• 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 CALSEC Conference
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• 2005.11a
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• pp.147-153
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• 2005

When a customer wants to buy an item at the Internet shopping mall, one of the difficulties is to decide when to buy the item because its price changes over time. If the shopping mall can be able to recommend appropriate buying points, it will be greatly helpful for the customer. Therefore, in this presentation, we propose a method to recommend buying points based on the time series analysis using a database that contains past prices data of items. The procedure to provide buying points for an item is as follows. First, we search past time series patterns from the database using normalized similarity, which are similar to the current time series pattern of the item. Second, we analyze the retrieved past patterns and predict the future price pattern of the item. Third, using the future price pattern, we recommend when to buy the item.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.67-75
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• 2021

Despite the increase in sales of imported vehicles in Korea, research on the sales forecast of parts logistics centers is very limited. This study aims to perform a sales prediction on bestselling goods in the automobile part logistics center. System dynamics was adopted as a methodology for the prediction method, which considered causal relationship of variables that affected the dynamic characteristics and feedback loops. The analysis results showed that the consumable sales amount of oil increased over time. As a result of conducting the MAPE, the model was assessed to be a reasonable predictive model of 31.3%. In addition, the sales of battery products increased from every October in both of actual and predicted data followed by the peak sales in December and then decrease from next February. This study has academic implications that it secured actual data of specific imported automobile part logistics center, which has not done before in previous studies and quantitatively analyzed the prediction of the quantity of released goods of future sales through system dynamics.

• Spatial Information Research
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• v.12 no.2
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• pp.211-228
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• 2004

The purpose of this study is to analyze the sales area of gas stations to see the quality and the efficiencies of spatial distribution structure for petroleum products in Tae-jeon City. Location pattern of gas station is classified by factors of competitive facilities, transportation, population and landuse in Tae-jeon City. As a result, High profit pattern and low profit pattern is classified. The characteristics of the distribution pattern of gas station are that the while densely populated has a small sales area, the thinly populated region has huge ones. Location-allocation model is used in order to minimize the travel distance from consumer location to gas station and balance the spatial distribution of gas station in case studies. The result reveals that the model-based locations of gas stations are more dispersed and balanced in the whole Tae-jeon City compared with the actual location of gas stations. This study shows the characteristics and spatial distribution patterns of sales area and location in petroleum products distribution facilities.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.491-494
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• 2004

최근 인터넷 쇼핑몰에서 상품을 구매하는 고객들에게 편의성과 효율성을 제공하기 위하여 구매자들의 선호도나 가격에 맞는 상품을 추천해 주는 연구들이 활발하게 진행되고 있다. 그러나 이러한 상품을 추천하는 연구들은 다양하게 발전하고 있지만 추천된 상품들의 구매시점에 관한 연구는 찾아보기 어렵다. 이에 본 논문에서는 인터넷 쇼핑몰의 적극적인 마케팅 일환으로 상품을 구매할 시점을 추천해 주는 방안을 제안한다. 이를 위하여 과거의 판매 기록 데이터베이스에 있는 판매가격의 기준 시계열 패턴과 유사한 시계열 패턴을 정규화 변환된 유사도로써 검색한다. 검색된 과거 가격 패턴을 기준으로 미래 가격 패턴을 분석하여, 미래 가격 패턴의 변화에 따라 상품 구매시점을 추천한다. 또한 본 논문에서는 이러한 구매시점을 추천하는 상품 추천 시스템을 설계한다.

• The tire
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• s.102
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• pp.41-43
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• 1982

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.6
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• pp.1053-1069
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• 2020

This study examined the sales pattern relationship with respect to product attributes to propose sales forecasting for fashion products. We analyzed 537 SKU sales data of T-shirts in the domestic sports brand using SAS program. The sales pattern of fashion products fluctuated and were influenced by exogenous factors; therefore, we removed the influence of exogenous factors found to be price discounts and holiday effects as a result of regression analysis. In addition, it was difficult to predict sales using the sales patterns of the same product since fashion products were released as new products every year. Therefore, the forecasting model was proposed using sales patterns of related product attributes when attributes were considered descriptive variables. We classified sales patterns using K-means clustering in order to explain the relationship between sales patterns and product attributes along with creating a decision tree classifier using attributes as input and sales patterns as output. As a result, the sales patterns of T-shirts were clustered into six types that featured the characteristic shape of peak and slope. It was also associated with the combination of product attributes and their values in regards to the proposed sales pattern prediction model.

• Annual Conference of KIPS
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• 2001.04a
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• pp.15-18
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• 2001

개별화 웹 마케팅은 본질적으로 고객지향의 패러다임이다. 즉, 개별 고객의 특수한 니즈를 개별적으로 파악해서 각각의 고객에게 차별화된 서비스를 제공하는 것이 그 핵심이다. 웹 서버의 고객이 접근한 상품의 로그파일에 데이터마이닝의 연관규칙 기술을 이용하게 되면 고객행동 패턴의 파악 및 예측을 위한 기법으로 활용할 수 있다. 본 연구에서는 웹 사용자의 교차 판매를 위한 원투원 마케팅에 필요한 접근패턴을 분석하고자 하며, 이는 고객의 상품에 대한 접근이 기록된 액세스 로그 데이터베이스의 분석을 통하여 이루어진다. 이들 데이터 처리와 교차판매 지원을 위한 데이터마이닝 모델링, 이를 통한 원투원 마케팅 모델 제시, 그리고 이의 활용이 고객관계관리(eCRM)에 미치는 효과를 제시한다.

• The Journal of Society for e-Business Studies
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• v.19 no.1
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• pp.119-129
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• 2014

The renowned Pareto rule of 80/20 has been challenged in the electronic marketplace with the emergence of long tail economy. Mass customization on top of the Internet infrastructure is expected to explain these changes of product concentration. In this paper, we empirically analyzed the micro-transactional data of a Groupon-like daily deal web site to identify the changes of product and customer concentration. The results show the long tail pattern aligned with the previous research on the e-commerce literature on the long tail. We find that the notification setting on email or SMS about daily deal influences the patterns of sales concentration. The information through email and SMS is expected to enable consumers to know about daily bargains and purchase the coupons eventually. However, the email notification for niche products results in the decreased sales while the SMS notification for overall product promotes overall products.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.81-83
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• 1999

본 논문은 전자상거래에서 판매와 구매방법의 획일화된 검색기법을 이용한 상품의 검색 및 사용자 인터페이스를 지능형 대화 판매 에이전트를 설계함으로써 보다 편리하고 효율적인 사용자 인터페이스를 제공하는 시스템을 설계하였다. 기존 사이버 쇼핑몰에서 구매자의 검색에 의한 방식을 판매자와의 대화에 의한 검색방법으로 전환하여 구매자의 구매의욕을 증가시키고 검색에 소요되는 시간을 절약할 수 있을 뿐만 아니라 구매자 어휘를 분석하고 구매패턴을 파악하여 추가의 수요를 창출 할 수 있는 데이터를 축적하는 방법을 제시하고 많은 고객을 동일한 시간에 상대해야 하는 웹의 전자상거래 시스템에서 판매를 담당하는 에이전트를 설계하게 되었다.