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Building Data Mining Applications for CRMDecember 1999
Publisher:
  • McGraw-Hill Professional
ISBN:978-0-07-134444-9
Published:01 December 1999
Pages:
510
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Abstract

From the Publisher:

How data mining delivers a powerful competitive advantage!

Are you fully harnessing the power of information to support management and marketing decisions__ __ You will,with this one-stop guide to choosing the right tools and technologies for a state-of-the-art data management strategy built on a Customer Relationship Management (CRM) framework.

Authors Alex Berson,Stephen Smith,and Kurt Thearling help you understand the principles of data warehousing and data mining systems,and carefully spell out techniques for applying them so that your business gets the biggest pay-off possible.

Find out about Online Analytical Processing (OLAP) tools that quickly navigate within your collected data. Explore privacy and legal issues. . . evaluate current data mining application packages. . . and let real-world examples show you how data mining can impact — and improve — all of your key business processes. Start uncovering your best prospects and offering them the products they really want (not what you think they want)!

How data mining delivers a powerful competitive advantage!

Are you fully harnessing the power of information to support management and marketing decisions__ __

You will,with this one-stop guide to choosing the right tools and technologies for a state-of-the-art data management strategy built on a Customer Relationship Management (CRM) framework. Authors Alex Berson,Stephen Smith,and Kurt Thearling help you understand the principles of data warehousing and data mining systems,and carefully spell out techniques for applying them so that your business gets the biggest pay-off possible.

Find out about Online Analytical Processing (OLAP) tools thatquickly navigate within your collected data. Explore privacy and legal issues. . . evaluate current data mining application packages. . . and let real-world examples show you how data mining can impact — and improve — all of your key business processes. Start uncovering your best prospects and offering them the products they really want (not what you think they want)!

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  3. Singh A (2017). Mining of Social Media data of University students, Education and Information Technologies, 22:4, (1515-1526), Online publication date: 1-Jul-2017.
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  6. Ahn H, Ahn J, Byun H and Oh K (2011). A novel customer scoring model to encourage the use of mobile value added services, Expert Systems with Applications: An International Journal, 38:9, (11693-11700), Online publication date: 1-Sep-2011.
  7. Rahman S, Siddiky F and Shrestha U (2011). A statistical data mining approach in bacteriology for bacterial identification, International Journal of Data Analysis Techniques and Strategies, 3:2, (117-142), Online publication date: 1-Apr-2011.
  8. Bresfelean V Data mining and model trees study on GDP and its influence factors Proceedings of the 11th WSEAS international conference on Applied informatics and communications, and Proceedings of the 4th WSEAS International conference on Biomedical electronics and biomedical informatics, and Proceedings of the international conference on Computational engineering in systems applications, (401-406)
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  10. Tsai C and Chen M (2010). Variable selection by association rules for customer churn prediction of multimedia on demand, Expert Systems with Applications: An International Journal, 37:3, (2006-2015), Online publication date: 1-Mar-2010.
  11. Liang Y (2010). Integration of data mining technologies to analyze customer value for the automotive maintenance industry, Expert Systems with Applications: An International Journal, 37:12, (7489-7496), Online publication date: 1-Dec-2010.
  12. Ranjan J and Bhatnagar V (2010). Information security-enabled business process architecture for mobile CRM: the role of technology, planning, training and process, International Journal of Networking and Virtual Organisations, 7:5, (452-464), Online publication date: 1-Aug-2010.
  13. Chen R, Chen Y and Chen C Using data mining technology to deign an quality control system for manufacturing industry Proceedings of the European conference of systems, and European conference of circuits technology and devices, and European conference of communications, and European conference on Computer science, (272-276)
  14. Ngai E, Xiu L and Chau D (2009). Review, Expert Systems with Applications: An International Journal, 36:2, (2592-2602), Online publication date: 1-Mar-2009.
  15. Ren Z Study on building data mining application Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing, (5059-5062)
  16. Yang Q, Yin J, Ling C and Pan R (2007). Extracting Actionable Knowledge from Decision Trees, IEEE Transactions on Knowledge and Data Engineering, 19:1, (43-56), Online publication date: 1-Jan-2007.
  17. Ajroud H and Amdouni H How clustering can be useful to supermarkets? Proceedings of the 5th WSEAS international conference on Telecommunications and informatics, (260-266)
  18. Chalmeta R (2006). Methodology for customer relationship management, Journal of Systems and Software, 79:7, (1015-1024), Online publication date: 1-Jul-2006.
  19. Chen R and Wu R Using data mining technology to design an intelligent quality analysis control system for semiconductor packaging industry Proceedings of the 10th WSEAS international conference on Computers, (185-191)
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  22. Dragut A and Nichitiu C (2004). A Monotonic On-Line Linear Algorithm for Hierarchical Agglomerative Classification, Information Technology and Management, 5:1-2, (111-141), Online publication date: 1-Jan-2004.
  23. Yan L, Wolniewicz R and Dodier R (2004). Predicting Customer Behavior in Telecommunications, IEEE Intelligent Systems, 19:2, (50-58), Online publication date: 1-Mar-2004.
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  27. Bacon L Marketing Handbook of data mining and knowledge discovery, (715-725)
Contributors
  • University of Illinois Urbana-Champaign

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