Marcel van Rooyen, Simeon J. Simoff


Businesses are experiencing difficulties with integrating data-mining analytics with decision-making and action. At present, two data-mining methodologies play a central role in enabling data-mining as a process. However, the results of reflecting on the application of these methodologies in real-world business cases against specific criteria indicate that both methodologies provide limited integration with business decision-making and action. In this paper we demonstrate the impact of these limitations on a Telco customer retention management project for a global mobile phone company. We also introduce a data-mining and analytics project methodology with improved business integration – the Strategic Analytics Methodology (SAM). The advantage of the methodology is demonstrated through its application in the same project, and comparison of the results.


  1. Ankerst, M. (2002). "Report on the SIGKDD-2002 panel - The perfect data mining tool: interactive or automated?" ACM SIGKDD Explorations 4(2): 110- 111.
  2. Chapman, P., J. Clinton, et al. (1999-2000). CRISP-DM 1.0: Cross Industry Standard Process for Data Mining. http://www.crisp-dm.org/CRISPWP-0800.pdf, CRISPDM Consortium. Accessed November 2003.
  3. Denzin, N. K. and Y. S. Lincoln (2003). Strategies of qualitative inquiry. Thousand Oaks, CA, Sage.
  4. Fayyad, U., G. Shapiro, et al. (2003). "Summary from the KDD-03 panel - Data mining: the next 10 years." ACM SIGKDD Explorations 5(2): 191-196.
  5. Hastie, T., R. Tibshirani, et al. (2001). The Elements of Statistical Learning. New York, Heidelberg, Berlin, Springer-Verlag.
  6. Hirji, K. K. (2003). A Proposed Process for Performing Data Mining Projects. Managing Data Mining Technologies in Organizations: Techniques and Applications. P. C. Pendharkar, Idea Group Inc.: 350.
  7. Kolyshkina, I. and S. J. Simoff (2007). Customer analytics projects: addressing existing problems with a process that leads to success. Conferences in Research and Practice in Information Technology, Data Mining and Analytics 2007, Australian Computer Society Inc: pp. 13-20.
  8. Kotler, P. (2002). Marketing Management: Analysis, Planning, Implementation, and Control. International, Prentice Hall.
  9. Liu, X. (2003). Systems and Applications. Intelligent Data Analysis. M. Berthold and D. J. Hand. Heidelberg, Springer-Verlag: 429-442.
  10. Pearce, I. J. A. and J. R. B. Robinson (2004). Strategic Management: Formulation, Implementation, and Control, McGraw-Hill.
  11. Pyle, D. (1999). Data Preparation for Data Mining. San Francisco, Morgan Kaufmann Publishers.
  12. Pyle, D. (2004). Business Modelling and Data Mining. London, Morgan Kaufmann.
  13. SAS Institute (2000). SAS Data Mining Projects Methodology. Cary, NC, SAS Institute Inc.
  14. Schön, D. A. (1995). The Reflective Practitioner: How Professionals Think in Action. London, Ashgate Publishing Limited.
  15. Van Rooyen, M. (2004). An evaluation of the utility of two data mining project methodologies. Proceedings of the 3rd Australasian Data Mining Conference, Cairns, Australia, University of Technology Sydney: 85-97.
  16. Van Rooyen, M. (2005). A Strategic Analytics Methodology. Faculty of Information Technology. Sydney, University of Technology: 350.
  17. Wedel, M. and W. Kamakura (2000). Segmentation: Conceptual and Methodological Foundations. Boston, Kluwer Academic Publishers.

Paper Citation

in Harvard Style

van Rooyen M. and J. Simoff S. (2008). A STRATEGIC ANALYTICS METHODOLOGY . In Proceedings of the Third International Conference on Software and Data Technologies - Volume 3: ICSOFT, ISBN 978-989-8111-53-1, pages 20-28. DOI: 10.5220/0001873300200028

in Bibtex Style

author={Marcel van Rooyen and Simeon J. Simoff},
booktitle={Proceedings of the Third International Conference on Software and Data Technologies - Volume 3: ICSOFT,},

in EndNote Style

JO - Proceedings of the Third International Conference on Software and Data Technologies - Volume 3: ICSOFT,
SN - 978-989-8111-53-1
AU - van Rooyen M.
AU - J. Simoff S.
PY - 2008
SP - 20
EP - 28
DO - 10.5220/0001873300200028