• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.338-345
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• 2023

Nowadays, many companies are striving to turn customer value into business value. Customer Relationship Management is a management system that develops effective and efficient marketing strategies by classifying customers in detail based on their information, i.e. databases, and consists of various information technologies. To implement this management system, a customer integration database must be established, and customer characteristics (buying behavior, preferences, etc.) must be analyzed with the databases established and the behavior of each customer must be predicted. This study aims to systematically manage a large amount of customer data generated by companies that apply Customer Relationship Management, in order to develop data design and data governance strategies that should be considered to increase customer value and even company value. We mainly looked at the characteristics of customer relationship management and data governance, and then explored the link between the field of customer relationship management and data governance. In addition, we have developed a data strategy that companies need to perform data governance for customer relationship management.

• The Journal of Information Systems
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• v.15 no.3
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• pp.87-106
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• 2006

The customer-focused enterprise is interested in integrating every record of an interaction with a customer. This study is to investigate the structural relationship of data integration customer analysis capability, marketing & sales capability, customer service capability, and CRM performance. 205 survey data were collected from the company which implemented the CRM package. SEM analysis shows that data integration has influence on the CRM performance through the improvement of customer analysis capability, marketing 8t sales capability, and customer service capability. The revised model for further goodness-fitting model shows that data integration has influence on the improvement of customer analysis capability, marketing & sales capability, and customer service capability. but customer analysis capability has indirect influence on CRM performance through the improvement of marketing & sales capability, customer service capability.

• Journal of Fashion Business
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• v.6 no.5
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• pp.136-150
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• 2002

In this study, we proposed a method to be standing customers as the supporting system for the improvement of fashion garment industry which was the marginal growth getting into full maturity of market. As for the customer creation method of Fashion garment company is developing a marketing program to be standing customer as customer scoring to estimate a existing customer‘s buying power, and figure out minimum fixed sales of company to use a future purchasing predict. This study was a result of data from total sixty thousands data to be created for the 11 months from september. 2000 to July. 2001. The data is part of which the company leading the Korean fashion garment industry has a lot of a customer purchasing history data. But this study used only 48,845 refined purchased data to discriminate from sixty thousands data and 21,496 customer case with the exception of overlapping purchased data among of those. The software used to handle sixty thousands data was SAS e-miner. As the analysis process is put in to operation the analysis of the purchasing customer’s profile firstly, and the second come into basket analysis to consider the buying associations for Association goods, the third estimate the customer grade of Customer loyalty by 3 ways of logit regression analysis, decision tree, Artificial Neural Network. The result suggested a method to be estimate the customer loyalty as 3 independent variables, 2 coefficients. The 3 independent variables are total purchasing amount, purchasing items per one purchase, payment amount by one purchasing item. The 2 coefficients are royal and normal for customer segmentation. The result was that this model use a logit regression analysis was valid as the method to be estimate the customer loyalty.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.761-774
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• 2008

Agility is vital to real-time enterprises in comtemporary dynamic business environment. This study aims to investigate the relationships between traditional shipping and port logistics firms' customer agility obtained by adopting Internet business, and their corporate image and customer satisfaction. Using questionnaire data, factor analyses were used to figure out five major agility factors, corporate image factor, and customer satisfaction factor. The agility factors were then used to investigate how they improve the firms' corporate image and customer satisfaction. The results of the regression analyses show that agility factors significantly influence the firms' corporate image and customer satisfaction factors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.1
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• pp.26-33
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• 2006

The potential needs as well as visible needs of customer should be considered in order to research and analyze of the customer data. The methods to analyze customer data is classified into customer segmentation, clustering analysis model, forecasting customer response probability model, analysis of the customer break rate model and new customer analysis model by the purpose. In this study, we developed the CW-CLV (Correlation Weight Customer Lifetime Value)method that used AHP(Analytic Hierarchy Process)rule for enhance the reliability of customer data and quantitative analysis of the customer segmentation, based on CLV(Customer Lifetime Value). We suggest to new variables and methodology from determined CW-CLV coefficients, because all of companies respect to the diversified customers classification and complexity of consumers needs. Finally, we unfolded any company's scheduling added new methodology using simulation and leaded conclusion about the new methodology.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.26-32
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• 2020

There are many airline customer evaluation data, but they are insufficient in terms of predicting customer satisfaction in practice. In particular, they are generally insufficient in case of verification of data value and development of a customer satisfaction prediction model based on customer evaluation data. In this paper, airline customer satisfaction analysis is conducted through an experiment of correlation analysis between customer evaluation data provided by Google's Kaggle. The difference in accuracy varied according to the three types, which are the overall variables, the top 4 and top 8 variables with the highest correlation. To build an airline customer satisfaction prediction model, they are applied to three classification algorithms of Random Forest, SVM, DNN and conduct a classification experiment. They are divided into training data and verification data by 7:3. As a result, the DNN model showed the lowest accuracy at 86.4%, while the SVM model at 89% and the Random Forest model at 95.7% showed the highest accuracy and performance.

• Journal of Distribution Science
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• v.14 no.8
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• pp.9-21
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• 2016

Purpose - This research analyzes the impact of motivations to dine out and selected attributes of customers on customer satisfaction and loyalty. Based on collected data, this study aims to suggest effective marketing strategies to help manage traditional Korean food restaurants. Research design, data, and methodology - The data were collected from the customers who visited traditional Korean food restaurants in Jeon-Ju for two months from December, 2015. The available data were 402 from collected 450 customers' data and they were analyzed by using SPSS 19.0. Result - These are the results of data analysis. First, environmental, personal and perceived factors influence on the motivations to dine out at Korean food restaurants which affect customer satisfaction. Next, selected attributes from Korean food restaurants have impacts on customer satisfaction. Third, motivations to dine out Korean food restaurants affect customer loyalty. Moreover, physical environments, curiosity and need satisfactions, which are the selected attributes, have impacts on customer loyalty. Lastly, it has been identified that customer satisfaction in Korean food restaurants influences customer loyalty. Conclusions - Satisfaction and good brand image of Jeon-Ju will increase customers' intention to revisit. This study has found that high customer satisfaction leads to re-visitation.

• Journal of Distribution Science
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• v.16 no.9
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• pp.53-58
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• 2018

Purpose - This study aims to examine how the time variations of customer satisfaction influence retail firms' performance. Research design, data, and methodology - The study employs yearly time series customer satisfaction data of Korean retail secured from the National Customer Satisfaction Index(NCSI) for the 2011~2016 period. Our data includes a total of 90 observations of 15 retail firms in 5 different sector(department store, filling station, large discount store, open market, TV home shopping). We obtained the firm performance data from the KIS Value database. The variables for financial performance include sales and net profit. Results - The results show that customer satisfaction has dynamic effects on retail firms' performance. More specifically, the time variation of customer satisfaction has the moderating effect on the linkage between customer satisfaction and financial performance as well as direct effects on the firms' financial performance. Conclusions - Customer satisfaction has the current effect lasting over time on firm performance and changes of customer satisfaction in positive direction also impact on firm performance. Retail firms need to not only focus on improving customer satisfaction in the current term, but make efforts to continuously enhance customer satisfaction in the long term.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.324-331
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• 2005

In this paper, we propose a new customer profile model based on customer behavior in Internet shopping mall. The proposed technique defines customer profile model based on customer behavior information such as click data, buy data, and interest categories. We also implement CBCPM(Customer Behavior-based Customer Profile Model) and perform extensive experiments. The experimental results show that CBCPM has higher precision, recall, and F1 than the existing customer profile model.