• Journal of Digital Convergence
• /
• v.18 no.6
• /
• pp.311-321
• /
• 2020

The purpose of the study is to verify the effectiveness the Freshman Preliminary Health Administrators(FPHA)' Empathic Problem Solving Ability(EPSA) through the application of Medical Service Design Thinking(MSDT) conducted by undergraduate school of SNS hospital marketing education. The pre-post questionnaire survey was conducted on 39 students in the freshman year of the Department of Health Administration after applying MSDT for 15 weeks from September to December, 2019 at a college in Daegu. MSDT was positive influenced on the improvement of Empathic Imagine, Empathic interest, Empathic awakening of the FPHA' EPSA. In the analysis of key common words, the use of neutral and negative words was low, while the use of positive words was high. In order to systematically equip Empathic problem solving job competency in the age of artificial intelligence, it is meaningful to develop a program for the freshmen curriculum and to conduct a analysis of the structured and unstructured data to verify its effectiveness. Additional program development research is needed for the application of theoretical subjects.

• Journal of Intelligence and Information Systems
• /
• v.11 no.1
• /
• pp.191-205
• /
• 2005

This paper presents a novel product recommender system as a tool fur differentiated marketing service of online shopping malls. Ihe proposed model uses genetic algorithnt one of popular global optimization techniques, to construct a personalized product recommender systen The genetic algorinun may be useful to recommendation engine in product recommender system because it produces optimal or near-optimal recommendation rules using the customer profile and transaction data. In this study, we develop a prototype of WeLbased personalized product recommender system using the recommendation rules fi:om the genetic algorithnL In addition, this study evaluates usefulness of the proposed model through the test fur user satisfaction in real world.

• The Korean Journal of Applied Statistics
• /
• v.21 no.2
• /
• pp.211-219
• /
• 2008

Customer Loyalty is very important for a company to be survived and to make profit for a long time. Especially, since the credit card company has to manage proper card members and merchants, the CRM(Customer Relationship Management) is much emphasized. A loyalty score is more essential to credit card companies which provide differential financial services based on card members and merchants than any other companies. In this paper, we discuss behavioral measures to define customer loyalty and suggest a method to make loyalty score with an example of a credit card company. The loyalty score developed is considered easy to understand and simple to apply in card industry. In the development of loyalty score, first, we define the loyal customers and non-loyal customers by measuring variables indicating loyalty. And we perform individual logistic regression by each exploratory measuring variable and obtain the weight of measure variables using Chi-square statistics which is used for model fitness. The loyalty score suggested shows very stable results in terms of PSI (Population Stability Index) as time goes.

• Journal of the Korea Computer Industry Society
• /
• v.2 no.8
• /
• pp.1109-1116
• /
• 2001

This paper describes a mechanism and its aspects for monitoring user actions and behaviors on the web operated under the layered architecture that utilizes intelligent agents, and by which personalized information services, for instance one-to-one marketing, is easily facilitated and developed. Intelligent agent is one of techniques which enables to automate the whole process for providing personalized services that includes monitoring, logging a user actions and analyzing behaviors based on his or her profiles, and then selecting, organizing customizable contents which are at last delivered to the users browser or mail box also by intelligent agents. Monitoring technologies can utilize to help better construct the process in providing personalized services in that those services require intelligence in operating. As a result, implementing effective personalized services, however, depends on how well to define various users interests and needs, and how correctly to detect and log the actions when hey are done by users.

• Journal of Intelligence and Information Systems
• /
• v.7 no.1
• /
• pp.149-159
• /
• 2001

There have been lots of researches for providing a personalized service to a customer using one-to-one marketing and collaborative filtering techniques in E-Commerce. However, there are technical difficulties for providing the recommendation of products far users, which often involve high complexity of computation. In this paper, we have presented an integrated method of classification problem solving method and constraint based configuration techniques. This method can reduce a complexity of computation by classifying a solution domain space that has a higher complexity of composition. Thereafter, we have modeled customers constraints and the components of products to configure a complete system by passing it to constraint processing module in Constraint Satisfaction Problems. Constraint-based configuration uses the constraint propagation using the constraints of buyers and the constraints among PC components to configure a proper product for a customer. We have transformed and applied vector space modeling method in the field of information retrieval to consider a customer satisfaction in addition to the CSP. Finally, we have applied our system to test data fur evaluating a customers satisfaction and performance of the proposed system.

• Journal of Intelligence and Information Systems
• /
• v.7 no.2
• /
• pp.35-49
• /
• 2001

Over the years, cluster analysis has become a popular tool for marketing and segmentation researchers. There are various methods for cluster analysis. Among them, K-means partitioning cluster analysis is the most popular segmentation method. However, because the cluster analysis is very sensitive to the initial configurations of the data set at hand, it becomes an important issue to select an appropriate starting configuration that is comparable with the clustering of the whole data so as to improve the reliability of the clustering results. Many programs for K-mean cluster analysis employ various methods to choose the initial seeds and compute the centroids of clusters. In this paper, we suggest a methodology to evaluate various clustering programs. Furthermore, to explore the usability of the methodology, we evaluate four clustering programs by using the methodology.

• Journal of Intelligence and Information Systems
• /
• v.15 no.4
• /
• pp.159-182
• /
• 2009

We construct product networks from the retail transaction dataset of an off-line department store. In the product networks, nodes are products, and an edge connecting two products represents the existence of co-purchases by a customer. We measure the quantities frequently used for characterizing network structures, such as the degree centrality, the closeness centrality, the betweenness centrality and the centralization. Using the quantities, gender, age, seasonal, and regional differences of the product networks were analyzed and network characteristics of each product category containing each product node were derived. Lastly, we analyze the correlations among the three centrality quantities and draw a marketing strategy for the cross-selling.

• The Korean Journal of Applied Statistics
• /
• v.21 no.5
• /
• pp.791-799
• /
• 2008

Ansim Flat DATA Plan is a DATA additional service product related to DATA call in a mobile telephone company A. Up to now, the company A is selling it by outbound TM after targeting customers which used data within specific price band. In this paper, we propose a targeting method applying score model combining response rate and retention rate by data mining. The suggested target model is to find customers more likely not only to respond to outbound TM but also to retain Ansim Flat DATA Plan. The proposed targeting method is expected to improve both from 23.7% to 38.8% in the response rate and from 53.2% to 61.4% in the retention rate.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.47 no.1
• /
• pp.73-79
• /
• 2010

Emotion intelligence computing is able to processing of human emotion through it's studying and adaptation. Also, Be able more efficient to interaction of human and computer. As sight and hearing, music & image is constitute of short time and continue for long. Cause to success marketing, understand-translate of humanity emotion. In this paper, Be design of check system that matched music and image by user emotion keyword(irritability, gloom, calmness, joy). Suggested system is definition by 4 stage situations. Then, Using music & image and emotion ontology to retrieval normalized music & image. Also, A sampling of image peculiarity information and similarity measurement is able to get wanted result. At the same time, Matched on one space through pared correspondence analysis and factor analysis for classify image emotion recognition information. Experimentation findings, Suggest system was show 82.4% matching rate about 4 stage emotion condition.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.1
• /
• pp.221-228
• /
• 2018

Big data is an important keyword in World's Fourth Industrial Revolution in public and private division including IoT(Internet of Things), AI(Artificial Intelligence) and Cloud system in the fields of science, technology, industry and society. Big data based on services are available in various fields such as transportation, weather, medical care, and marketing. In particular, in the field of sports, various types of bio-signals can be collected and managed by the appearance of a wearable device that can measure vital signs in training or rehabilitation for daily life rather than a hospital or a rehabilitation center. However, research on big data with vital signs from wearable devices for training and rehabilitation for baseball players have not yet been stimulated. Therefore, in this paper, we propose a system for baseball infield and outfield players, especially which can store and analyze the momentum measurement vital signals based on big data.