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

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.31-34
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• 2001

The customer unsatisfaction in the new products exists, though the most of enterprises using QFD. It is mainly caused by the failure of corrective determination of technical importance rating(TIR). To derive the technical importance rating, the impact of the fulfillment of design requirements on the satisfaction of customer requirements must first be quantified. This has been accomplished through the use of a 1-3-9 or a 1-5-9 scale and ignored the peak of the house of quality(HOQ). In this paper we suggested the methodology reflecting effectiveness among engineering characteristics and adjusting the relationship strength between customer attribute(CA) and engineering characteristic (EC), by using limit probability and PCMR(pairwise comparison and median rank). With using this method, the determination of TIR would be more suitable for the voice of customers objectively. Here negative correlation is ignored.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.389-392
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• 2020

The growth in social media, blogs and restaurant listing directories have led to increasing customer reviews about restaurants, their quality of food items and services available on the internet. These user reviews offer a massive amount of valuable information that can be used for various decision-making purposes. Currently, most food recommendation sites provide recommendation scores about restaurants rather than food items of the restaurant and the provided recommendation scores may be biased since they are calculated only from user reviews listed only in their sites. Usually, people wants a reliable recommendation about foods, not restaurant. In this paper, we present a reliable Korean food items rating method; we first extract food items by applying NER technique to restaurant reviews collected from many Korean restaurant recommendation web sites, blogs and web data. Then, we apply lexicon-based sentiment analysis on collected user reviews and predict people's opinions as sentiment polarity scores (+1 for positive; -1 for negative; 0 for neutral). Finally, by taking average of all calculated polarity scores about a food item, we obtain a rating to individual menu items of the restaurant. The proposed food item rating is more reliable since it does not depend on reviews of only one site.

• IE interfaces
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• v.25 no.2
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• pp.229-240
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• 2012

Conventional research on customer relationship management(CRM) in general has focused on the effects of individual customer's satisfaction, retention and profit management. However, corporate customers are more profitable than individual customers because of high volume and frequent transactions between companies. In this article, a prototype for a corporate customer relationship management is developed in the postal service. First, the frequency and amount of customers' usage were examined, and thereby the corporate customer rating scheme was established to provide customized service. Second, five different types of usage patterns were determined using clustering analysis. In addition, we presented the rationales behind the five types of patterns. Third, RFM(recency, frequency, monetary) analysis was performed, and then action plans were developed to increase sales. Finally, the prototype software was developed to automatically perform the above analysis using MS Excel program.

• Journal of the Korean Society for Railway
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• v.15 no.3
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• pp.300-305
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• 2012

The saving of time, which is defined as the demand from passengers and the supply from the urban railway, must be balanced. The selected factors influencing on the balance are the traffic, customer contact facilities, the number of failures, customer complaints(VOC), passenger moving time and transfer stations. Also, the overall ratio of SMRT's 4 lines is generated by differentiating the rate of each attribute in each factor. This is not only to differentiate the stations with peculiar factors but also to standardize criteria of the personalized services. Furthermore, as part of standardization, standard drawings of facility management are prepared for the improvement on the management of human resource and material. The drawings include passenger moving lines, location of safety incidents and also indicate the rating of the factors in each station and overall evaluation rating. In conclusion, this thesis aims to improve customer satisfaction constantly by reducing passenger moving time, through the differentiated management of each station.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.336-343
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• 2009

Companies often use database for performing task more efficiently and data mining for marketing and production efficiency through analyzing of the stored database. The use of the knowledge through the data mining maintains and provides a direction of development for the company. It could be as an additional competitive power for the company when decision making is necessary. This study is designing a model that predicts a rating of existing customer and consumption pattern with using actual data of the manufacturer and data mining methodology. The objective of this model is to improve profits for the company and brand value through connecting the marketing with identifying the customer's rating and consumer behavior.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.79-98
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• 2011

This paper proposes a semantic recommendation technique for a personalized e-commerce portal. Semantic recommendation is achieved by utilizing the attributes of products. The semantic similarity of the products is merged with the rating information of the products to provide an accurate recommendation. The recommendation technique also analyzes various attitudes of the customer to evaluate the implicit rating of products. Attitudes are classifies into three types such as "purchasing product", "adding product to shopping cart", and "viewing the product information." We implicitly track customer attitude to estimate the rating of products for recommending products. Also we implement a session validation process to identify the valid sessions that are highly important for giving an accurate recommendation. Our recommendation technique shows a high degree of accuracy as we use age groupings of customers with similar preferences. The experimental section shows that our proposed recommendation method outperforms well known collaborative filtering methods not only for the existing customer, but also for the new user with no previous purchase record.

• Journal of Applied Tourism Food and Beverage Management and Research
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• v.15 no.1
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• pp.57-80
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• 2004

Resort Hotel Rate System was first introduced as an official guideline after Tourism Promotion Act, which enables the secretary of transportation to rate resort hotel by its facility and accommodation, was enacted on January 18, 1971. And the system was modified time to time to what we currently have after numerous revisions. However, the system has made a slow progress compare to the other countries system and have shown many potential problems that need to be improved. There is a problem that it is not even clear whether the act is as effective to apply it to rate any resort hotel in reality. The hotel rate system was first introduced in 1970's and changed ever since, and it also changed the private organizations to audit the decision. However, unlike the hotels in other countries, our hotel rating system is not focus on the customer's service and informations. It focus on the hotel's quality so that cause the problem whether the hotel is for customer or not In other different countries, they have some specific standard for evaluation of customer service based on customers' reference or needs. However, there is no evaluation part concerning on customer service in Korea. Also, even the hotel rating system is not based on the hotel waitress or waiter's service part. It means the system is almost focus on the hotel's qualities. Therefore, customer who needs hotel service, can not trust whether they can choose the hotels which gives the right informations and good quality services. Although hotel's physical layout is important, the service part is also important for evaluating the hotel entirely. There are a lot of things to develop and to be changed in order to develop tourism industry in the process of decision about Hotel's level in Korea Thus, this research will summarize some problems which are revised through the former research of hotel's level. And it will compare the system of hotel's level between Korea and developed countries in hotel industry Additionally, I will show current tourism industry in Korea. Finally, I suggest the improvement proposal for the level system of hotel in Korea and process of this system in the future.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.75-82
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• 2023

In collaborative filtering systems, the item rating prediction values calculated by the systems are very important for customer satisfaction with the recommendation list. In the time-aware system, predictions are calculated by reflecting the rating time of users, and in general, exponentially lower weights are assigned to past rating values. In this study, to find out whether the influence of rating time on the rating value varies according to various factors, the correlation between user rating value and rating time is investigated by the degree of user rating activity, the popularity of items, and item genres. As a result, using two types of public datasets, especially in the sparse dataset, significantly different correlation index values were obtained for each factor. Therefore, it is confirmed that the influence weight of the rating time on the rating prediction value should be set differently in consideration of the above-mentioned various factors as well as the density of the dataset.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1467-1477
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• 2011

This paper proposes a HPPS(Hybrid Preference Prediction System) design using the analysis of user profile and of the similarity among users precisely to predict the preference for custom-tailored service. Contrary to the existing NBCFA(Neighborhood Based Collaborative Filtering Algorithm), this paper is designed using these following rules. First, if there is no neighbor's commodity rating value in a preference prediction formula, this formula uses the rating average value for a commodity. Second, this formula reflects the weighting value through the analysis of a user's characteristics. Finally, when the nearest neighbor is selected, we consider the similarity, the commodity rating, and the rating frequency. Therefore, the first and second preference prediction formula made HPPS improve the precision by 97.24%, and the nearest neighbor selection method made HPPS improve the precision by 75%, compared with the existing NBCFA.