• Journal of the Korea Computer Industry Society
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• v.2 no.9
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• pp.1175-1182
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• 2001

One to one Marketing (a.k.a. database marketing or relationship marketing) is one of the many fields that will benefit from the electronic revolution and shifts in consumer sales and advertising. As a component of intelligent customer services on Internet storefront, this paper describes technology of providing personalized advertisement using the market basket analysis, a well-Known data mining technique. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customer's data. However, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed Knowledge base. In this paper, using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store.

• Proceedings of the Korea Contents Association Conference
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• 2010.05a
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• pp.243-246
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• 2010

In this research, the object is to analysis the impact of the four dimensions of Marketing Mix, product, price, place and promotion on store attitude. At the same time as mediating factors of relationship between Marketing Mix and customer attitude, experience factors, such as sense experience, feel experience and think experience are also analyzed. The results show that the promotion has a significant effect on sense experience, and place also has a significant effect on sense experience, feel experience and think experience. Another important result is, experience factors, such as sense experience, feel experience and think experience have significant effect on store attitude.

• Journal of Korean Ophthalmic Optics Society
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• v.7 no.1
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• pp.79-83
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• 2002

The purpose of this study was analyze CRM(customer relationship management) and marketing promotion mix, which is widely developed by many opticians at the present time, to establish the methods for more effective application of CRM.

• Asia Marketing Journal
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• v.2 no.1
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• pp.83-98
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• 2000

본 논문은 국내소비자들을 대상으로 상표애호도를 측정하고 고객의 애호도 수준과 가격판촉에 대한 반응간의 관계를 분석하려는 탐색적 연구의 하나이다. 이를 위해 국내시장에서 수집된 비내구소비재에 대한 패널자료 중 탄산음료와 씨리얼제품을 대상으로 분석을 실시하였다. 실증분석 결과 각 제품에 높은 애호도를 보이는 고객의 인구통계적 특성은 다소 차이가 있는 것으로 밝혀졌다. 상표애호도와 가격판촉간의 관계에 있어서 상표애호도가 높은 고객집단이 낮은 집단에 비해 선호하는 상표에 대해 높은 가격을 지불하며 가격판촉에 덜 민감하게 반응하는 것으로 나타났다.

• Journal of Global Scholars of Marketing Science
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• v.6
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• pp.127-142
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• 2000

오늘 날 외식업체는 고객욕구의 파악에 있어 고객만족에 영향을 미치는 요인에 대한 보다 세심한 배려를 기울이기 보다는 서비스기업이 가지는 모방성의 특성에 따라 표면적인 측면을 기준으로 신제품 개발과 서비스제공으로 경쟁력을 갖추려 하고 있다. 따라서 인적자원에 의한 의존도가 매우 높은 외식업체의 특성상 서비스 제공자의 의도 와는 달리 고객의 입장에서 불만족의 요인은 언제나 발생할 수 있는 가능성에 노출된 상 태에서 그러한 상황에 대처노력이 고객만족에 어떤 영향을 미칠 것인가에 대한 연구는 향후 외식업체의 경쟁력 강화에 많은 시사점을 제시할 수 있을 것으로 보인다. 본 연구의 목적은 앞에서 제기한 문제의식을 바탕으로 외식업체의 서비스품질과 고객 만족의 관계에서 상황대처요인의 역할을 규명하는데 있다. 본 연구를 통해 얻은 결론은 일반적인 서비스 품질속성에 부정적인 생각을 가지고 있다고 하더라도 불만족요인에 대한 상황대처요인에 의해 전반적인 만족을 느낀다는 것이다. 특히 상황대처요인 가운데 대응의지가 가장 큰 영향을 미치는 것으로 나타났는데 이를 두고 볼 때 외식업체에서는 고객만족을 위해 대응의지 제고방안을 강구해야 할 것이다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.439-442
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• 2002

고객을 중심으로 한 마케팅 기법중 하나인 고객관계관리(CRM : Customer Relationship Management)는 인터넷의 적용과 더불어 다양하게 발전하고 있는 분야 중 최근 가장 큰 이슈가 되고 있다. eCRU이란 CRM에서 인터넷을 이용해 기존의 시스템을 재구성하는 것을 말하는데 고객만족을 극대화하면서 동시에 관련 비용을 절감할 수 있는 새로운 고객관리라고 할 수 있다 본 논문은 웹 상의 고객 패턴을 마이닝을 통하여 고객 정보 추출을 최적화하는 알고리즘을 제시하고 이를 통해 고객분류를 자동으로 할 수 있음을 보였다.

• Asia Marketing Journal
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• v.9 no.3
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• pp.151-166
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• 2007

본 사례는 국내 렌터카 시장에서 시장점유율과 매출액에서 최고의 위치에 있는 금호렌터카의 성공요인에 대해 분석하였다. 금호렌터카가 렌터카 시장에서 대표기업으로 부상한 주요 성공요인은 고객만족을 중시하는 경영에 있음에 주목하여 금호렌터카의 고객만족 경영방식을 상세히 고찰하고 시사점을 제시하였다. 분석을 위해 금호렌터카의 고객만족 경영을 첫째, 고객의 욕구를 파악하는 것을 중심으로 하는 고객관계관리 측면과, 둘째 서비스 품질통제를 위한 서비스 프로세스 관리의 측면으로 나누어 살펴보았다.

• Journal of Global Scholars of Marketing Science
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• v.17 no.1
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• pp.23-49
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• 2007

The purpose of this study is to investigate the relationships among customer satisfaction, trust, and loyalty and examine the differences and similarities between specialized and general merchandise internet shopping malls concerning the relationships of these key success factors. The structural model is tested with each of the sub-samples(i.e., specialized and general merchandise shopping customers taken separately) and the data from entire data samples(i.e., specialized and general merchandise shopping customers pooled together). The same research model was used in analysing general merchandise, specialized, and integrated internet shopping mall to reveal and compare the casual path constructs. In the analysis of path coefficients composed of separated taken sample, all research hypothesis is accepted and the model's goodness of fit also shows all high. In the basis of the previous empirical test, multiple sample method was used. Properties of the causal paths, including standardized path coefficients, the significance of difference, latent correlations and variance explained for satisfaction, trust and loyalty in the hypothesized model, have similarities in general. This means that the proper structural management concerned with customer satisfaction, trust and loyalty is very crucial for the success of all kinds of internet shopping malls. Following the model test, I conduct a test of integrated model's path coefficients. Statistical results show that all the hypothesis concerned with internet shopping malls were accepted like the separated sample test. Click and mortar companies should clearly understand and articulate the key requirements of shopping mall satisfaction, trust and loyalty and encourage to establish linkage and interactive relationship among the research variables. In addition, internet marketers are required to customize the interaction considering and adapting the patterns of internet shopping malls. Other interesting results concerning the strategies in internet shopping malls strategy are also presented.

• Journal of dental hygiene science
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• v.4 no.2
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• pp.61-73
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• 2004

In this study, I have analyzed domestic dental clinics which had introduced CRM for customer satisfaction management. The purpose of this study is to provide information for designing efficient CRM. I would suggest some strategies to carry out CRM as followed. First, promote long-term relationship with customers. Second, select a target patient group and classify customers. Third, set a systematic database up. Fourth, make various channels to communicate with customers. In addition, the following ways are essential to be successful in implementing CRM. First, strengthen the service provided at the Moment of Truth (MOT). Second, organize learning in hospital and all the staff should be customer-oriented. Third, perform systematic "internal marketing".

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.232-234
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• 2022

Big data is widely used in customer relationship management, relationship marketing, financial business improvement, credit information and risk management. Moreover, as non-face-to-face financial transactions have become more active recently due to the COVID-19 virus, the use of financial big data is more demanded in terms of relationships with customers. In terms of customer relationship, financial big data has arrived at a time that requires an emotional rather than a technical approach. In relational marketing, it was necessary to emphasize the emotional aspect rather than the cognitive, rational, and rational aspects. Existing traditional financial data was collected and utilized through text-type customer transaction data, corporate financial information, and questionnaires. In this study, the customer's emotional image data, that is, atypical data based on the customer's cultural and leisure activities, is acquired through SNS and the customer's activity image is analyzed with an artificial intelligence CNN algorithm. Activity analysis is again applied to the annotated AI, and the AI big data model is designed to analyze the behavior model shown in the annotation.