Sadath Lipsa, (2013), “Data Mining in E-Commerce: A CRM Platform” , International Journal of Computer Applications, Vol. 68, No. 24, PP. 32-37.
Satish Belsare and Sunil Patil, (2012), “Study and Evaluation of user’s behavior in e-commerce Using Data Mining” , Research Journal of Recent Sciences International Science Congress Association, Vol. 1, Issue 2, PP. 375-387.
Sheetal A. Raiyani, Shailendra Jain, Ashwin G. Raiyani, (2012), “Advanced Preprocessing using Distinct User Identification in web log usage data” , International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), Vol. 1, Issue 6, PP. 418-422.
Shinde S. P. ,Kapase S. S. , Mulik S. R. , (2011), “E-Service Intelligence in Web Mining” , International Journal of Enterprise Computing and Business Systems (IJECBS), Vol. 1, Issue 2, PP. 26-41.
Tiwari Sonal, Richariya Prashant, Razdan Deepti, Tomar Shivkumar, (2011), “A Web Usage Mining Framework for Business Intelligence” , ۳rd International Conference on Communication Software and Networks (ICCSN), Bhopal, India ,PP. 731-734.
Tuzhilin Alexander, (2012), “Customer relationship management and Web mining: the next frontier” , Journal of Data Mining and Knowledge Discovery, Vol. 24, Issue 3, PP. 584-612.
Venkata Krishna M. , Raghavendar Raju L. , Jamuna D. , Gayathri M. , (2012), “Analysis of Web Mining and Evolving of User Profiles” , International Journal of Engineering and Innovative Technology (IJEIT), Vol. 1, Issue 4, PP. 206-208.
Weigang Zuo and Qingyi Hua, (2012), “The application of Web data mining in the electronic commerce” , Fifth International Conference on Intelligent Computation Technology and Automation, Hunan, Chine, PP. 337-339.
ABSTRACT
Proposing a New Method for Customer Relationship Management in e-Commerce Using Web Mining Techniques
BY
FARAHNAZ POURSINA
With the development of e-business, e-commerce success depends on the use of methods to attract and build customer loyalty and fulfilling their needs and interests. Web mining, uses data mining techniques to discover useful information from web related data and its application in market analysis and understanding customer behavior. In this study after expressing the importance of web mining techniques in e-commerce and customer relationship management, current methods in this field has been reviewed and a method is proposed to increase customer satisfaction and loyalty, as well as corporate profitability. Customer information is analyzed by data mining techniques to evaluate the proposed method.
KEYWORDS
Data mining, Web mining, e-Commerce, Customer Relationship Management
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- Cross-selling ↑
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- Clustering ↑
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- Classification ↑
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- Association Rules ↑
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- Dimension ↑
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- Measure ↑
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- Support ↑
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- Confidence ↑
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- Distance Function ↑
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- On-Line Analytical Processing (OLAP) ↑
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- Log Files ↑
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- user session ↑
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- Traffic analysis ↑
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- e-Commerce analysis ↑
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- web server ↑
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- web proxy server ↑
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- user client ↑
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- User Identification ↑
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- login ↑
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- cookies ↑
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- e-CRM ↑
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- Adaptive ↑
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- Web Logs ↑
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- Home Page ↑
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- Clementine ↑
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- Classification and Regression ↑
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- Chi-squared Automatic Interaction Detection ↑
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- Quick, Unbiased, Efficient Statistical Tree ↑
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- Generalized Rule Induction ↑
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- Numeric ↑
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- Categorical ↑
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- Target ↑
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- Analysis node ↑
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- Cross Validation ↑