Data Mining for Targeted Marketing

Targeted marketing is a new business model of interactive one-to-one communication between marketer and customer. There is great potential for data mining to make useful contributions to the marketing discipline for business intelligence. This chapter provides an overview of the recent development in data mining applications for targeted marketing.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic €32.70 /Month

Buy Now

Price includes VAT (France)

eBook EUR 117.69 Price includes VAT (France)

Softcover Book EUR 158.24 Price includes VAT (France)

Hardcover Book EUR 158.24 Price includes VAT (France)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Preview

Similar content being viewed by others

Machine Learning and Data Mining Use Cases in the Development of Marketing Strategies

Chapter © 2023

An Overview of Data Mining and Marketing

Chapter © 2015

Special Session: Big Data Analytics for Marketing (Contributed Session by the IÉSEG Center for Marketing Analytics (ICMA))

Chapter © 2017

References

  1. R. Agrawal, T. Imielinski, A. Swami: Mining Association Rules Between Sets of Items in Large Aatabases. Proc. ACM SIGMOD International Conference on the Management of Data (ACM Press, 1993 ) pp. 207–216 Google Scholar
  2. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, A.I. Verkamo: Fast Discovery of Association Rules, Advances in Knowledge Discovery and Data Mining (MIT Press, 1996 ) pp. 307–328 Google Scholar
  3. L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone: Classification and Regression Trees (Wadsworth, 1984 ) Google Scholar
  4. David Shepard Associates: The New Direct Marketing (McGraw-Hill, 1999) Google Scholar
  5. F. Berman: From TeraGrid to Knowledge Grid, CACM, 44, 27–28 (2001) Google Scholar
  6. M. Cannataro and D. Talia: The Knowledge Grid, CA CM, 46, 89–93 (2003) Google Scholar
  7. G. Dong and J. Li: Efficient Mining of Emerging Patterns: Discovering Trends and Differences. Proc. 5th ACM SIGKDD International Conference on Knowl-edge Discovery and Data Mining (KDD-99) (ACM Press, 1999 ) pp. 43–52 Google Scholar
  8. P.C. Fishburn: Seven Independence Concepts and Continuous Multiattribute Utility Functions. Journal of Mathematical Psychology, 11, 294–327 (1974) ArticleMathSciNetMATHGoogle Scholar
  9. I. Foster and C. Kesselman: The Grid: Blueprint for a New Computing Infras-tructure (Morgan Kaufmann, 1999 ) Google Scholar
  10. I. Foster and C. Kesselman: The Grid 2: Blueprint for a New Computing Infrastructure (Morgan Kaufmann, 2004 ) Google Scholar
  11. J. Han, Y. Cai, N. Cercone: Data-Driven Discovery of Quantitative Rules in Relational Databases. IEEE Transaction on Knowledge and Data Engineering, 5, 29–40 (1993) ArticleGoogle Scholar
  12. J. Han, M. Kamber: Data Mining: Concepts and Techniques (Morgan Kaufmann, 2001 ) Google Scholar
  13. S.Y. Hwang, E.P. Lim, J.H. Wang, J. Srivastava: Proc. PAKDD 2002 Workshop on Mining Data across Multiple Customer Touchpoints for CRM (2002) Google Scholar
  14. J.J. Jonker, P.H. Franses, N. Piersma: Evaluating Direct Marketing Campaigns; Recent Findings and Future Research Topics. Erasmus Research Institute of Management (ERIM), Erasmus University Rotterdam in Its Series Discussion Paper with Number 166 (2002) Google Scholar
  15. W. Klosgen, J.M. Zytkow: Handbook of Data Mining and Knowledge Discovery (Oxford University Press, 2002 ) Google Scholar
  16. S. Kullback and R.A. Leibler: On Information and Sufficiency. Annals of Mathematical Statistics, 22, 79–86 (1951) ArticleMathSciNetMATHGoogle Scholar
  17. D.B. Leake: Case-Based Reasoning (AAAI Press, 1996) Google Scholar
  18. C.X. Ling, C. Li: Data Mining for Direct Marketing: Problems and Solutions. Proc. 4th International Conference on Knowlege Discovery and Data Mining (KDD’98) (AAAI Press, 1998 ) pp. 73–79 Google Scholar
  19. J. Nabrzyski, J.M. Schopf, J. Weglarz: Grid Resource Management (Kluwer, 2004 ) Google Scholar
  20. Z. Pawlak: Rough Sets, Theoretical Aspects of Reasoning about Data (Kluwer, 1991 ) Google Scholar
  21. P. Van Der Putten: Data Mining in Direct Marketing Databases, W. Baets (ed.) Complexity and Management: A Collection of Essays (World Scientific, 1999 ) Google Scholar
  22. R. Potharst, U. Kaymak, W. Pijls: Neural Networks for Target Selection in Direct Marketing, In K.A. Smith, J.N.D. Gupta (eds.) Networks in Business: Techniques and Applications (Idea Group Publishing, 2001 ) Google Scholar
  23. J.R. Quinlan: Programs for Machine Learning (Morgan Kaufmann, 1993)130 N. Zhong et al. Google Scholar
  24. C.R. Rao: Diversity and Dissimilarity Coefficients: a Unified Approach. Theoretical Population Biology, 21, 24–43 (1982) ArticleMathSciNetMATHGoogle Scholar
  25. B. Ratner: Finding the Best Variables for Direct Marketing Models. Journalof Targeting Measurement and Analysis for Marketing, 9, 270–296 (2001) ArticleGoogle Scholar
  26. S.E. Robertson: On Relevance Weight Estimation and Query Expansion. Jour-nal of Documentation, 42, 182–188 (1986) ArticleGoogle Scholar
  27. S.E. Robertson, K. Sparck Jones: Relevance Weighting of Search Terms. Jour-nal of the American Society for Information Science, 27, 129–146 (1976) ArticleGoogle Scholar
  28. Y. Sai, Y.Y. Yao, N. Zhong: Data Analysis and Mining in Ordered Information Tables. Proc. 2001 IEEE International Conference on Data Mining (ICDM’01) (IEEE Computer Society Press, 2001 ) pp. 497–504 Google Scholar
  29. G. Salton and M.H. McGill: Introduction to Modern Information Retrieval (McGraw-Hill, 1983 ) Google Scholar
  30. E. Suzuki: Autonomous Discovery of Reliable Exception Rules, Proc Third International Conference on Knowledge Discovery and Data Mining (KDD-97) (AAAI Press, 1997) pp. 259–262 Google Scholar
  31. K.S. Jones, P. Willett: Readings in Information Retrieval (Morgan Kaufmann, 1997 ) Google Scholar
  32. A.R. Simon, S.L. Shaffer: Data Warehousing and Business Intelligence for e-Commerce (Morgan Kaufmann, 2001 ) Google Scholar
  33. J. Srivastava, R. Cooley, M. Deshpande, P. Tan: Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGKDD Explorations, Newsletter of SIGKDD, 1, 12–23 (2000) ArticleGoogle Scholar
  34. R. Stone: Successful Direct Marketing Methods, 6th ed. (NTC Business Books, 1996 ) Google Scholar
  35. D. Van den Poel, Z. Piasta: Purchase Prediction in Database Marketing with the ProbRough System, In L. Polkowski, A. Skowron (eds.) Rough Sets and Current Trends in Computing, LNAI 1424, 593–600 (Springer, 1998 ) Google Scholar
  36. S. Watanabe: Pattern Recognition as a Quest for Minimum Entropy. Pattern Recognition, 13, 381–387 (Elsevier, 1981 ) Google Scholar
  37. M. Wedel, W.A. Kamakura: Market Segmentation: Conceptual and Methodological Foundations (Kluwer, 1999 ) Google Scholar
  38. S.K.M. Wong, Y.Y. Yao: A Probability Distribution Model for Information Retrieval. Information Processing and Management, 25, 39–53 (1989) ArticleGoogle Scholar
  39. S.K.M. Wong, Y.Y. Yao: A Generalized Binary Probabilistic Independence Model. Journal of the American Society for Information Science, 41, 324–329 (1990) ArticleGoogle Scholar
  40. S.K.M. Wong, Y.Y. Yao: An Information-Theoretic Measure of Term Specificity. Journal of the American Society for Information Science, 43, 54–61 (1992) ArticleGoogle Scholar
  41. Y.Y. Yao, S.K.M. Wong, C.J. Butz: On Information-Theoretic Measures of Attribute Importance. In N. Zhong, L. Zhou (eds.) Methodologies for Knowledge Discovery and Data Mining, LNAI 1574 (Springer, 1999 ) pp. 479–488 Google Scholar
  42. Y.Y. Yao, N. Zhong: Mining Market Value Functions for Targeted Marketing. Proc. 25th IEEE International Computer Software and Applications Conference (COMPSAC’01) (IEEE Computer Society Press, 2001 ) pp. 517–522 Google Scholar
  43. Y.Y. Yao, N. Zhong: Granular Computing Using Information Tables. T.Y. Lin, Y.Y. Yao, L.A. Zadeh (eds.) Data Mining, Rough Sets and Granular Computing (Physica-Verlag, 2002 ) pp. 102–124 Google Scholar
  44. Y.Y. Yao, N. Zhong, J. Huang, C. Ou, C. Liu: Using Market Value Functions for Targeted Marketing Data Mining. International Journal of Pattern, Recognition and Artificial Intelligence, 16 (8) 1117–1131 (World Scientific, 2002 ) Google Scholar
  45. N. Zhong, J.Z. Dong, C. Liu, S. Ohsuga: A Hybrid Model for Rule Discovery in Data. Knowledge Based Systems,14 (7) 397–412 (Elsevier, 2001) 6. Data Mining for Targeted Marketing 131 Google Scholar
  46. N. Zhong, S. Ohsuga: Automatic Knowledge Discovery in Larger Scale Knowledge-Data Bases. In C. Leondes (ed.) the Handbook of Expert Systems, 4, 1015–1070 (Academic Press, 2001 ) Google Scholar
  47. N. Zhong, C. Liu, S. Ohsuga: Dynamically Organizing KDD Processes, International Journal of Pattern Recognition and Artificial Intelligence, 15 (3) 451–473 (World Scientific, 2001 ) Google Scholar
  48. N. Zhong, Y.Y. Yao, M. Ohshima, S. Ohsuga: Interestingness, Peculiarity, and Multi-Database Mining. Proc. 2001 IEEE International Conference on Data Mining (ICDM’01) (IEEE Computer Society Press, 2001 ) pp. 566–573 Google Scholar
  49. N. Zhong, J.Z. Dong, S. Ohsuga: Using Rough Sets with Heuristics to Feature Selection. Journal of Intelligent Information Systems, 16 (3) 199–214 (Kluwer, 2001 ) Google Scholar
  50. N. Zhong, A. Skowron: A Rough Sets Based Knowledge Discovery Process. International Journal of Applied Mathematics and Computer Science, 11 (3) 101–117 (Technical University Press, 2001 ) Google Scholar
  51. N. Zhong, J. Liu, Y.Y. Yao (eds.): In Search of the Wisdom Web, IEEE Computer, 35 (11) 27–31 (2002) Google Scholar
  52. N. Zhong, J. Liu, Y.Y. Yao (eds.): Web Intelligence (Springer, 2003 ) Google Scholar
  53. N. Zhong: Toward Web Intelligence. In E.M. Ruiz, J. Segovia, P.S. Szczepaniak(eds.) Advances in Web Intelligence, LNAI 2663 (Springer, 2003 ) pp. 1–14 Google Scholar
  54. N. Zhong, Y.Y. Yao, M. Ohshima: Peculiarity Oriented Multi-Database Min-ing, IEEE Transaction on Knowlegde and Data Engineering, 15 (4) 952–960 (2003) Google Scholar
  55. N. Zhong: Developing Intelligent Portals by Using WI Technologies, Proc. the Second International Conference on Active Media Technology (AMT’04) (World Scientific, 2004) Google Scholar

Author information

Authors and Affiliations

  1. Maebashi Institute of Technology, Japan Ning Zhong
  2. University of Regina, Canada Yiyu Yao
  3. Beijing University of Technology, China Chunnian Liu, Jiajin Huang & Chuangxin Ou
  1. Ning Zhong