• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.323-326
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• 2005

실세계에서 일어나는 문제는 매우 복잡하고 다양하기 때문에 예측하기가 어렵고 다양한 상황들이 발생한다. 특히, 소비자의 구매에 따르는 행동을 분석하고 소비자의 다양한 기호를 예측하기 위해서는 구매자의 심리적 요인과 내적 요인이 많은 영향을 미치게 된다. 이러한 요인들은 직접적인 정보 처리가 어렵기 때문에 정보의 불확실성을 취급하는 기술이 필요하다. 따라서 본 논문에서는 상품 구매에 따르는 소비자의 구매행동 패턴을 분석하기 위해 판매자의 노하우와 소비자의 구매의식을 조사하여 이 데이터를 바탕으로 베이지안 네트워크를 구성하고 구매패턴을 분류하는 방법을 제안하였다. 특히, 베이지안 네트워크를 이용하여 불필요한 속성을 가진 데이터를 제거한 후 코호넨의 SOM을 이용하여 소비자의 구매 패턴을 분류하도록 하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.306-311
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• 2005

To analyze the consumer's purchase pattern, we must consider a factor which is a cultural, social, individual, psychological and so on. If we consider the internal state by the consumer's purchase, Both the consumer's purchase action and the purchase factor can be predicted, so the corporation can use effectively in suitable goods development in a consumer's preference. These factors need a technology that treat uncertain information, because it is difficult to analyze by directly information processing. Therefore, bayesian network manages elements those the observation of inner state such as consumer's purchase is difficult. In addition, it is interpretable about data that the observation is impossible. In this paper, we examine the seller's know-how and the way of consumer's purchase to analyze consumer's purchase action pattern through goods purchase. Also, we compose the bayesian network based on the examined data, and propose the method that predicts purchase patterns. Finally, we remove the data including unnecessary attribute using the bayesian network, and analyze the consumer's Purchase pattern using Kohonen's SOM method.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.239-241
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• 2000

전자상거래에서는 소비자의 구매 패턴을 분석하는 것이 필요하다. 이러한 패턴을 효과적으로 분석하기 위해 지능형 로그 서버를 정의하고 이를 설계, 구현하였다. 지능형 로그 서버란 전자상거래 쇼핑몰의 환경에서 사용자 로그를 데이터베이스화하여 저장하고 데이터베이스에 저장된 정보를 서버 종류별, 시간별, 페이지별 등으로 분석하여 사용자 패턴을 분석할 수 있는 서버를 말한다. 이 서버는 텍스트 파일로 로그를 저장하는 서버보다 자세한 정보를 효율적으로 저장할 수 있다. 그리고 데이터베이스 접근 기술로 ADO(ActiveX Data Object)를 사용하여 데이터베이스 접근 속도를 향상시켰으며 관계형.비관계형 데이터베이스에 모두 접근할 수 있다는 장점을 가진다. 또한 소비자의 구매 패턴을 분석하기 위해 DBMiner2.0을 사용하였다.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.347-357
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• 2000

In this study, the buying pattern of consumers in domestic e-business is analyzed by conjoint analysis. We showed the followings through online survey: the consumers prefer comprehensive distributing company to broker type company, the product of the well-known company to that of the specialized company in brand, credit to e-money for payment. Quick delivery and the convenience in exchanges and refunds are more preferable.

• Proceedings of the Costume Culture Conference
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• 2003.04a
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• pp.99-100
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• 2003

최근, IT산업의 급격한 발달과 쇼핑패턴의 변화로, 전 세계적으로 온라인쇼핑의 이용자가 증가되고 있다. 이러한 변화와 더불어 학계에서 중요하게 다루어진 부분은 소비자들이 사이버공간이라는 새로운 쇼핑매개체를 어떻게 받아들이는가 하는 부분, 즉 온라인 쇼핑에서 기존의 구매방식과는 다르게 어떠한 위험들이 지각되고 있는가 하는 것이다. 온라인 쇼핑에서 지각되는 위험을 알고, 이를 고려한 쇼핑환경을 조성한다면, 소비자들이 온라인 쇼핑패턴을 받아들이고 신뢰하게 되며, 나아가 특정 온라인 쇼핑몰에 상표충성 하도록 할 수 있을 것이다. (중략)

• Proceedings of the Korean Information Science Society Conference
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• 2000.10a
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• pp.163-165
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• 2000

고객 DB를 통해 세분 고객별로 구매량, 주요 고객을 파악케 하며 각종 마케팅 활동이나 판촉활동이 고객의 구매/이용 패턴에 어떤 영향을 미치며, 물품을 구매하는 소비자의 다양한 구매 패턴을 분석하기 위해서 쇼핑몰 운영자가 여러 각도에서 문제 분석과 의사 결정을 빠르고 신속하게 할 수 있도록 기존 쇼핑몰의 관계형 DB을 이용하여, 다차원적 데이터 모델링을 통해서 다차원적인 분석이 가능하도록 하는 ROLAP를 이용한 인터넷 쇼핑몰의 데이터 웨어하우스 구축 방안을 제시하고자 한다.

• Monthly Korean Chicken
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• v.17 no.10
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• pp.120-121
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• 2011

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1125-1128
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• 2012

최근 FTA 체결은 국내의 농식품 소비자들을 값싼 외국산 농식품으로 소비 패턴을 변화시킬 수 있다. 또한 유통시장의 변화 즉, 소비자-생산자 간의 직거래 형태는 개인이 프로슈머로서 농식품 관련 컨텐츠의 제작과 생산이 더욱 활발해지도록 하며 소비자들이 구매의사 결정에 중요하게 작용하고 있다. 따라서 농식품의 효과적인 마케팅 전략읠 수립 및 실행을 위하여 소비자가 무엇을 원하고 인식하지 못한 욕구가 있는지 소비자 유형을 분석 할 필요가 있다. 본 논문에서는 농식품 소비자 구매의도를 통한 제품이나 서비스의 이용의도로서 종속변수로 설정하여 구매의도에 영향을 미치는 요인을 분석하였다. 또한 연구 모형을 위해 교류빈도, 친밀감, 호혜성, 감정의 강도라는 SNS 특성을 도출하여 분석하였다.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of Internet of Things and Convergence
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• v.5 no.2
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• pp.7-13
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• 2019

As online shopping is activated by the development of the Internet, consumers' purchase form is changing from the traditional face-to-face purchase method to online purchase method. Many sellers have flowed into shopping malls, and competition among sellers is very intense. Therefore, sellers in shopping malls need to establish rational marketing strategies by analyzing consumer purchase patterns and product sales trends. In this paper, we analyzed the purchase price of consumers by analyzing the product price, rating, and sales quantity of competitors who sell the same product in open shopping malls by time zone. In addition, the collected information was visualized in a chart so that the company's and competitors' sales trends could be easily compared. Using the above system, it is possible to predict the sales volume through the analyzed purchasing pattern and to select the reasonable price of the product by grasping the sales trend.