Advertisement

Collective Intelligence in Marketing

  • Tilmann Bruckhaus
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 258)

Abstract

As marketing professionals communicate value and manage customer relationships, they must target changing markets, and personalize offers to individual customers. With the recent adoption of large-scale, Internet-based information systems, marketing professionals now face large volumes of complex data, including detailed purchase and service transactions, social network links, click streams, blogs, comments and inquiries. While traditional marketing methodologies struggled to produce actionable insights from such information quickly, emerging collective intelligence techniques enable marketing professionals to understand and act on the observed behaviors, preferences and ideas of groups of people. Marketing professionals apply collective intelligence technology to create behavioral models and apply them for targeting and personalization. As they analyze preferences, match products to customers, discover groups of similar consumers, and construct pricing models, they generate significant competitive advantage. In this chapter, we highlight publications of interest, describe analytic processes, review techniques, and present a case study of matching products to customers.

Keywords

Data Mining Customer Relationship Management Collective Intelligence Data Mining Algorithm Customer Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, C.: The Long Tail: Why the Future of Business Is Selling Less of More. Hyperion (2006)Google Scholar
  2. Ayres, I.: Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart. Bantam Dell (2007)Google Scholar
  3. Baker, S., Leak, B.: Math Will Rock Your World. Business Week, 54–62 (January 23, 2006)Google Scholar
  4. Berry, M., Linoff, G.: Mastering Data Mining: The Art and Science of Customer Relationship Management. John Wiley & Sons, Chichester (1999)Google Scholar
  5. Basili, V.R., Caldiera, G., Rombach, H.D.: Goal Question Metric Paradigm. In: Marciniak, J.J. (ed.) Encyclopedia of Software Engineering, pp. 528–532. John Wiley & Sons, Chichester (1994)Google Scholar
  6. Bruckhaus, T.: The Business Impact of Predictive Analytics. In: Zhu, Q., Davidson, I. (eds.) Knowledge Discovery and Data Mining: Challenges and Realities with Real World Data. Idea Group Publishing, USA (2007)Google Scholar
  7. Caruana, R., Niculescu-Mizil, A.: Data mining in metric space: an empirical analysis of supervised learning performance criteria. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 69–78. ACM, New York (2004)CrossRefGoogle Scholar
  8. Caruana, R., Niculescu-Mizil, A.: An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd international conference on Machine learning, pp. 161–168. ACM, New York (2006)CrossRefGoogle Scholar
  9. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: CRISP-DM 1.0: Cross Industry Standard Process for Data Mining CRISP-DM Consortium (2000)Google Scholar
  10. Chawla, N.V., Japkowicz, N., Kolcz, A. (eds.): Special Issue on Learning from Imbalanced Datasets. SIGKDD, 6(1) (2004)Google Scholar
  11. Davenport, T.H., Harris, J.: Competing on analytics: the new science of winning. Harvard Business School Press, Boston (2007)Google Scholar
  12. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. The Morgan Kaufman Series in Data Management Systems. Morgan Kaufman, San Francisco (2005)Google Scholar
  13. Levitt, S.D., Dubner, S.J.: Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. William Morrow (2005)Google Scholar
  14. Lewis, M.: Moneyball: The Art of Winning an Unfair Game. WW Norton & Company, New York (2003)Google Scholar
  15. McClave, J.T., Benson, P.G., Sincich, T.: Statistics for Business and Economics: International Edition. Pearson Prentice Hall, London (2008)Google Scholar
  16. Mitchell, T.: Machine Learning, 1st edn. McGraw Hill, New York (1997)zbMATHGoogle Scholar
  17. Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  18. Ranadive, V.: The Power to Predict: How Real Time Businesses Anticipate Customer Needs, Create Opportunities and Beat the Competition. McGraw-Hill Professional, New York (2006)Google Scholar
  19. Segaran, T.: Programming Collective Intelligence: Building Smart Web 2.0. Applications. O’Reilly, Sebastopol (2007)Google Scholar
  20. Soukup, T., Davidson, I.: Visual data mining: Techniques and tools for data visualization and mining. Wiley & Sons, Chichester (2002)Google Scholar
  21. Surowiecki, J.: The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations. Doubleday (2004)Google Scholar
  22. Tancer, B.: Click: What Millions of People Are Doing Online and Why It Matters. Hyperion (2008)Google Scholar
  23. Witten, I.H., Frank, E.: Data mining: practical machine learning tools and techniques with Java implementations, 2nd edn. Morgan Kaufman, San Francisco (2005)Google Scholar
  24. Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S.: Top 10 algorithms in data mining. In: Knowledge and Information Systems, vol. 14(1), pp. 1–37. Springer, Heidelberg (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tilmann Bruckhaus
    • 1
  1. 1.eBay Inc. 

Personalised recommendations