• Journal of the Korean Society of Clothing and Textiles
• /
• v.29 no.9_10 s.146
• /
• pp.1285-1294
• /
• 2005

This study was fulfilled in the purpose of proposing construction strategies of Internet shopping-mall through the analysis of consumer moving line in Internet shopping-mall. This study was executed in two stages: theoretical study, qualitative study. In the theoretical study, hypothetical Internet shopping behavior model were constructed. Five internet shopping behavior types of apparel : purchase, searching purchase, prepurchase deliberation, information accumulation, opinion leadership and recreation were constructed. Next, consumer decision process were extracted from previous studies and a hypothetical internet shopping behavior model was constructed on the base of consumer decision process and Internet shopping behavior types. And then, through the qualitative study, Internet shopping behavior types were identified and hypothetical model was confirmed after adjustment. For qualitative study, 30 subjects were sampled by focus sampling and investigated by in-depth interview and observation. Seven internet shopping behavior types of apparel were found by the qualitative study: cautious purchase by price comparison, searching purchase, special low price purchase, impulse purchase, prepurchase deliberation, information accumulation and recreation-oriented. On the base of these behavior types, Internet shopping behavior model was adjusted and completed. Finally, according to the results of this study, Internet shopping construction methods that made customer's loyalty high and marketing strategy of Internet shopping-mall were proposed.

• Journal of Intelligence and Information Systems
• /
• v.16 no.4
• /
• pp.213-225
• /
• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.22 no.51
• /
• pp.29-40
• /
• 1999

The distribution center location and routing problem involves interdependent decisions among facility, transportation, and inventory decisions. The design of distribution system affects the customers' purchase decision by sets the level of customer service to be offered. Thus the lower product availability may cause a loss of demand as falls off the customers' purchase intention, and this is related to the firm's profit reduction. This study considers the product availability of the distribution centers as the measure of the demand level change of the demand points, and represents relation between customer service and demand level with linear demand function. And this study represents the distribution center location and routing to demand point in order to maximize the total profit that considers the products' sales revenue by customer service, the production cost and the distribution system related costs.

• International Area Studies Review
• /
• v.22 no.3
• /
• pp.149-174
• /
• 2018

This paper takes a close look at technology related services of e-Commerce companies from the viewpoint of B2C customers. The goal is to determine at which specific points of the customer purchase journey changes should occur, to address consumers' personal needs more effectively. All ten technologies examined fall into one of the following steps of the customer purchase journey: Decision Making, Payment Methods, and Delivery. AHP was utilized to clarify the preferences of millennials from Germany and Korea. The findings show that factors such as country of origin and gender had an impact on the preferences of the survey participants. In case of Germany, women replied that a change in Payment Methods would lead to significant enhancement of their shopping experiences, whereas men favored Decision Making. As for Korea, both genders stated that Decision Making should be the focus of marketers' efforts. One of the main findings was that participants from Germany and Korea exhibited different tastes in the use of technologies. Germans preferred functional technologies, whereas Koreans favored technologies which are more engaging and entertaining.

• Proceedings of the Safety Management and Science Conference
• /
• 2000.11a
• /
• pp.299-306
• /
• 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.

• Korean Management Science Review
• /
• v.26 no.1
• /
• pp.183-196
• /
• 2009

Customer retention has been a pressing issue for companies to get and maintain the loyal customers in the competing environment. Lots of researchers make effort to seek the characteristics of the churning customers and the loyal customers using the data mining techniques such as decision tree. However, such existing researches don't consider relationships among customers. Social network analysis has been used to search relationships among social entities such as genetics network, traffic network, organization network and so on. In this study, a customer network is proposed to investigate the differences of network characteristics of churning customers and loyal customers. The customer networks are constructed by analyzing the real purchase data collected from a Korean cosmetic provider. We investigated whether the churning customers and the loyal customers have different degree centralities and densities of the customer networks. In addition, we compared products purchased by the churning customers and those by the loyal customers. Our data analysis results indicate that degree centrality and density of the churning customer network are higher than those of the loyal customer network, and the various products are purchased by churning customers rather than by the loyal customers. We expect that the suggested social network analysis is used to as a complementary analysis methodology with existing statistical analysis and data mining analysis.

• Korea Trade Review
• /
• v.45 no.5
• /
• pp.201-222
• /
• 2020

This paper examines that determinants of BAOMAI, (i.e., behavior of Chinese tourist bulk purchase in duty free shop) with analytical framework and empirical tests. The results of applying the structural equation modeling to 196 samples suggest that Chinese tourist consumption orientations (conspicuous/compulsive/unplanned consumption) have a positive effect on BAOMAI decision value chain (perceived value and loyalty). The marketing mix of duty free shop as control variables in research framework also have a positive effect on BAOMAI perceived values (functional/social/emotional value). This paper has a contribution to prior literatures: the first empirical analysis on BAOMAI determinants with exploring scholarly definition.

• IE interfaces
• /
• v.21 no.3
• /
• pp.343-353
• /
• 2008

Vendor-managed inventory(VMI) is a supply chain strategy to improve the inventory turnover and customer service in supply chain management. Unfortunately, many VMI programs fail because they simply transfer the transactional aspects of placing replenishment orders from customer to vendor. In fact, such VMI programs often degrade supply chain performance because vendors lack capability to plan the VMI operations effectively in an integrated way under the dynamic, complex, and stochastic VMI supply chain environment. This paper presents a decision support system, termed DSSV, for VMI in the retail supply chain. DSSV supports the market forecasting, vendor's production planning, retailer's inventory replenishment planning, vehicle routing, determination of the system operating parameter values, retailer's purchase price decision, and what-if analysis. The potential benefits of DSSV include the provision of guidance, solution, and simulation environment for enterprises to reduce risks for their VMI supply chain operations.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.4
• /
• pp.177-182
• /
• 2024

A consumer's decisions are made by affection of product. Affection has types: evaluation, mood, emotion and sensibility that means unconscious changes. Previous researches have clarified weather factors affect to sensibility that means weather factors may have causal effects to consumer's decision making. This research utilize weather information from KMA(Korea Meteorological Administration) and SNS geographical information and text to make weather sensibility model, and clarify the model shows significant change to online shop customer's purchase behavior(purchase frequency) by merging customer's address information and geometric information of the model for apply weather model. As a result, a model utilize daily precipitation, sunshine hours, average ground temperature, and average relative humidity makes significant result to e-commerce purchase behavior frequency.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.7
• /
• pp.139-148
• /
• 2024

In the post-pandemic era, the demand for online education platforms has surged, leading to increased consumer reliance on online reviews for decision-making. This study investigates the impact of Chinese online customer reviews on consumer purchase behavior in online education. By examining the role of trust, review sentiment, and the quantity and timeliness of reviews, the research aims to understand how these factors influence consumer decisions. By using regression model, findings reveal that negative reviews, timely feedback, and a higher volume of reviews positively affect consumer purchase decisions, while course pricing demonstrates an inverse relationship. Furthermore, cognitive and affective trust mediate the relationship between reviews and purchase behavior, highlighting a reverse U-shaped effect on consumer decision inclination. These insights provide valuable implications for online education providers, emphasizing the need to manage and leverage online reviews to foster consumer trust and improve sales performance.