• Journal of Global Scholars of Marketing Science
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• v.14
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• pp.125-144
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• 2004

The purpose of this study is to classify customer bye-mailing responsiveness on time-series analysis and RFM module and testify the effectiveness of grouping by ROI analysis. RFM (Recency, Frequency, Monetary Value) analysis are used for customer classification that is fundamental process of e-CRM application. ROI analysis were consisted of open, click-through, duration time, conversion rate, personalization and e-mail loyalty index. Major findings are as follows; Customer segmentation were loyal customer, odds customer, dormant customer, secession customer and observation customer by Activity email module. And Loyal, dormant and secession customer are segregated by RFM module. Loyal customer group have higher point of all ROI index than other groups. These results indicated that customer responsiveness of e-mailing and RFM analysis were appropriate methods to grouping the customer. Mid-small Internet Biz adapted marketing strategy by optimization of consumer information.

• Journal of Korea Multimedia Society
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• v.9 no.9
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• pp.1173-1183
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• 2006

Matrix-star, Pancake, and RFM graphs have such a good property of Star graph and a lower network cost than Hypercube. Matrix-star graph has Star graph as a basic module and the node symmetry, the maximum fault tolerance, and the hierarchical decomposition property. Also it is an interconnection network that improves the network cost against Star graph. In this paper, we propose a method to embed among Matrix-star Pancake, and RFM graphs using the edge definition of graphs. We prove that Matrix-star $MS_{2,n}$ can be embedded into Pancake $P_{2n}$ with dilation 4, expansion 1, and $RFM_{n}$ graphs can be embedded into Pancake $P_{n}$ with dilation 2. Also, we show that Matrix-star $MS_{2,n}$ can be embedded into the $RFM_{2n}$ with average dilation 3.

• Aerospace Engineering and Technology
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• v.4 no.1
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• pp.229-238
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• 2005

The main objective of this study is to develop RPC geometric correction module for the pre-processing systems of the satellite image. For this purpose, the Terrain-Independent Ⅰ, Terrain-Independent Ⅱ and Terrain-Dependent Ⅲ have been applied in tests with KOMPSAT-1 EOC and SPOT PAN images.