• Asia pacific journal of information systems
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• 제32권3호
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• pp.496-513
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• 2022

Artificial intelligence or big data technologies can benefit finance companies such as those in the insurance sector. With artificial intelligence, companies can develop better customer segmentation methods and eventually improve the quality of customer relationship management. However, the application of AI-based customer segmentation in the insurance industry seems to have been unsuccessful. Findings from our interviews with sales agents and customer service managers indicate that current customer segmentation in the Korean insurance company relies upon individual agents' heuristic decisions rather than a generalizable data-based method. We propose guidelines for AI-based customer segmentation for the insurance industry, based on the CRISP-DM standard data mining project framework. Our proposed guideline provides new insights for studies on AI-based technology implementation and has practical implications for companies that deploy algorithm-based customer relationship management systems.

• 응용통계연구
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• 제18권3호
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• pp.555-565
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• 2005

고객세분화는 기업이 관계하고 있는 고객을 이해하고 그 이해를 바탕으로 효과적인 고객관리를 수행하기 위해 필수적인 요소인데 데이터마이닝이 기업의 정보관리영역에 적극적으로 활용되면서 보다 과학적이고 최적화된 형태로 개발되고 있다. 본 연구에서는 신용카드고객 의 카드사용행태에 근거하여 각 고객을 서로 유사한 사용행태를 보이는 고객군으로 세분화하는 과정을 소개하였다. 고객이 실제로 신용카드를 사용하면서 발생시킨 거래정보에만 의존하여 고객세분화를 개발하였으며 이는 마케팅의 관점에서 상당히 의미있는 내용이 될 수 있다. 고객세분화의 개발을 위하여 데이터마이닝기법인 k-평균 군집방법과 최장연결법에 의한 계보적 군집방법을 단계적으로 활용하는 이단계 군집방법을 이용하였다.

• 대한산업공학회지
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• 제33권3호
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• pp.339-348
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• 2007

Since the 1980s, the marketing paradigm has rapidly changed from product-driven marketing to customer-driven marketing. Recently, due to an increase in the amount of information, customer-differentiation strategies have been emphasized more than product-differentiation strategies. This paper suggests a methodology for new customer segmentation and purchase forecasting using changes in customer behavior. This methodology includes a segmentation method for new customers using existing customer's characteristics and a purchase-forecasting system using the purchase-behavior patterns of existing customers. The proposed methodology not only provides differential services from a segmentation system but also recommends differential items from the purchase forecasting system for new and existing customers.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 춘계정기학술대회 e-Business를 위한 지능형 정보기술 / 한국지능정보시스템학회
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• pp.187-196
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• 2000

A lot of Internet shopping malls strive for obtaining a competitive advantage over others in an increasingly tighter electronic marketplace. To this end, understanding customer preference toward products (or services) and administering appropriate marketing strategy is essential for their continuous survival. However, only a few marketing researchers and practicioners focused on this issue, compared with academic and industry efforts devoted to traditional market segmentation. In this paper, we suggest a methodology of conjoint segmentation for electronic shopping malls. Traditional market segmentation methodologies based on customer's profile sometimes fail to utilize abundant information given while navigating around cyber shopping malls. In this methodology, we do not impose information overload to the customer for preference elicitation, but this methodology, we do not impose information overload to the customer for preference elicitation, but capture automatically generated surfing or buying data and analyze them to get useful market segmentation information. The methodology consists of 4-stages: 1) analyzing legacy homepages, 2) data preparation, 3) estimating and interpreting the result, and 4) developing marketing mix. Our methodology was to give useful guidelines for market segmentation to companies working in the electronic marketplace.

• Journal of Information Technology Applications and Management
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• 제10권1호
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• pp.19-34
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• 2003

Identifying customer repurchase intention is very Important for the Internet shopping mall to activate CRM (customer relationship management) in B2C (Business to Customer) eCommerce. In this paper, the experienced buyer's repurchase intention Is analyzed by using the approach of customer segmentation. Total of 979 samples, which had already experience of Internet shopping, are analyzed to demonstrate that the degree of repurchase Intentions differs from each segmented group. The benefit segmentation is performed by identifying private benefits for which consumers can seek among 14 services. The results show that the different group has a significant difference in the repurchase Intention. The results of repurchase intention can lead to practical recommendations for CRM in B2C eCommerce.

• Asia pacific journal of information systems
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• 제16권3호
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• pp.49-65
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• 2006

This study proposes a model for customer segmentation using the psychological characteristics of Internet banking customers. The model was developed through two phased clustering method, called SONN-HC by integrating self-organizing neural networks (SONN) and hierarchical clustering (HC) method. We applied the SONN-HC method to internet banking customer segmentation and performed an empirical analysis with 845 cases. The results of our empirical analysis show the psychological characteristics of Internet banking customers have significant differences among four clusters of the customers created by SONN-HC. From these results, we found that the psychological characteristics of Internet banking customers had an important role of planning a strategy for customer segmentation in a financial institution.

• 디지털산업정보학회논문지
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• 제19권4호
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• pp.1-11
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• 2023

Clustering analysis is used in various fields including customer segmentation and clustering methods such as k-means are actively applied in the credit card customer segmentation. In this paper, we summarized the input features selection method of k-means clustering for the case of the credit card customer segmentation problem, and evaluated its feasibility through the analysis results. By using the label values of k-means clustering results as target features of a decision tree classification, we composed a method for prioritizing input features using the information gain of the branch. It is not easy to determine effectiveness with the clustering effectiveness index, but in the case of the CH index, cluster effectiveness is improved evidently in the method presented in this paper compared to the case of randomly determining priorities. The suggested method can be used for effectiveness of actively used clustering analysis including k-means method.

• 한국컴퓨터정보학회논문지
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• 제11권2호
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• pp.303-317
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• 2006

CRM에서 고객세분화 단계는 기업 뿐 아니라 그 고객들의 기대 가치(expected value) 또는 이익을 동시에 고려해야 한다. 그러나 최근의 고객세분화에 관한 대부분의 연구들은 단지 '수익성'이라는 기업 관점만을 고려하고 있다. 본 연구에서는 고객세분화 단계의 문제점을 규명하고 보완된 관점을 제시하고자 한다. 저자들은 기업 및 고객들 양자에 공히 이익이 되며, 나아가 고객세분화 단계를 보다 균형적으로 수행할 수 있는 방안을 모색했다. 그 결과 고객 세분화 단계에서 기업 관점과 고객 관점의 기대가치를 동시에 고려할 수 있는 균형적 제안모형을 제시했으며, 이 모형을 사례연구에 적용해 보았다. 또한 균형적 모형을 통해 분류된 네 개의 고객군들에 대해 고객전략을 도출하였다. 이 전략은 금융산업에서 일반적으로 적용할 수 있는 유형이다. 이 같은 균형적 세분화는 한 기업의 우량고객들을 보다 정확하게 규명할 수 있도록 할 것이다. 이를 통해 고객들의 만족도를 향상시키고 고객 유지 기간을 연장할 수 있을 것으로 기대한다. 또한 고객들의 요구와 선호도에 대한 기업의 통찰력을 제고할 수 있으며, 타겟마케팅을 위한 자원 할당에서 효과성을 제고할 수 있을 것이다.

• Journal of Information Technology Applications and Management
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• 제22권1호
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• pp.77-93
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• 2015

As the range of the customer choice becomes more diverse, the average life span of companies' products and services is becoming shorter. Most companies are striving to maximize the revenue by understanding the customer's needs and providing customized products and services. However, companies had to bear a significant burden, in terms of the time and cost involved in the process of determining each individual customer's needs. Therefore, an alternative method is employed that involves grouping the customers into different categories based on certain criteria and establishing a marketing strategy tailored for each group. In this way, customer segmentation and customer clustering are performed using demographic information and behavioral information. Demographic information included sex, age, income level, and etc., while behavioral information was usually identified indirectly through customers' purchase history and search history. However, there is a limitation regarding companies' customer behavioral information, because the information is usually obtained through the limited data provided by a customer on a company's website. This is because the pattern indicated when a customer accesses a particular site might not be representative of the general tendency of that customer. Therefore, in this study, rather than the pattern indicated through a particular site, a customer's interest is identified using that customer's access record pertaining to external news. Hence, by utilizing this method, we proposed a methodology to perform customer segmentation. In addition, by extracting the main issues through a topic analysis covering approximately 3,000 Internet news articles, the actual experiment applying customer segmentation is performed and the applicability of the proposed methodology is analyzed.

• 한국산업정보학회논문지
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• 제15권5호
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• pp.79-87
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• 2010

본 논문에서는 현재 대학 한방병원에서 운영하고 있는 통합의료정보시스템의 외래환자 인구학적 정보와 진료기록 정보를 이용하여 고객세분화를 실시하고, 그 결과를 활용하여 외래환자 고객만족도 증진을 위한 고객관계관리 시스템 구축 모형을 제안하였다. 제안된 고객 관계관리 시스템 모형은 최선 정보기술과 인프라를 이용하기 보다는 현재 구축된 병원정보시스템의 부분적 수정을 통해 구축이 가능하므로 즉시 실현이 가능한 실용적인 모델이 될 수 있다. 또한 마케팅 전략에 따라 적절한 변수와 세분화 방법을 활용할 경우, 외래환자 고객만족 증진뿐만 아니라 다양한 형태의 마케팅 전략을 지원할 수 있는 고객관계관리 시스템 구축이 가능할 것이다.