• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2001.05a
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• pp.23-24
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• 2001

기존의 전자상거래에서 사용되고 있는 지능형 검색엔진은 단순히 구매자가 상품에 대한 선호도를 직접 입력하면 그것을 바탕을 검색하는 기능의 형태였다. 그러나 고정된 형식을 바탕으로 한 선호도 등록은 각각의 사용자의 구매 패턴 등을 정확히 표현할 수 없고, 고객의 요구사항이 다이나믹하게 변화하는 것에 대처하기 힘들다. 본 논문에서는 사용자의 선호도를 고객의 등록에만 의존하지 않고 능동적으로 고객의 구매 패턴 등을 수집하고 고객의 선호도를 분석하고 상품정보를 제공할 수 있는 쇼핑몰을 제시한다.

• The Research Journal of the Costume Culture
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• v.32 no.2
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• pp.199-214
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• 2024

Until now, research on consumers' purchasing behavior has primarily focused on psychological aspects or depended on consumer surveys. However, there may be a gap between consumers' self-reported perceptions and their observable actions. In response, this study aimed to investigate consumer purchasing behavior utilizing a big data approach. To this end, this study investigated the purchasing patterns of fashion items, both online and in retail stores, from a data-driven perspective. We also investigated whether individual consumers switched between online websites and retail establishments for making purchases. Data on 516,474 purchases were obtained from fashion companies. We used association rule analysis and K-means clustering to identify purchase patterns that were influenced by customer loyalty. Furthermore, sequential pattern analysis was applied to investigate the usage patterns of online and offline channels by consumers. The results showed that high-loyalty consumers mainly purchased infrequently bought items in the brand line, as well as high-priced items, and that these purchase patterns were similar both online and in stores. In contrast, the low-loyalty group showed different purchasing behaviors for online versus in-store purchases. In physical environments, the low-loyalty consumers tended to purchase less popular or more expensive items from the brand line, whereas in online environments, their purchases centered around items with relatively high sales volumes. Finally, we found that both high and low loyalty groups exclusively used a single preferred channel, either online or in-store. The findings help companies better understand consumer purchase patterns and build future marketing strategies around items with high brand centrality.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.413-414
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• 2023

IT 기술의 발달로 E-Commerce 분야는 실시간으로 발생되는 데이터양이 증가하고 있으며, 발생된 데이터는 개인화 맞춤 서비스에 많이 활용되고 있다. 그러나 신생 E-commerce 기업은 신규 상품 및 기존 상품에 대한 정보와 고객 간의 상호 작용 데이터가 존재하지 않아 콜드 스타트 문제가 발생한다. 이에 본 논문에서는 E-commerce 환경에서 실시간 사용자 구매패턴 분석을 통한 사용자 상품 추천 시스템을 제안한다. 제안하는 시스템은 Kafka와 Spark를 사용해 실시간 스트림을 데이터를 처리한다. 주요 기능은 ALS 알고리즘과, FP-Growth 알고리즘을 적용해 콜트 스타트 문제를 해결하며, 사용자 구매 패턴 분석을 통한 분석 결과에 맞는 상품을 사용자에게 추천한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.382-385
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• 2016

본 논문에서는 오프라인의 장점인 고객방문, 실물전시 및 체험 구매를 위한 상품콘텐츠를 가상공간에 디지털화로 제작하고 실시간 제공함으로써 고객들의 구매욕구와 구매패턴의 변화수용과 서비스 만족도를 향상시키는 O2O수주시스템을 제안한다. 이 시스템은 오프라인 패턴에 익숙한 카탈로그 책자중심의 사용자경험을 디지털로 전환하여 온라인의 거부감과 불편함을 해소하였고 편리성을 높이기 위해 다양한 접근 디바이스환경 제공과 오프라인에서 할 수 없는 동영상에 의한 사용법, 제품 도면 등을 e-카탈로그로 제공하여 성공적인 매출확대를 창출하도록 설계, 구현하였다.

• The KIPS Transactions:PartD
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• v.19D no.1
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• pp.121-126
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• 2012

The study aims to identify and classify the decision factors in selecting a device, to measure buyers' perception for the factors at the point of smart phone purchase, and finally to propose the business strategies based on those purchase pattern changes. The research performs to not only investigate the decision factors through literature reviews, but also draw the device selection factors directly from buyers. The findings illustrated how the perceptions between the first buyers and existing buyers have been changes. The analyses will practically contribute as the references when the business strategy and policy are planned by smart phone related companies and the government.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.495-507
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• 1998

For the product development using Human Sensibility Ergonomics, it is very important to understand human preference and sensibility of products. Human sensibility is affected by each person's psychological factor and is different for individuals. This paper analyzed consumer's automobile purchase patterns, according to the types and the features of face, using the psychological concept that human the types and the features of face have been used to understand human personalities. In this paper, it is showed that there are some differences among customer's automobile purchase patterns, according to the types and the features of face If further study has been done in this field, this study could be applicated in Human Sensibility Ergonomics, Marketing, etc.

• 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)

• Journal of the Korea Society of Computer and Information
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• v.12 no.4
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• pp.179-189
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• 2007

Every purchase a customer makes builds patterns about how products are purchased together. The process of finding these patterns, called market basket analysis, is composed of two steps in the Microsoft Association Algorithm. The first step is to find frequent item-sets. The second step which requires much less time than the first step does is to generate association rules based on frequent item-sets. Even though the first step, finding frequent item-sets, is the core part of market basket analysis, when applied to Online Analytical Processing(OLAP) cubes it always raises several points such as longitudinal analysis becomes impossible and many unpractical transactions are built up. In this paper, a new OLAP cube structures designing method which makes longitudinal analysis be possible and also makes only real customers' purchase patterns be identified is proposed for market basket analysis.

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