The Experts below are selected from a list of 11832 Experts worldwide ranked by ideXlab platform
Keun Ho Ryu - One of the best experts on this subject based on the ideXlab platform.
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SOM Clustering Method Using User’s Features to Classify Profitable Customer for Recommender Service in u-Commerce
2015Co-Authors: Young Sung Cho, Song Chul Moon, Keun Ho RyuAbstract:This paper proposes an SOM clustering method using user’s features to classify Profitable Customer for recommender service in e-Commerce. In this paper, it is necessary for us to classify Profitable Customer with RFM (Recency, Frequency, and Monetary) score, to use the purchase data to join the Customers using SOM with input vectors of different features, RFM factors in order to do the recommending services in u-commerce, to reduce Customers’ search effort for finding items, and to improve the rate of accuracy. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.
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som clustering method using user s features to classify Profitable Customer for recommender service in u commerce
2015Co-Authors: Young Sung Cho, Song Chul Moon, Keun Ho RyuAbstract:This paper proposes an SOM clustering method using user’s features to classify Profitable Customer for recommender service in e-Commerce. In this paper, it is necessary for us to classify Profitable Customer with RFM (Recency, Frequency, and Monetary) score, to use the purchase data to join the Customers using SOM with input vectors of different features, RFM factors in order to do the recommending services in u-commerce, to reduce Customers’ search effort for finding items, and to improve the rate of accuracy. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.
Øyvind Helgesen - One of the best experts on this subject based on the ideXlab platform.
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ARE LOYAL CustomerS Profitable? Customer SATISFACTION, Customer (ACTION) LOYALTY AND Customer PROFITABILITY AT THE INDIVIDUAL LEVEL
Journal of Marketing Management, 2006Co-Authors: Øyvind HelgesenAbstract:Customer loyalty is supposed to be positively related to profitability. The link between satisfaction, loyalty and profitability is perceived to be so self-evident that the relationship often is taken for granted. Nevertheless, only a few studies have examined this fundamental relationship. Here the focus is on the individual Customer with respect to the links between Customer satisfaction, Customer (action) loyalty and Customer profitability. The following hypotheses are tested; H1: The more satisfied a Customer tends to be, the higher is the loyalty of the Customer; H2: The more loyal a Customer tends to be, the higher Customer profitability is obtained. As expected, the results provide strong support for the hypotheses. However, the relationships between the variables seem to be non-linear (increasingly downward sloping), and only valid beyond certain levels or thresholds. Besides, the explanatory powers of the individual variables are rather low.
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Are Loyal Customers Profitable? Customer Satisfaction, Customer (Action) Loyalty and Customer Profitability at the Individual Level
Journal of Marketing Management, 2006Co-Authors: Øyvind HelgesenAbstract:Customer satisfaction is supposed to be positively related to profitability. This conception may be called “the paradigm of Customer satisfaction”. Nevertheless, only a few studies have examined this fundamental relationship. Thus, evidence for this “much talked about relationship” is questioned. In this working paper the focus is on the individual Customer with respect to the relationships between Customer satisfaction, Customer loyalty and Customer profitability at the Customer level. The following hypotheses are tested; H1 : The more satisfied a Customer tends to be, the higher is the loyalty of the Customer; H2 : The more loyal a Customer tends to be, the higher Customer profitability is obtained; H3: The more satisfied and loyal a Customer tends to be, the higher is the obtained Customer profitability. As expected, the findings (results) provide strong support for the three hypotheses. However, the relationships between the variables seem to be non-linear (increasingly downward sloping), and only valid beyond certain levels or thresholds. As long as Customer satisfaction is not achieved without costs, the findings suggest that an optimal level of Customer satisfaction may be estimated
Young Sung Cho - One of the best experts on this subject based on the ideXlab platform.
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SOM Clustering Method Using User’s Features to Classify Profitable Customer for Recommender Service in u-Commerce
2015Co-Authors: Young Sung Cho, Song Chul Moon, Keun Ho RyuAbstract:This paper proposes an SOM clustering method using user’s features to classify Profitable Customer for recommender service in e-Commerce. In this paper, it is necessary for us to classify Profitable Customer with RFM (Recency, Frequency, and Monetary) score, to use the purchase data to join the Customers using SOM with input vectors of different features, RFM factors in order to do the recommending services in u-commerce, to reduce Customers’ search effort for finding items, and to improve the rate of accuracy. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.
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som clustering method using user s features to classify Profitable Customer for recommender service in u commerce
2015Co-Authors: Young Sung Cho, Song Chul Moon, Keun Ho RyuAbstract:This paper proposes an SOM clustering method using user’s features to classify Profitable Customer for recommender service in e-Commerce. In this paper, it is necessary for us to classify Profitable Customer with RFM (Recency, Frequency, and Monetary) score, to use the purchase data to join the Customers using SOM with input vectors of different features, RFM factors in order to do the recommending services in u-commerce, to reduce Customers’ search effort for finding items, and to improve the rate of accuracy. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.
Sang Chan Park - One of the best experts on this subject based on the ideXlab platform.
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intelligent Profitable Customers segmentation system based on business intelligence tools
Expert Systems With Applications, 2005Co-Authors: Jang Hee Lee, Sang Chan ParkAbstract:For the success of CRM, it is important to target the most Profitable Customers of a company. Many CRM researches have been performed to calculate Customer profitability and develop a comprehensive model of it. Most of them, however, had some limitations and accordingly the Customer segmentation based on the Customer profitability model is still underutilized. This paper aims at providing an easy, efficient and more practical alternative approach based on the Customer satisfaction survey for the Profitable Customers segmentation. We present a multi-agent-based system, called the survey-based Profitable Customers segmentation system that executes the Customer satisfaction survey and conducts the mining of Customer satisfaction survey, socio-demographic and accounting database through the integrated uses of business intelligence tools such as DEA (Data Envelopment Analysis), Self-Organizing Map (SOM) neural network and C4.5 for the Profitable Customers segmentation. A case study on a Motor company's Profitable Customer segmentation is illustrated.
Vishal Bhatnagar - One of the best experts on this subject based on the ideXlab platform.
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Application of data mining techniques in the financial sector for Profitable Customer relationship management
International Journal of Information and Communication Technology, 2010Co-Authors: Jayanti Ranjan, Vishal BhatnagarAbstract:The paper presents the benefits of applying data mining (DM) techniques in Customer relationship management (CRM) of the financial sectors like banking, forecasting stock market, currency exchange rate and bank bankruptcies. The competition in the financial sector of the business is growing and the firms find it very difficult to sustain the ever-changing behaviour of the Customer. To sustain in the competitive world, firms are taking the advantage of the CRM, the new emerging concept in the business world. DM is helping the firms to achieve Profitable and efficient CRM by providing them with advance techniques to analyse the already existing data in the databases of the firms using the complex modelling algorithms The paper demonstrates the ability of the data mining to automate the process of searching the mountain of Customer's related data to find patterns that are good predictors of the behaviours of the Customer which help achieve successful CRM. The paper gives an idea of how data mining capabilities can provide the increased Customer retention and minimises the risk involved in the financial sectors to achieve competitive advantage and concludes by providing the limitations and opportunities in this field.
