• Journal of Korean Institute of Industrial Engineers
• /
• v.33 no.3
• /
• pp.339-348
• /
• 2007

Since the 1980s, the marketing paradigm has rapidly changed from product-driven marketing to customer-driven marketing. Recently, due to an increase in the amount of information, customer-differentiation strategies have been emphasized more than product-differentiation strategies. This paper suggests a methodology for new customer segmentation and purchase forecasting using changes in customer behavior. This methodology includes a segmentation method for new customers using existing customer's characteristics and a purchase-forecasting system using the purchase-behavior patterns of existing customers. The proposed methodology not only provides differential services from a segmentation system but also recommends differential items from the purchase forecasting system for new and existing customers.

• Journal of Korean Institute of Industrial Engineers
• /
• v.14 no.1
• /
• pp.103-117
• /
• 1988

Replacement demand plays an important role to forecast the total demand of durable goods, while most of the diffusion models deal with only adoption data, namely initial purchase demand. This paper presents replacement demand forecasting models incorporating repurchase rate, multi-ownership, and dynamic product life to complement the existing diffusion models. The performance of replacement demand forecasting models are analyzed and practical guidelines for the application of the models are suggested when life distribution data or adoption data are not available.

• Korean Management Science Review
• /
• v.27 no.2
• /
• pp.31-44
• /
• 2010

It is very important to forecast demand and determine the optimal procurement quantities of spare parts. The Army has been forecasting demand not with actual usage of spare parts but with request quantities. However, the Army could not purchase all of forecasted demand quantities due to budget limit. Thus, the procurement quantities depend on the item managers' intuition and their meetings. The system currently used contains many problems. This study suggests a new determination procedure; 1) forecasting demand method based on actual usage, 2) determining procurement method through LP model with budge and other constraints. The newly determined quantities of spare parts is verified in the simulation model, that represents the real operational and maintenance situation to measure the operational availability. The result shows that the new forecasting method with actual usage improves the operational availability. Also, the procurement determination with LP improves the operational availability as well.

• Journal of Technology Innovation
• /
• v.13 no.1
• /
• pp.1-24
• /
• 2005

This paper applies a prelaunch forecasting model to the Home-Networking (HN) market of South Korea. The HN market of Korea is categorized into two distinctive markets. One HN market consists of new apartments in which builders install HN and the other HN market consists of existing houses in which residents purchase HN Among these markets, this paper focuses on existing houses as capturing consumers' choice. To forecast sales of HN for existing houses, we use a conjoint model based on our survey data of consumer preferences. By incorporating various indicators of HN technologies into our conjoint model, we also forecast diffusion of HN system embodied in PLC or Wireless Lan. We call this model Choice-Based Diffusion Model. In addition, based on the simulation experiments, we also identify important factors that affect the demands of HN system.

• Journal of Korea Game Society
• /
• v.4 no.1
• /
• pp.34-40
• /
• 2004

This study introduces and empirically test the validity of Bass model that helps demand forecasting of new game products. The application of Bass model to new game products show that Bass model predicts the demand of new game accurately. In particular, it showed very good predictability of on-line game products.

• Journal of Intelligence and Information Systems
• /
• v.8 no.2
• /
• pp.159-175
• /
• 2002

Web mining is an emerging science of applying modem data mining technologies to the problem of extracting valid, comprehensible, and actionable information from large databases of web in e-Business environment and of using it to make crucial e-Business decisions. In this paper, we present the noble framework of data visualization system based on web mining for analyzing the characteristics of on-line customers in e-Business. We also propose the framework of forecasting system for providing the forecasting information of sales/purchase through the use of web mining based on artificial intelligence techniques such as back-propagation network, memory-based reasoning, and self-organizing map.

• 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.

• Korean Management Science Review
• /
• v.32 no.4
• /
• pp.209-236
• /
• 2015

This study focuses on the network externality effect related to the platform supplementary services. This study designs the network externality of platform and suggests a supplementary service adoption model. Additionally, this study examines the moderating effect of demand forecasting for the platform. Using AMOS program, a structural equation modeling has been used to analyze the research model. The findings can be summarized as follows : First, we find out the structural relationship among the factors (usefulness, perceived value, purchase intention) affecting adoption of the supplementary services. Second, positive perception of platform flow can promote the platform interaction. Third, positive perception of present users based on platform can arouse friendly evaluation in the platform interaction. Fourth, loyalty to the platform brand can improve the perceived usefulness of supplementary services, but cannot lessen the resistance to supplementary service cost. In addition, the moderating effects of demand forecasting for the platform in the path leading from platform factors to supplementary service factors were identified. In conclusion, traditional brand strategy may be effective in platform marketing activities but the extent of performance in the strategy can appear to be quite different. Therefore, taking the relationship with network externality into consideration should be involved in the marketing strategy in platform.

• Korean Journal of Agricultural Science
• /
• v.47 no.4
• /
• pp.769-782
• /
• 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.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.30 no.4
• /
• pp.117-130
• /
• 2005

It is effective and desirable for a proper customer relationship management or marketing to focus on the specific customers rather than a number of non specific customers. This study forecasts the prospective purchasers with high probability to purchase a specific product. Artificial Neural Network( ANN) can classily the characteristics of the prospective purchasers but ANN has a limitation in comprehending of outputs. ANN is integrated into IRANN with IR of decision tree program C5.0 to comprehend and analyze the outputs of ANN. We compare and analyze the accuracy of ANN, IR, and IRANN each other.