Advertisement

Marketing Intelligent System for Customer Segmentation

  • Brano Markic
  • Drazena Tomic
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 258)

Abstract

Marketing intelligent system consists of people, procedures, software, databases, and devices that are used in problem-specific decision-making and problem-solving. Marketing intelligent system is an interdisciplinary field that relates to databases, data warehouse, machine learning, expert systems (formalisms of knowledge representation), statistics and operational research and data visualization. The common goal of integrating these different fields is extracting knowledge from data stored in large databases and data warehouses.

Marketing intelligent system uses sophisticated software for satisfaction manager’s quires. Software is designated so that its use is relatively simple. Top manager can very quickly receive the essential and key information about the basic economic indicators. Long running education of managers for implementation of marketing intelligent system is unnecessary. Information is short, condensed and visualized.

Marketing intelligent system for customers’ segmentation performs useful tasks for marketing researches. They will make marketing researchers more productive allowing doing more work in less time and creating interesting information for marketing decision making. They comprise enough knowledge to react quickly.

In the paper is analyzing and building marketing intelligent system for customers segmentation based on crisp and fuzzy set clustering. Fuzzy logic is a well proven and well established logic of degrees and provides a framework for dealing quantitatively and logically with vague concepts. In fuzzy logic a data point’s membership in a set is not crisp (crisp means either in or out of the set) but can be specified as a degree of membership. Fuzzy logic has a wide range of applicability (in clustering, machine learning, expert system, neural networks and decision trees). Marketing intelligent system built in the paper uses fuzzy clustering algorithm and assigns a set of multiple clusters with varying degrees of membership, unlike conventional cluster analysis that assigns a value to a single cluster. Data for customers clustering are stored in relational data warehouse that is temporarily loaded from transactional data bases.

Keywords

marketing intelligent system fuzzy c-means clustering market segmentation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algoritms. Plenum Press, New York (1981)Google Scholar
  2. 2.
    Buttle, F.: Customer Relationship management, Concepts and Technologies, 2nd edn. Elsevier, Amsterdam (2009)Google Scholar
  3. 3.
    Chiu, S., Domingo, T.: Data Mining and Market Intelligence for Optimal Marketing Returns, Jordan Hill, Oxford OX28DP, Elsevier (2008)Google Scholar
  4. 4.
    Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1(2), 224–227 (1979)CrossRefGoogle Scholar
  5. 5.
    Dunn, J.C.: A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics 3, 32–57 (1973)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, S., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. MIT Press, Cambridge (1996)Google Scholar
  7. 7.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufman, San Francisco (2000)Google Scholar
  8. 8.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2001)zbMATHGoogle Scholar
  9. 9.
    Bezdek, J.C.: Fuzzy Mathemathics in Pattern Classification. PhD Thesis, Applied Math. Center, Cornell University, Ithaca (1973)Google Scholar
  10. 10.
    Kantardžić, M.: Data Mining: Concepts, Models, Methods, and Algorithms. Wiley-Interscience, Piscataway (2003)zbMATHGoogle Scholar
  11. 11.
    Kimball, R.: The data warehouse toolkit. Wiley, Chichester (1998)Google Scholar
  12. 12.
    Looney, C.G.: Interactive clustering and merging with a new fuzzy expected value. Pattern Recognition Lett. 35, 187–197 (2002)Google Scholar
  13. 13.
    Markić, B., Tomić, D.: Executive information system for customers clustering, Društvo i tehnologija, Međunarodni simpozij, Hrvatska, Zadar (2005)Google Scholar
  14. 14.
    Markić, B., Tomić, D.: Integrating cluster algorithms and expert systems. In: The 8th International Conference Modern Technologies in Manufacturing, Technical University of Cluj-Napoca, Romania (October 2005)Google Scholar
  15. 15.
    Markić, B., Tomić, D.: Software solutions in marketing research for knowledge discovery in databases by fuzzy clustering. Informatologija, Zagreb 39(4), 240–244 (2006)Google Scholar
  16. 16.
    Markić, B., Tomić, D.: Building software agents for market segementation, Baden Baden (2006)Google Scholar
  17. 17.
    Joel, M.: Murach’s Visual Basic 2008. Mike Murach & Associates, Inc. (2008)Google Scholar
  18. 18.
    Joel, M.: Murach’s SQL Server 2005 for Developers. Mike Murach & Associates, Inc. (2008)Google Scholar
  19. 19.
    Rainer, M.: Intelligent Information Systems (SS 2009), www.informatik.uni-bonn.de/~manthey/IIS09/
  20. 20.
    Shuliang, L., Barry, D., Edwards, J., Kinman, R., Duan, Y.: Integrating group Delphi, fuzzy logic and expert systems for marketing strategy development: the hybridisation and its effectiveness. Journal: Marketing Intelligence & Planning 20(5), 273–284 (2002)CrossRefGoogle Scholar
  21. 21.
    Ryu, T.-W., Eick, C.F.: A Database Clustering Methodology and Tool, Department of Computer Science University of Houston, Information Science in Spring (2005)Google Scholar
  22. 22.
    Teknomo, K.: K-Means Clustering Tutorials, http://people.revoledu.com/kardi/tutorial/kMean
  23. 23.
    Turban, E., Aronson, J.E., Liang, T.-P.: Decision Support Systems and Intelligent Systems, 7th edn. Pearson, Prentice Hall (2005)Google Scholar
  24. 24.
    Witten, I.H., Frank, E.: Data Mining. Academic Press, London (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Brano Markic
    • 1
  • Drazena Tomic
    • 1
  1. 1.Faculty of EconomicsUniversity of Mostar, Bosnia and Herzegovina 

Personalised recommendations