• 경영과학
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• 제23권3호
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• pp.183-194
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• 2006

In this paper, we propose a customer profiling technique based on customer behavior for personalized products recommendation in Internet shopping mall. The proposed technique defines customer profile model based on customer behavior Information such as click data, buying data, market basket data, and interest categories. We also implement CBCPT(customer behavior based customer profiling technique) and perform extensive experiments. The experimental results show that CBCPT has higher MAE, precision, recall, and F1 than the existing other customer profiling technique.

• International Journal of Advanced Culture Technology
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• 제1권2호
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• pp.13-17
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• 2013

Today, most of the companies have numerous issues to take advantage of the data within the organization. Modeling techniques could be described using profile and historical log data as a tool of data mining techniques. It is covered increasingly with data entry, research, processing, modeling and reporting components of the icon in the form of easy-to-use in many datamining tools. Visual data mining process can create a data stream. In this paper, customer behavior is predicted in pages or products, using the history profile analysis and the navigation items are necessary to predict unknown features.

• Industrial Engineering and Management Systems
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• 제6권1호
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• pp.28-39
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• 2007

The issue of customer-centric enterprise focusing on realizing customer needs has recently received considerable attention in the corporate world. However, little research has yet been reported on developing Information Systems (IS) supporting the customer-centric enterprises. This research proposes an integrated approach of IS development that supports organizations aiming to become customer-centric enterprises using various customer profiles. In this paper, we propose an integrated approach unifying goal modeling, business process modeling, and information systems modeling. The approach is expected to be seamlessly linked with the object-oriented systems development approach. Finally, we apply this approach to the real case of a securities company in Japan.

• 산업공학
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• 제19권1호
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• pp.86-96
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• 2006

Some customer data of manufacturing industries have a large number of incomplete data set due to the customer's infrequent purchasing behavior and the limitation of customer profile data gathered from sales representatives. So that, most sophisticated data analysis methods may not be applied directly. This paper proposes a heuristic data analysis method to classify customers in household appliances industries. The proposed PD (percent of difference) method can be used for the discriminant analysis of incomplete customer data with simple mathematical calculations. The method is composed of variable distribution estimation step, PD measure and cluster score evaluation steps, variable impact construction step, and segment assignment step. A real example is also presented.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.1068-1071
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• 1996

Good shape on flat rolled product is necessary to meet today's customer quality requirement. To meet the increasing demand in quality of strip shape from downstream customers, POSCO has replaced the Automatic Shape Control(ASC) system with the existing one that had used noncontact type measuring system at No.2 Cold Rolling Mill, Pohang works in October, 1995. The strip shape is influenced by the profile, roll crown, bending control, skew control system, as well as work roll cooling system. We have used ASC to adjust those factors in Cold Rolling Mill that could get a satisfactory result, almost less than .+-.5 1-unit deviation from the target shape. However, the downstream customer(i.e. Continuos Annealing Line) wants a good shape not only at the moment of exit of roll bite, but after rolling without tension. In this investigation, the difference will be discussed and how deal with this problem.

• Journal of Electrical Engineering and Technology
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• 제7권3호
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• pp.281-287
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• 2012

The customer load profile clustering method is used to make the TDLP (Typical Daily Load Profile) to estimate the quarter hourly load profile of non-AMR (Automatic Meter Reading) customers. This study examines how the repeated clustering method improves the ability to discriminate among the TDLPs of each cluster. The k-means algorithm is a well-known clustering technology in data mining. Repeated clustering groups the cluster into sub-clusters with the k-means algorithm and chooses the sub-cluster that has the maximum average error and repeats clustering until the final cluster count is satisfied.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.141-148
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• 2003

Customer retention is a common concern for many industries and a critical issue for the survival in today's greatly compressed marketplace. Current customer retention models only focus on detection of potential defectors based on the likelihood of defection by using demographic and customer profile information. In this paper, we propose a dynamic procedure for defection detection and prevention using past and current customer behavior by utilizing SOM and Markov chain. The basic idea originates from the observation that a customer has a tendency to change his behavior (i.e. trim-out his usage volumes) before his eventual withdrawal. This gradual pulling out process offers the company the opportunity to detect the defection signals. With this approach, we have two significant benefits compared with existing defection detection studies. First, our procedure can predict when the potential defectors could withdraw and this feature helps to give marketing managers ample lead-time for preparing defection prevention plans. The second benefit is that our approach can provide a procedure for not only defection detection but also defection prevention, which could suggest the desirable behavior state for the next period so as to lower the likelihood of defection. We applied our dynamic procedure for defection detection and prevention to the online gaming industry. Our suggested procedure could predict potential defectors without deterioration of prediction accuracy compared to that of the MLP neural network and DT.

• 정보처리학회논문지D
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• 제11D권5호
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• pp.1205-1212
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• 2004

전자상거래 시스템의 보급이 활성화되기 시작하면서, 사용자의 필요와 욕구에 밀착한 적응형 전자상거래 에이전트의 필요성이 증대되고 있다. 이와 같은 적응형 전자상거래 에이전트는 사용자의 행위를 모니터하고 자동 분류하여 사용자의 취향을 학습하는 기능을 요하게 되었다. 이러한 기능을 가지는 적응형 전자상거래 에이전트를 구축하기 위해서, 본 논문에서는 사용자 개인의 관심정보와 선호하는 상품에 대한 호감도를 고려한 적응형 전자 상거래 에이전트 시스템을 제안한다. 제안하는 시스템은 사용자의 구매 행위에 적응력을 가질 수 있도록 보다 정확한 사용자 프로파일을 구축하고, 이와 같은 사용자 프로파일을 기반으로 사용자에게 불필요한 검색과정 없이 필요한 상품 정보를 제공 할 수 있도록 한다. 본 시스템에서는 모니터링을 통하여 사용자 의도를 파악하는 모니터 에이전트, 사용자의 행동성향을 학습 한 후 행동 패턴이 유사한 그룹을 참조하는 유사도 참조 에이전트, 사용자의 행위의 변화에 따른 개인화된 행동 DB를 구축할 수 있는 관심 추출 에이전트로 구성하였다.

• 패션비즈니스
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• 제6권5호
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• pp.136-150
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• 2002

In this study, we proposed a method to be standing customers as the supporting system for the improvement of fashion garment industry which was the marginal growth getting into full maturity of market. As for the customer creation method of Fashion garment company is developing a marketing program to be standing customer as customer scoring to estimate a existing customer‘s buying power, and figure out minimum fixed sales of company to use a future purchasing predict. This study was a result of data from total sixty thousands data to be created for the 11 months from september. 2000 to July. 2001. The data is part of which the company leading the Korean fashion garment industry has a lot of a customer purchasing history data. But this study used only 48,845 refined purchased data to discriminate from sixty thousands data and 21,496 customer case with the exception of overlapping purchased data among of those. The software used to handle sixty thousands data was SAS e-miner. As the analysis process is put in to operation the analysis of the purchasing customer’s profile firstly, and the second come into basket analysis to consider the buying associations for Association goods, the third estimate the customer grade of Customer loyalty by 3 ways of logit regression analysis, decision tree, Artificial Neural Network. The result suggested a method to be estimate the customer loyalty as 3 independent variables, 2 coefficients. The 3 independent variables are total purchasing amount, purchasing items per one purchase, payment amount by one purchasing item. The 2 coefficients are royal and normal for customer segmentation. The result was that this model use a logit regression analysis was valid as the method to be estimate the customer loyalty.

• 한국신뢰성학회:학술대회논문집
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• 한국신뢰성학회 2011년도 춘계학술발표대회 논문집
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• pp.21-26
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• 2011

Initial reliability prediction done by calculation would be more practical if support by evidence from customer usage profile and field failure data to improve the prediction. Thus, the consistency of the design and the product would be practically validated. In this paper, it will address rationale and method to decide on Acceleration Factor (AF) to be used in Accelerated Life Test (ALT) through usage profile and field failure. The case study of tractor transmission is used to demonstrate the method which data obtained from surveys done on farmers, field visits and field failure data from service center. By considering all the elements, it will determine more relevant AF which indicates the real use conditions of the component.