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IBM Systems Journal
IT-Enabled Business Transformation   Volume 46, Number 4, 2007
Table of contents: HTMLPDF This article: HTMLPDF   Copyright info

Analytics-driven solutions for customer targeting and sales-force allocation - References

 by R. Lawrence,
C. Perlich,
S. Rosset,
J. Arroyo,
M. Callahan,
J. M. Collins,
A. Ershov,
S. Feinzig,
I. Khabibrakhmanov,
S. Mahatma,
M. Niemaszyk,
and S. M. Weiss
Cited references

  1. D. Ledingham, M. Kovac, and H. L. Simon, “The New Science of Sales Force Productivity,” Harvard Business Review, 124–133 (September 2006).
  2. W. G. Zikmund, R. McLeod, Jr., and F. W. Gilber, Customer Relationship Management: Integrating Marketing Strategy and Information Technology, John Wiley & Sons, Inc., Hoboken, NJ (2002).
  3. M. J. A. Berry and G. S. Linoff, Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Wiley Computer Publishing, Hoboken, NJ (2004).
  4. A. J. Morgan and S. A. Inks, “Technology and the Sales Force–Increasing Acceptance of Sales Force Automation,” Industrial Marketing Management 30, No. 5, 463–472 (2001).
  5. C. Speier and V. Venkatesh, “The Hidden Minefields in the Adoption of Sales Force Automation Technologies,” Journal of Marketing 66, No. 3, 98–111 (2002).
  6. Rational Data Architect, IBM Corporation, http://www-306.ibm.com/software/data/integration/rda/.
  7. WebSphere DataStage, IBM Corporation, http://www-306.ibm.com/software/data/integration/datastage/.
  8. S. Rosset and R. D. Lawrence, “Data-Enhanced Predictive Modeling for Sales Targeting,” Proceedings of the SIAM Conference on Data Mining, Bethesda, MD (2006), http://www.siam.org/meetings/sdm06/proceedings/063rossets.pdf.
  9. T. Hastie, R. Tibshirani, and J. H. Friedman, Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, New York, NY (2003).
  10. S. Rosset, E. Neumann, U. Eick, N. Vatnik, and I. Idan, “Evaluation of Prediction Models for Campaign Planning,” Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA (2001), pp. 456–461.
  11. R. Koenker, Quantile Regression, Cambridge University Press, New York, NY (2005).
  12. J. Langford, R. Oliveira, and B. Zadrozny, “Predicting Conditional Quantiles via Reduction to Classification,” Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, Cambridge, MA (2006), http://hunch.net/∼jl/projects/reductions/median/median_uai.pdf.
  13. S. Merugu, S. Rosset, and C. Perlich, “A New Multi-View Regression Approach with an Application to Customer Wallet Estimation,” Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA (2006), pp. 656–661.
  14. C. Perlich, S. Rosset, R. Lawrence, and B. Zadrozny, “High Quantile Modeling for Customer Wallet Estimation with Other Applications,” Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, CA (August 2007).
  15. L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and Regression Trees, Chapman and Hall/CRC Press, Boca Raton, FL (1984).
  16. T. M. Mitchell, Machine Learning, McGraw-Hill Publishing, San Francisco, CA (1997).
  17. S. Rosset, C. Perlich, and B. Zadrozny, “Ranking-Based Evaluation of Regression Models,” Proceedings of the 5th IEEE International Conference on Data Mining, Houston, TX (2005), pp. 370–377.


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