• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.515-523
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• 2012

Most companies make efforts to maximize their profitability by improving loyalty to existing customers through customer relationship management (CRM). According to the Wikipedia, CRM is a widely implemented strategy for managing a company's interactions with customers, clients and sales prospects. And RFM is a method used for analyzing customer behavior and defining market segments. It is commonly used in database marketing and direct marketing and has received particular attention in retail. In general, one considers recency, frequency, and monetary for customer segmentation in RFM method. In this paper, we apply RFMP method added to the purchase period of advertising items in the traditional RFM model for real estate customer segmentation. We will be able to establish the differentiated marketing strategy by RFMP method.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.386-394
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• 2011

Recently it is difficult to draw an improvement for customer satisfaction because the ratio of satisfied customers increases in customer satisfaction survey. In addition, the effectiveness of practical application of customer satisfaction survey decreases due to its constitution limitation on its data analysis. In order to solve these problems, it is necessary to develop a novel research to identify the strategy meanings and find dissatisfied factors of satisfied customers using the satisfied customers' reclassification. This study focuses on the satisfied customer segmentation based on Latent Class Analysis (LCA). The case study with high-speed internet service customers show that the satisfied customers are divided into three subgroups using LCA and we draw meaning results such as satisfaction and dissatisfaction factors through analyzing each group. This study is expected to play the role as the groundwork for the revitalization of customer satisfaction survey as well as improving customer satisfaction management.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.21-36
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• 2022

Recently, as COVID-19 has made the e-commerce market expand widely, customers who have different consumption patterns appear in the market. Because companies can obtain opinions and information of customers from reviews, they increasingly face the requirements of managing customer reviews on online platform. In this study, we analyze customers and carry out market segmentation for classifying and defining type of customers in e-commerce. Specifically, K-means clustering was conducted on customer review data collected from Wemakeprice online shopping platform, which leads to the result that six clusters were derived. Finally, we define the characteristics of each cluster and propose a customer management plan. This paper is possible to be used as materials which identify types of customers and it can reduce the cost of customer management and make a profit for online platforms.

• Information Systems Review
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• v.7 no.2
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• pp.23-40
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• 2005

Discovering customers' behavioral patterns from large data set and providing them with corresponding services or products are critical components in managing a current business. However, the diversity of customer needs coupled with the limited resources suggests that companies should make more efforts on understanding and managing specific groups of customers, not the whole customers. The key issue of this paper is based on the fact that the behavioral patterns extracted from the specific groups of customers shall be different from those from the whole customers. This paper proposes the idea of pre-segmentation before developing customer classification models. We collected three customers' demographic and transactional data sets from a credit card, a tele-communication, and an insurance company in Korea, and then segmented customers by major variables. Different churn prediction models were developed from each segments and the whole data set, respectively, using the decision tree induction approach, and compared in terms of the hit ratio and the simplicity of generated rules.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10c
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• pp.154-159
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• 2006

본 논문은 웹 서버에 의해 자동으로 수집되는 로그 파일로부터 고객 가치 판단 기준을 고객의 행동 기반에 두고 군집화 기법을 이용하여 고객을 세분화하고 세분화 결과에 의사결정나무를 적용함으로써 고객을 분류하는 통합 모형을 제안하였다. 또한, 분류된 고객들의 주 서비스 활용 패턴을 분석하기 위하여 연관규칙기법을 적용하여 고객의 과학기술정보 활용의 연관성을 분석함으로써, 과학정보포털 서비스를 제공하는 사이트 이용자의 분류군에 해당하는 정보와 인터페이스를 제공하는 새로운 방법에 대하여 연구하였다. 고객 관리 측면에서 본 논문은 정보 서비스를 제공하는 웹 사이트의 기존고객을 분류하여 패턴을 분석함으로써 고객 위주의 사이트 운영정책과 동적 인터페이스를 제공하기 위한 웹사이트 활용 방안을 제시하였다. 또한, 고객의 지속적인 관리라 각 고객 분류군별에 안는 서비스를 제공하고 고객의 관리에도 기여할 수 있을 것이다.

• Proceedings of the Safety Management and Science Conference
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• 2001.11a
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• pp.79-83
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• 2001

무선인터넷을 이용한 고객관리는 고객에게 더욱더 세분화된 서비스를 제공할 수 있으며, 고급화된 서비스를 제공함으로써 고객의 만족과 구매욕구를 증진시킬 수 있다. 하지만, 개인화된 서비스를 제공하기 위해서는 고객에 대한 패턴 연구 및 세분화 작업이 먼저 이루어져야 한다 이러한 작업을 위한 다양한 연구중 한 분야가 웹 로그를 이용한 사용자는 패턴분석일 것이다. 본 연구에서는 웹 로그 분석을 통한 주요 컨텐츠를 추출하는 과정 및 예제시스템의 구현 방향에 대해서 알아보고자 한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.203-206
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• 2000

신경망을 이용해 고객집단을 분류하고 고객의 특성에 따라 세분화된 고객들에 대해 다단계 연관규칙을 적용해서 고객의 상품 구매패턴을 찾아 줌으로써 마케팅 전략 결정을 지원하는 구매패턴분류 시스템을 설계한다. 고객분류를 위한 신경망 시스템은 다층 퍼셉트론에 역전파 알고리즘을 이용한다. 주소, 구매금액, 구매횟수, 고객 구분, 상긴 등과 같은 고객정보를 입력층에 입력변수로 지정하고, 이에 따른 우량/일반고객을 출력변수로 지정한 후 신경망을 학습시키면, 실제의 우량/일반의 간과 예측되는 우량/일반의 값의 차이론 최소화시키면서 모형을 형성시켜 나가게 된다. 구매패턴 분류 시스템은 다단계 연관규칙을 이용한다. 고객분류 서브시스템을 통해 고객집단이 세분화되면 각각의 고객집단에 대해 TID와 품목 트랜잭션을 입력으로 cumulate 알고리즘과 개념계층을 이용해 일반화 과정을 수행하면서 빈발 항목을 찾게 되고 이론 근거로 항목간의 연관규칙을 찾아내게 된다.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.687-696
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• 2011

Customer Lifetime or Customer Lifetime Value is a essential metric of differentiated CRM marketing and differentiated marketing strategy as a company core competency. However, customer lifetime used in companies is easily obtained from a confined simple customer attrition rate at some specific time point regardless of customer characteristics. In this study, in order to overcome the constraints of previous simple methods and to make practical use of it in industries, we suggest a method that estimates a customer lifetime using a customer segment based survival analysis with the censored data of customers; in addition, we apply this method to A mobile telecom company data. A method using customer segment based survival analysis is suggested in this study 1) includes all customers having different subscription dates, 2) reduces individual error, 3) can reflect trends after the observed time point and is more realistic.

• 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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.261-281
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• 2001

IMF이후로 우리나라의 은행들은 현재 큰 구조조정을 맞이하고 있으며 이 속에서 살아남기 위하여 기존의 고객의 유형을 분석하고 이를 마케팅 전략에 활용하는 연구의 필요성이 높아지고 있다. 기존의 만은 연구들이 은행 고객들의 유형을 설문지 분석방법에 의존하여 몇 개의 군집으로 분류하고 이들의 집단 및 특성을 연구하였다 하지만 설문데이터의 경우 고객들의 실제적인 행동이 반영되지 못하는 한계점을 가지고 있다. 이에 본 연구에서는 C은행의 실제 고객 자료를 통하여 다양한 데이터마이닝 기법을 적용하여 고객을 세분화한 다음 고객이 가계성예금을 해지하고 다른 은행으로 이탈하는 집단의 특성을 분류하고 규칙을 도출하였다. 또한 이들을 관리하는 전략을 제시하였다.