent industrial environment, such as globalization of business, complexity and diversity of company's management structure, and variety of customer's demand. In a rapidly changed environment of business, quick and efficient decision making is the important matter to the survival of the company. The purpose of this study supports decision making for efficient supply chain management. In this study, we consider simultaneously and mutually reflecting the preference of each constituent (Supplier, Manufacturing plant, Distribution center, Customer) on supply chain network, and decide company's strategic choice and coordinated production/distribution models of company. The Analytical Hierarchy Process is used for decision making of qualitative and quantitative elements. We use the results of AHP as inputs and propose mathematical models thru Mixed Integer Programming.

• 산업공학
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• 제25권2호
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• pp.178-186
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• 2012

The DRAM module is an important part of servers, workstations and personal computer. Its malfunction causes a lot of damage on customer system. Therefore, customers demand the highest quality products. The company applies DRAM module Outgoing Quality Assurance Inspection(OQA) to secures the highest quality. It is the key process to decides shipment of products through sample inspection method with customer oriented tests. High fraction of defectives entering to OQA causes inevitable high quality cost. This article proposes the application of ensemble learning to classify the lot status to minimize the ratio of wrong decision in OQA, observing a potential in reducing the wrong decision.