• Journal of Korean Ophthalmic Optics Society
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• v.20 no.4
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• pp.405-414
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• 2015

Purpose: We analyse characteristics of optical shop customer's segmented market by using clustering analysis, and we expect it would be a useful indicator of marketing strategy for optical shops. Methods: Survey was conducted from March 10 to March 31, 2015. The survey asked customers who have visited optical shops in Seoul and Northern Gyeonggi-do regions, and analyzed by utilizing SPSS v.10.0 statistical package program. The analysing methods are frequency analysis, factor analysis about variable of values, clustering analysis for market segmentation, and crosstabs. Results: The market is segmented based on values. In the process of establishing marketing strategy, it is useful to establish strategy by classifying customers into 3 types of cluster; "middle level value oriented cluster", "high level value oriented cluster", "high level value oriented and non-religious cluster". In marketing strategy of progressive lenses, it turned out that the most important strategy is to target self-employed person in "middle level value oriented cluster". Conclusions: As a result of market segmentation by using clustering analysis, it was classified into 3 types of cluster, and we found that most important customer for progressive lenses is self-employed person in "middle level value oriented cluster" who is more than 41 years old.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.206-213
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• 2003

Application of existing decision tree algorithms for Internet retail customer classification is apt to construct a bushy tree due to imprecise source data. Even excessive analysis may not guarantee the effectiveness of the business although the results are derived from fully detailed segments. Thus, it is necessary to determine the appropriate number of segments with a certain level of abstraction. In this study, we developed a stopping rule that considers the total amount of information gained while generating a rule tree. In addition to forwarding from root to intermediate nodes with a certain level of abstraction, the decision tree is investigated by the backtracking pruning method with misclassification loss information.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.309-321
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• 2014

Recently, due to the advent of ubiquitous computing and the spread of intelligent portable device such as smart phone, iPad and PDA has been amplified, a variety of services and the amount of information has also increased fastly. It is becoming a part of our common life style that the demands for enjoying the wireless internet are increasing anytime or anyplace without any restriction of time and place. And also, the demands for e-commerce and many different items on e-commerce and interesting of associated items are increasing. Existing collaborative filtering (CF), explicit method, can not only reflect exact attributes of item, but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, using a implicit method without onerous question and answer to the users, not used user's profile for rating to reduce customers' searching effort to find out the items with high purchasability, it is necessary for us to analyse the segmentation of customer and item based on customer data and purchase history data, which is able to reflect the attributes of the item in order to improve the accuracy of recommendation. We propose the method of recommendation system using association rule and weighted preference so as to consider many different items on e-commerce and to refect the profit/weight/importance of attributed of a item. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• Asia Marketing Journal
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• v.7 no.1
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• pp.21-42
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• 2005

Customer Relationship Management (CRM) is a corporate marketing strategy maintaining and managing customers. And with CRM companies maximize the customer's value through a series of processes of new customer retention, VIP customer retention, customer value increase, potential customer activation, and customers for lifetime by collecting the customer information and taking advantage of it effectively. In particular, as the competitive environment is changing rapidly and getting more intense, maintaining the customer retention through customer churn management becomes more important in order to increase the customer value for maximizing the company's profit and to build up the relationship with customers. For example, the financial industry has managed the customer churn with the concept of customer segmentation. Recently the customer retention and churn management is becoming increasingly important in all business fields as well as financial industry since the companies expect the effect of preventing the customer churn by identifying characteristics of customers. However, despite the increasing interest and importance of the management of the customer churn, not many of studies are systematically executed by analyzing the data of customer churn. In this study we analyze the actual data of CRM activities for the customer retention, specifically the data of TV home-shopping. By doing so, we hope to identify the differences of demographic attributes and transaction specific characteristics in consumer behaviors between the churning customer and the retained customers. In addition, we try to find out the variables which can impact the churning of the customers and to predict the churn rate of individual customer through our proposed model of customer churn. In the end, based on our findings we suggest the possible marketing strategies for TV home-shopping companies.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.193-198
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• 2004

In this paper, we estimate the difference of port preference and attributed importance by diving subjects of the survey into internal and external shipping companies considered as the main customers of port. From the results of conjoint analysis, it is found that there are differences in preference between domestic shipping companies and foreign ones. The difference in port preference shows; foreign shipping companies mark Shanghai port in the first place in the preference of transshipment port, while domestic shipping companies prefer Busan port. Similar results are applied to preference of rolling port. The result of the survey means it is necessary to group shipping company, when port is analyzed, because the port preference is subject to wether internal or external shipping companies. Also, it implies target marketing strategies should continuously be needed in order to maintain Busan port's preference gaining advantage over other ports and major target would be shipping companies.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2003.11a
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• pp.55-65
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• 2003

Since 1990s, despite the emergence of innovative telecommunication services in accordance with technological changes, only few services have attained satisfactory number of subscribers, and create revenues. As with other products or services, the possibility for the success of new telecommunication services is low, thus a systematic business strategy by businesses is required to achieve market success with broadband wireless Internet. For the current broadband wireless Internet to successfully enter the early market with an early growth, a customer oriented market strategy and service provisioning strategy is inevitable. In this study based on the market survey of individuals and business customers, the customer demand and related needs of broadband wireless Internet have been analyzed in depth. Then with the analysis and establishment of the killer applications and market segmentation, market development strategy is proposed.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.531-538
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• 2005

In k-means clustering, we standardize variables before clustering and iterate two steps: units allocation by Euclidean sense and centroids updating. In applications to DB marketing where clusters are to be used as customer segments with similar consumption behaviors, we frequently acquire additional variables on the customers or the units through marketing campaigns a posteriori. Hence we need to modify the clusters originally formed after each campaign. The aim of this study is to propose a revision method of k-means clusters, incorporating added information by weighting clustering variables. We illustrate the proposed method in an empirical case.

• Journal of Distribution Science
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• v.10 no.8
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• pp.25-34
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• 2012

Understanding customer' lifestyles important for banks because it will guide in determining marketing policies, such as services, pricing, service delivery and promotion decisions. From the customer' lifestyle, banks will know what kind of customers' attitudes, interests and opinions, so they also will understand what the costumer' needs and what services needed by them. For Islamic banks, customers understanding are important because, nowadays, the competition of the banks is not only with other Islamic banks but also with the well-established conventional banks offering Islamic products or services The aims of this research paper are to describe what factors underline the customer's lifestyle of both Islamic and conventional bank, to segment the bank customers based on their lifestyles and investigate the profile of each segments, to compare the characteristics of the segments, and to identify marketing policies based on the characteristics. The population of the study is banking customers in Indonesia, in which the researchers have used judgment sampling as sample selection. There were 186 customers of Islamic banks and 244 customers of conventional bank as respondents in this study. Statistical methods employed were exploratory factor analysis and cluster analysis. The finding of the study shows that there are twelve factor underlining the customers' lifestyle, namely: factor of fashion conscious, internet usage, sports spectator, financial and technology optimism, price sensitivity, independent, compulsive housekeeper, new brand tryer community activities, opinion leader, credit usage, and homebody. In addition, for Islamic banking, there are two market segments, namely fashionable-independent and innovative-social segment. Based on the lifestyle characteristics, the first segment has higher level in factor of fashion conscious, homebody, independent, optimism and price conscious, which is therefore called fashionable-independent segment. On the other hand, the second cluster has higher level in factor of new brand tryer, community minded, sport spectator, credit user, internet usage, opinion leader, and compulsive housekeeper, which is therefore called the innovative-social segment. Furthermore, for conventional banking, there are also two segments, namely persuasive-optimistic and sensitive-independent segment. The first segment has higher level on some factors, namely: opinion leader, optimism, internet usage rate, credit usage level, sport spectator, and new brand tryer. On the other hand, the second cluster is characterized by higher level in factor of price conscious, confidence, community minded, homebody, fashion conscious, and compulsive housekeeper. Managerial implications for the management of Islamic banks could be identified in this study as follows. Firstly, the twelve lifestyle factors of this study could be an alternative view in observe Islamic banking customers. The domination of both the fashionable conscious and the internet usage factor show that the aspects are quite instrumental in perceiving the customer' lifestyles, in which reflects the importance of these two aspects to customers. Secondly, in serving their customers, Islamic banks need to understand the customer lifestyle, in which the lifestyle segments found in this study provide a guide of how their needs were reflected. Finally, by understanding the segments and the characteristics each segment of the conventional banks, Islamic banks could adjust their marketing strategies differently from the conventional banks.

• Culinary science and hospitality research
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• v.24 no.2
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• pp.112-118
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• 2018

The purpose of this study is to develop and test a model that explains the effect of experiential marketing factors on: 1) brand image, 2) satisfaction, and 3) loyalty in context of restaurants located in complex shopping mall. In addition, the study clarified how these variables relate to each other. Survey were distributed to customers who have visiting experience(s) in a restaurants in complex shopping mall. Total 360 participants were distributed and 344 questionnaires were used for analysing. The confirmatory factor analysis and structural equation modeling(SEM) have been employed research methods for frequency analysis, reliability analysis and measurement model validation. The findings of this study identified that relation factor of experiential marketing elements was only significant factor on brand image Furthermore, sense and recognition were critical components of customer satisfaction. Last, present study also identified the significant relationship between satisfaction and customer loyalty. These findings may contribute to provide valuable marketing strategic for this business segmentation, and it can be utilized as a fundamental study to establish an efficient business plan to increase revenue especially for restaurant business located in complex shopping mall.