• Title/Summary/Keyword: 고객데이터

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A Management Improvement Study by the Use Survey of an Academic Library - Focused on the Analysis of Circulation Records of the C-Academic Library Users - (대학도서관 이용조사를 통한 경영개선 연구 - C 대학도서관 이용자의 대출기록 분석을 중심으로-)

  • Yoo, Kyeong-Jong;Park, Il-Jong
    • Journal of the Korean Society for information Management
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    • v.24 no.3
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    • pp.93-117
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    • 2007
  • The books and circulation-related data in the Library Automation System(LAS) of C-academic library were collected and analyzed, and also the method which may be applied to the Customer Relationship Management (CRM) based on the results was suggested in this paper. Collected data were 269,387 bibliographic data of books, 12,281 patron data, and 39,269 circulation records. User identity, circulation frequencies, total number of circulated books, and publication year as relation factor from the analyzed data of circulation records were extracted. They were also analyzed, and verified by correlation coefficient.

User's SNS Data-Based Scoring Scheme For Personalized Cosmetics Recommendation (개인 맞춤형 화장품 추천을 위한 사용자 SNS 정보 기반의 스코어링 기법)

  • Ha, Eunji;Moon, Jihoon;Hwang, Eenjun
    • Proceedings of the Korea Information Processing Society Conference
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    • 2016.10a
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    • pp.386-389
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    • 2016
  • 최근 남녀노소를 불문하고 피부 관리에 대한 관심이 증가하면서, 피부 개선에 효과적인 화장품의 선택에 관심이 높아지고 있다. 하지만 다양한 화장품들을 대상으로 자동화된 고객 맞춤형 화장품 추천은 그 발전이 더디고, 이와 관련된 연구 또한 아직 미미한 실정이다. 또한, 다양한 특성을 가지는 고객 피부 데이터 셋의 확보가 어려운 상황에서, 소수의 데이터 표본만을 이용하여 화장품 추천이 진행되고 있어 추천의 정확도를 확보하지 못하고 있다. 본 논문은 스마트폰용 휴대용 카메라를 이용하여 고객의 피부 상태를 진단한 후, 고객의 피부 개선에 적합한 화장품을 자동으로 추천하는 기법을 제안한다. 먼저, 화장품 추천을 위해 사용자의 SNS 데이터와 피부 데이터를 수집 및 분석하여 추천 리스트를 생성한다. 이를 기반으로, 추천된 각 화장품의 스코어를 계산한다. 그 다음, 피부 개선 순위와 스코어 기반의 화장품 특성 순위 간의 상관계수를 이용하여 가장 높은 상관계수의 화장품을 우선 추천한다. 성능 평가를 위해 실제 화장품 회사에서 제시한 화장품 추천 리스트와 본 논문에서 제안한 방법을 적용한 화장품 추천 리스트를 비교함으로써 효용성과 타당성을 입증하였다.

Effective eCRM using prediction function of Data Mining (Data Mining의 예측기능을 이용한 효과적인 eCRM)

  • Kang Rae-Goo;Kim Seung-Eon;Jung Chai-Yeoung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2006.05a
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    • pp.1039-1042
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    • 2006
  • Because many corporations computerize process figure enemy who is introducing eCRM fast and are used mainly at past by purpose to detect and analyze and forecast systematic analysis of customer information and various pattern of customer recently, ordinary peoples are trend that is alternated gradually by data mining that can drawand forecast result of good quality easily. Field that this data mining is used representatively is eCRM. In this treatise customer data of A discount store and sale data of 1 years experimenting that forecast customer contribution to base next year through data mining actuality data and data mining through comparison with predicted data are how effective to eCRM prove.

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Design and Implementation of Membership Website for the Activation of CRM: A Case Study on the 'c' Newspaper (CRM 활성화를 위한 고객 멤버십 사이트 설계 및 구현 : C신문사 사례 중심으로)

  • Yoon, Won-Jin
    • Proceedings of the Korean Information Science Society Conference
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    • 2010.06b
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    • pp.431-435
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    • 2010
  • CRM (Customer Relationship Management)은 효율적 고객 관리를 통해 기업의 이익과 경쟁우위를 향상사킬 수 있는 비즈니스 전략 모델이다. 신문사도 일반 기업과 마찬가지로 신규 고객 유치와 기존 고객 유지에 다각도의 노력을 기울이고 있다. 국내 C신문사의 경우 CRM이 구축되어 고객정보의 획득과 관리가 용이해졌어도 지국이 입력한 고객정보가 온라인 마케팅을 하기엔 부적합하고, 인터넷을 통한 구독관련 셀프서비스 및 독자우대 콘텐츠를 제공하는 채널이 없었기에 그만큼 고객관리의 효용성이 떨어졌었다. 이를 해결하기 위해 고객과 직접 커뮤니케이션하면서 타사와 차별화된 혜택을 제공할 수 있는 고객 멤버십사이트를 설계 구현하였다. 이를 통해 고객 데이터의 정제는 물론 온라인 마케팅이 활발히 진행되고 고객 이탈의 억제 효과까지 나타냈다. 이는 고객관리를 활성화할 수 있는 효과적 방법으로 이용될 수 있을 것이다.

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UX Methodology Study by Data Analysis Focusing on deriving persona through customer segment classification (데이터 분석을 통한 UX 방법론 연구 고객 세그먼트 분류를 통한 페르소나 도출을 중심으로)

  • Lee, Seul-Yi;Park, Do-Hyung
    • Journal of Intelligence and Information Systems
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    • v.27 no.1
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    • pp.151-176
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    • 2021
  • As the information technology industry develops, various kinds of data are being created, and it is now essential to process them and use them in the industry. Analyzing and utilizing various digital data collected online and offline is a necessary process to provide an appropriate experience for customers in the industry. In order to create new businesses, products, and services, it is essential to use customer data collected in various ways to deeply understand potential customers' needs and analyze behavior patterns to capture hidden signals of desire. However, it is true that research using data analysis and UX methodology, which should be conducted in parallel for effective service development, is being conducted separately and that there is a lack of examples of use in the industry. In thiswork, we construct a single process by applying data analysis methods and UX methodologies. This study is important in that it is highly likely to be used because it applies methodologies that are actively used in practice. We conducted a survey on the topic to identify and cluster the associations between factors to establish customer classification and target customers. The research methods are as follows. First, we first conduct a factor, regression analysis to determine the association between factors in the happiness data survey. Groups are grouped according to the survey results and identify the relationship between 34 questions of psychological stability, family life, relational satisfaction, health, economic satisfaction, work satisfaction, daily life satisfaction, and residential environment satisfaction. Second, we classify clusters based on factors affecting happiness and extract the optimal number of clusters. Based on the results, we cross-analyzed the characteristics of each cluster. Third, forservice definition, analysis was conducted by correlating with keywords related to happiness. We leverage keyword analysis of the thumb trend to derive ideas based on the interest and associations of the keyword. We also collected approximately 11,000 news articles based on the top three keywords that are highly related to happiness, then derived issues between keywords through text mining analysis in SAS, and utilized them in defining services after ideas were conceived. Fourth, based on the characteristics identified through data analysis, we selected segmentation and targetingappropriate for service discovery. To this end, the characteristics of the factors were grouped and selected into four groups, and the profile was drawn up and the main target customers were selected. Fifth, based on the characteristics of the main target customers, interviewers were selected and the In-depthinterviews were conducted to discover the causes of happiness, causes of unhappiness, and needs for services. Sixth, we derive customer behavior patterns based on segment results and detailed interviews, and specify the objectives associated with the characteristics. Seventh, a typical persona using qualitative surveys and a persona using data were produced to analyze each characteristic and pros and cons by comparing the two personas. Existing market segmentation classifies customers based on purchasing factors, and UX methodology measures users' behavior variables to establish criteria and redefine users' classification. Utilizing these segment classification methods, applying the process of producinguser classification and persona in UX methodology will be able to utilize them as more accurate customer classification schemes. The significance of this study is summarized in two ways: First, the idea of using data to create a variety of services was linked to the UX methodology used to plan IT services by applying it in the hot topic era. Second, we further enhance user classification by applying segment analysis methods that are not currently used well in UX methodologies. To provide a consistent experience in creating a single service, from large to small, it is necessary to define customers with common goals. To this end, it is necessary to derive persona and persuade various stakeholders. Under these circumstances, designing a consistent experience from beginning to end, through fast and concrete user descriptions, would be a very effective way to produce a successful service.

Design of a Product Recommender based on Web Log Analysis (웹 로그 분석에 기반한 상품 추천기의 설계)

  • 김건량;이도헌
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2000.10a
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    • pp.349-352
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    • 2000
  • As a lot of people have used electronic commerce, many shopping malls have appeared on the Interne and the shopping information in them has been enormous. So, the need for a system to recommend product to customers is on the increase so as to reduce time and efforts for shopping. In this paper, we suppose a Product Recommender System which is constructed by applying data mining techniques to web for files and analyzing customer's action pattern, customer's profile and product purchase data. This system offers convenience that customers can get their desired information easily, by sending e-mail or mail and recommending web pages when they visit a shopping mall.

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Power Consumption Patterns Analysis Using Expectation-Maximization Clustering Algorithm and Emerging Pattern Mining (기대치-최대화 군집 알고리즘과 출현 패턴 마이닝을 이용한 전력 소비 패턴 분석)

  • Jin Hyoung Park;Heon Gyu Lee;Jin-Ho Shin;Keun Ho Ryu;Hiseok Kim
    • Proceedings of the Korea Information Processing Society Conference
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    • 2008.11a
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    • pp.261-264
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    • 2008
  • 전력 회사의 효율적인 운용과 전력 시장에서의 경쟁을 위하여 고객의 전력 소비 패턴 분석 및 정확한 예측이 이루어져야 한다. 이를 위해서 이 논문에서는 원격 검침 시스템에 의한 전국의 고압 고객 데이터를 대상으로 고객의 전력 소비 패턴을 정확히 예측할 수 있는 마이닝 기법을 제안하였다. 먼저, 국내 계약종별 고객 특성에 맞는 부하 패턴의 정확한 구별을 위한 9가지의 특징 벡터를 추출하였고, 기대치-최대화 군집화 알고리즘을 사용하여 고객의 34개 대표 부하프로파일을 생성하였다. 마지막으로 추출된 특징 벡터로부터 각 대표 프로파일에 대한 출현 패턴 기반의 분류 모델을 구성하여 고객의 전력 소비 패턴을 분류하였다. 국내 원격 검침 시스템에 의해 측정된 총 3,895명의 고압 고객 데이터에 대한 실험 결과 약 91%의 분류 정확성을 보였다.

Buying Customer Classification in Automotive Corporation with Decision Tree (의사결정트리를 통한 자동차산업의 구매패턴분류)

  • Lee, Byoung-Yup;Park, Yong-Hoon;Yoo, Jae-Soo
    • The Journal of the Korea Contents Association
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    • v.10 no.2
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    • pp.372-380
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    • 2010
  • Generally, data mining is the process of analyzing data from different perspectives and summarizing it into useful information that can be used to increase revenue, cuts costs, or both. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Data mining is one of the fastest growing field in the computer industry. Because of According to computer technology has been improving, Massive customer data has stored in database. Using this massive data, decision maker can extract the useful information to make a valuable plan with data mining. Data mining offers service providers great opportunities to get closer to customer. Data mining doesn't always require the latest technology, but it does require a magic eye that looks beyond the obvious to find and use the hidden knowledge to drive marketing strategies. Automotive market face an explosion of data arising from customer but a rate of increasing customer is getting lower. therefore, we need to determine which customer are profitable clients whom you wish to hold. This paper builds model of customer loyalty detection and analyzes customer buying patterns in automotive market with data mining using decision tree as a quinlan C4.5 and basic statics methods.

Churn Management Minging Model based on Integrated Customer ID (고객ID 통합구조에 기반한 고객이탈방지 마이닝 모델)

  • 김혜정;임정연;성진동
    • Proceedings of the Korean Information Science Society Conference
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    • 2002.10e
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    • pp.58-60
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    • 2002
  • CRM이 기업의 핵심 경영전략으로 도입되면서 기업이 보유하고 있는 고객데이터를수집, 통합, 가공, 분석하여 마케팅을 위해 활용하고자 하는 시도가 계속되고 있다. 특히, 기존고객의 유지 전략과 기존고객을 활용한 신상품 유도 전략이 중요한 이슈로 대두되면서 마이닝을 통한 CRM관점의 고객이탈방지는 각 통신사에서 지속적으로 추진하고 있는 분야이다. 본 연구에서는 KT의 고객이탈방지 모텔 구축을 사례로 효율적인 마이닝 모델 구축을 위한 고객통합구조를 제안하고자 한다. 그러고, 고객이탈방지 모델 구축의 전처리 과정으로 고객통합구조를 적용하여 고객중심의 변수 도출, 이용행태 추적 등을 통해 의미 있는 해지변수를 찾아내는 방법과 그 효과에 대해 기술한다.

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Model of Customer Classification Target Marketing in Automotive Corporation (자동차산업의 고객분류 및 타겟 마케팅 모델)

  • Lee, Byoung-Yup;Park, Yong-Hoon;Yoo, Jae-Soo
    • The Journal of the Korea Contents Association
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    • v.9 no.4
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    • pp.313-322
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    • 2009
  • Recently, According to computer technology has been improving, Massive customer data has stored in database. Using this massive data, decision maker can extract the useful information to make a valuable plan with data mining. Data mining offers service providers great opportunities to get closer to customer. Data mining doesn't always require the latest technology, but it does require a magic eye that looks beyond the obvious to find and use the hidden knowledge to drive marketing strategies Automotive market face an explosion of data arising from customer but a rate of increasing customer is getting lower. therefore, we need to determine which customer are profitable clients whom you wish to hold. This paper builds model of customer loyalty detection and analyzes customer patterns in automotive market with data mining using association rule and basic statics methods. With 4he help of information technology.