• 농업과학연구
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• 제47권4호
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• pp.769-782
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• 2020

Interest in fruit and vegetables has increased due to changes in consumer consumption patterns, socioeconomic status, and family structure. This study determined the factors influencing the demand for fruit and vegetables (strawberries, paprika, tomatoes and cherry tomatoes) using a panel of Rural Development Administration household-level purchases from 2010 to 2018 and compared the ability to the prediction performance. An artificial neural network model was constructed, linking household characteristics with final food expenditure. Comparing the analysis results of the artificial neural network with the results of the panel model showed that the artificial neural network accurately predicted the pattern of the consumer panel data rather than the fixed effect model. In addition, the prediction for strawberries was found to be heavily affected by the number of families, retail places and income, while the prediction for paprika was largely affected by income, age and retail conditions. In the case of the prediction for tomatoes, they were greatly affected by age, income and place of purchase, and the prediction for cherry tomatoes was found to be affected by age, number of families and retail conditions. Therefore, a more accurate analysis of the consumer consumption pattern was possible through the artificial neural network model, which could be used as basic data for decision making.

• 지능정보연구
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• 제21권3호
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• pp.101-116
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• 2015

많은 기관들이 데이터에 기반을 둔 의사결정을 수행해 왔으며, 특히 수치자료를 비롯한 정형 데이터가 이러한 목적으로 널리 활용되어 왔다. 하지만 최근에는 스마트기기와 소셜미디어의 발달로 인해 다양한 형태를 가진 방대한 양의 정보가 생성, 공유, 저장되면서, 전통적인 정형 데이터 기반 의사결정으로부터 비정형 빅데이터 기반 의사결정으로 관심의 전환이 이루어지고 있다. 데이터 기반 의사결정의 대표적 분야인 추천시스템 분야에서도 성능 향상을 위해 비정형 데이터를 활용해야 한다는 필요성이 최근 꾸준히 제기되고 있다. 특히 사용자의 성향이나 선호도는 고객의 니즈와 직결되기 때문에, 비정형 데이터 분석을 통해 사용자의 성향을 파악하고 이를 통해 상품 추천 및 구매 예측의 정확도를 향상시키기 위한 노력이 매우 시급하게 이루어질 필요가 있다. 따라서 본 연구에서는 사용자의 성향을 측정하여 재구매 예측 정확도, 특히 카테고리별 재구매 예측 정확도를 높임으로써, 궁극적으로 추천시스템의 성능을 향상시킬 수 있는 방안을 제시한다. 구체적으로는 사용자의 일상적인 인터넷 사용 기록을 분석하여 고객이 조회하는 뉴스 기사의 이슈를 식별하고 다양한 이슈에 대한 고객의 관심을 계량화한 후, 이를 활용하여 고객의 카테고리별 재구매 여부를 예측하는 모델을 제안하고자 한다. 실제 웹 트랜잭션으로부터 도출된 인터넷 뉴스 조회 기록 및 쇼핑몰 구매 기록을 대상으로 실험을 수행한 결과, 고객의 과거 구매이력만을 활용한 카테고리 재구매 예측 모형에 비해 본 연구에서 제안한 모형, 즉 고객의 과거 구매이력과 관심 이슈를 모두 활용한 예측 모형의 정확도가 다소 우수한 것으로 나타났다.

• 한국경영과학회지
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• 제34권4호
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• pp.57-72
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• 2009

As development of information technologies, customer retention has been an important issue in the competing environment. A lot of researches focus on prediction of the churning customers and seeking their characteristics. However, relationships among customers or products have not been considered in existing researches. In this study, product networks are proposed and analyzed to investigate the differences of network characteristics of products purchased by potential churning customers and those of loyal customers. The product networks are constructed from real product purchase data collected from a Korean department store. We investigated the characteristic differences, such as the degree centrality, degree centralization, and density, of two product networks constructed by potential churning customers and the loyal customers. The results indicate that degree centrality, density and degree centralization of the product network of the loyal customers are higher than those of the potential churning customers. And the promotional products of the department store are resulted to be effective in attracting the loyal customers.

• Journal of Information Technology Applications and Management
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• 제25권2호
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• pp.73-90
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• 2018

Despite of increasing studies for product recommendation, the recommendation of product repurchase timing has not yet been studied actively. This study aims to propose deep neural network models usingsimple purchase history data to predict the repurchase timing of each customer and compare performances of the models from the perspective of prediction quality, including expected ROI of promotion, variability of precision and recall, and diversity of target selection for promotion. As an experiment result, a recurrent neural network (RNN) model showed higher promotion ROI and the smaller variability compared to MLP and other models. The proposed model can be used to develop a CRM system that can offer SMS or app-based promotionsto the customer at the right time. This model can also be used to increase sales for product repurchase businesses by balancing the level of ordersas well as inducing repurchases by customers.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.193-197
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• 2021

Customers are increasingly attracted towards different e-commerce websites and applications for the purchase of products significantly. This is the reason the sellers are moving to different internet based services to sell their products online. The growth of customers in this sector has resulted in the use of big data analytics to understand customers' behavior in predicting the demand of items. It uses a complex process of examining large amount of data to uncover hidden patterns in the information. It is established on the basis of finding correlation between various parameters that are recorded, understanding purchase patterns and applying statistical measures on collected data. This paper is a document of the bottom-up strategy used to manage the selling price of a first-time product for maximizing profit while selling it online. It summarizes how existing customers' expectations can be used to increase the sale of product and attract the attention of the new customer for buying the new product.

• 한국정보시스템학회지:정보시스템연구
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• 제22권3호
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• pp.25-42
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• 2013

Recently various kinds of Information Technology services are created and the quantities of the data flow are increase rapidly. Not only that, but the data patterns that we deal with also slowly becoming diversity. As a result, the demand of discover the meaningful knowledge/information through the various mining analysis such as linkage analysis, sequencing analysis, classification and prediction, has been steadily increasing. However, solving the business problems using data mining analysis does not always concerning, one of the major causes of these limitations is there are some analyzed data can't accurately reflect the real world phenomenon. For example, although the time gap of purchasing the two products is very short, by using the traditional sequencing analysis, the precedence relationship of the two products is clearly reflected. But in the real world, with the very short time interval, the precedence relationship of the two purchases might not be defined. What was worse, the sequence of the purchase intention and the sequence of the purchase realization of the two products might be mutually be reversed. Therefore, in this study, an expanded sequencing analysis methodology has been proposed in order to reflect this situation. In this proposed methodology, the purchases that being made in a very short time interval among the purchase order which might not important will be notice, and the analysis which included the original sequence and reversed sequence will be used to extend the analysis of the data. Also, to some extent a very short time interval can be defined as the time interval, so an experiment were carried out to determine the varying based on the time interval for the actual data.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2000년도 추계학술발표논문집
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• pp.299-306
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• 2000

General definition of data mining is the knowledge discovery or is to extract hidden necessary information from large databases. Its technique can be applied into decision making, prediction, and information analysis through analyzing of relationship and pattern among data. One of the most important work is to find association rules in data mining. The objective of this paper is to find customer's trend using association rule from analysis of database and the result can be used as fundamental data for CRM(Customer Relationship Management). This paper uses Apriori algorithm and FoodMart data in order to find association rules.

• 대한산업공학회지
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• 제41권1호
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• pp.10-16
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• 2015

In this study, we suggest a method to predict probability distribution of a new customer's degree of loyalty using C-CRF that reflects the RFM score and similarity to the neighbors of the customer. An RFM score prediction model is introduced to construct the first feature function of C-CRF. Integrating demographical similarity, purchasing characteristic similarity and purchase history similarity, we make a unified similarity variable to configure the second feature function of C-CRF. Then parameters of each feature function are estimated and we train our C-CRF model by training data set and suggest a probabilistic distribution to estimate a new customer's degree of loyalty. An example is provided to illustrate our model.

• 산경연구논집
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• 제10권10호
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• pp.53-65
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• 2019

Purpose - Current study aimed at investigating the effects of the choice easiness as a thought triggered at the time of making decision and the goal achievement emotion as a prediction of how consumers feel in the state of achieving consumption goal on brand purchase intention. And It also explored moderation role of incidental pride type such as ambient hubris pride and ambient authentic pride felt before the event in the effects of message type such as self-verifying message and self-enhancing message on the choice easiness and the goal achievement emotion. Research design, data, and methodology - Message type was divided into self-verifying message and self-enhancing message. Incidental pride type was divided into hubris and authentic pride. Smart mobile phone was selected for empirical study. And the experiment was performed with 2(pride type: hubristic versus authentic) × 2(message type: self-verifying message versus self-enhancing message) between-subjects design. Questionnaires from 215 undergraduate students were used to test hypotheses by Macro process model 7. The hypotheses were tested at each of self-verifying message group and self-enhancing message group. Results - First, both choice easiness and goal achievement emotion positively influenced on the purchase intention at both self-verifying message group and self-enhancing message group. Second, at self-verifying message group, the positive effects of self verification on both choice easiness and goal achievement emotion were higher to the customers under incidental hubris pride than to those under incidental authentic pride customers. Third, at self-enhancing message group, the positive effects of self enhancement on goal achievement emotion were higher to the customers under incidental authentic pride than to those under incidental hubris pride. However, at self-enhancing message group, the positive effects of self enhancement on choice easiness (goal achievement emotion) were not higher (higher) to the customers under incidental authentic pride than to those under incidental hubris pride. Conclusions - Focusing on the results of this study, to promote their brand purchase intention, brand managers should use self-enhancing message to induce goal achievement emotion from incidental authentic pride customers. And the brand managers should develop and use self-verifying message to induce choice easiness as well as goal achievement emotion from hubris pride customers, which in turn, promote their brand purchase intention.

• 한국콘텐츠학회논문지
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• 제6권10호
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• pp.163-173
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• 2006

섬유 패션 산업은 소비자의 구매 욕구를 예측하여 제품을 개발하는 경우가 빈번하며, 예측이 잘못될 때는 즉 소비자들이 특정제품을 외면하는 경우 할인판매를 통해 제품을 처리해야 하는 문제점을 안고 있다. 반면에 신속대응 시스템은 소비자의 욕구를 지속적으로 관찰하여 신속하게 제품개발 및 생산일정을 수립함으로써 불필요한 재고가 쌓이는 경우를 사전에 방지할 수 있게 한다 소비자의 욕구는 POS 시스템에서 창출되는 자료를 통해 수집 분석되고 이런 소비자의 선호도는 네트워크를 통해 실시간으로 관련 제조업자에게 제공되어 제조업자들이 소비자의 선호도에 부합하는 제품을 개발, 생산, 제공할 수 있도록 해준다. 본 연구에서는 신속대응 시스템의 주요한 목표인 신기술의 접목을 통하여 의류제품의 기획, 구매, 생산, 유통과정 상의 재고 수준의 절감 및 과정 소요기간의 단축, 의류제조업자와 소매업자간의 보다 나은 협조체계의 개발, 소비자의 욕구에 적절히 대응하는 시스템을 학생들에게 교육할 수 있는 프로그램을 개발하였는데 신속대응 시스템을 위한 섬유 패션 스트림간 상품 기획 프로그램을 개발하였다.