Estimating campaign benefits and modeling lift

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Estimating campaign benefits and modeling lift

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Conference on Knowledge Discovery in Data archive
Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

San Diego, California, United States

Pages: 185 - 193

Year of Publication: 1999

ISBN:1-58113-143-7

Authors

Gregory Piatetsky-Shapiro	Knowledge Stream Partners, Boston, MA
Brij Masand	GTE Laboratories, Waltham, MA

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
AAAI : Am Assoc for Artifical Intelligence
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 6, Downloads (12 Months): 63, Citation Count: 13

Additional Information:

references cited by index terms collaborative colleagues peer to peer

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Bhattacharya, S., Direct Marketing Response models using Genetic Algorithms, Proceedings of the KDD-98: Fourth International Conference on Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA, pp 144-148.
	2	Ronald J. Brachman , Tej Anand, The process of knowledge discovery in databases, Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, Menlo Park, CA, 1996
	3	Elkan, C. Boosting and Naive Bayesian Learning. Technical Report No. CS97-557, September 1997, UCSD. May 1997.
	4	Ling, C.X., and Li, C, Data Mining for Direct Marketing: Problems and solutions, In Proceedings of KDD-98: Fourth International Conference on Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA, pp 73-79.
	5	Masand B. and Piatetsky-Shapiro G., A comparison of different approaches for maximizing business payoff of prediction models, Proceedings of KDD-96: the Second International Conference on Knowledge Discovery and Data Mining, AAAI/MIT press, pp 195-201.
	6	Donald Michie , D. J. Spiegelhalter , C. C. Taylor , John Campbell, Machine learning, neural and statistical classification, Ellis Horwood, Upper Saddle River, NJ, 1995
	7	Parsa,I.KDD-CUP-97 results,' www.epsilon.com/KDDCUP/index.htm.
	8	Piatetsky-Shapiro, G., et al, An Overview of Issues in Developing industrial Data Mining and Knowledge Discovery Applications Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, (KDD- 96), p. 89, AAAI Press, 1996.
	9	Provost, F. and Fawcett, T., Analysis and visualization of Classifier performance: Comparison under Imprecise Class and Cost distributions, In KDD-97: Proceedings of the third International Conference on Knowledge Discovery and Data Mining of KDD-97, Newport Beach, CA, AAAI-Press, Menlo Park, Ca, pp 43-48.

CITED BY 13

	Gregory Piatetsky-Shapiro , Sam Steingold, Measuring lift quality in database marketing, ACM SIGKDD Explorations Newsletter, v.2 n.2, p.76-80, Dec. 2000
	Bhavani Raskutti , Alan Herschtal, Predicting the product purchase patterns of corporate customers, Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, August 21-24, 2005, Chicago, Illinois, USA
	Xiaohua Hu, A Data Mining Approach for Retailing Bank Customer Attrition Analysis, Applied Intelligence, v.22 n.1, p.47-60, January-February 2005
	Bing Liu , Yiming Ma , Ching Kian Wong , Philip S. Yu, Scoring the Data Using Association Rules, Applied Intelligence, v.18 n.2, p.119-135, March-April 2003
	Tomoharu Iwata , Kazumi Saito , Takeshi Yamada, Recommendation method for extending subscription periods, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA
	Wendy Gersten , Rüdiger Wirth , Dirk Arndt, Predictive modeling in automotive direct marketing: tools, experiences and open issues, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, p.398-406, August 20-23, 2000, Boston, Massachusetts, United States
	Rodney D. Nielsen, MOB-ESP and other improvements in probability estimation, Proceedings of the 20th conference on Uncertainty in artificial intelligence, p.418-425, July 07-11, 2004, Banff, Canada
	YongSeog Kim, Toward a successful CRM: variable selection, sampling, and ensemble, Decision Support Systems, v.41 n.2, p.542-553, January 2006
	Matthew Richardson , Pedro Domingos, Mining knowledge-sharing sites for viral marketing, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada
	Huseyin Cavusoglu , Srinivasan Raghunathan, Configuration of Detection Software: A Comparison of Decision and Game Theory Approaches, Decision Analysis, v.1 n.3, p.131-148, September 2004
	Yiming Ma , Bing Liu , Ching Kian Wong , Philip S. Yu , Shuik Ming Lee, Targeting the right students using data mining, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, p.457-464, August 20-23, 2000, Boston, Massachusetts, United States
	YongSeog Kim , W. Nick Street, An intelligent system for customer targeting: a data mining approach, Decision Support Systems, v.37 n.2, p.215-228, May 2004
	Pedro Domingos , Matt Richardson, Mining the network value of customers, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.57-66, August 26-29, 2001, San Francisco, California

INDEX TERMS

Primary Classification:
H. Information Systems
H.2 DATABASE MANAGEMENT
H.2.8 Database applications
Subjects: Data mining

Additional Classification:
J. Computer Applications
J.1 ADMINISTRATIVE DATA PROCESSING
Subjects: Marketing

General Terms:
Design, Management, Measurement, Performance, Reliability, Theory

Keywords:
database marketing, estimation, lift

Collaborative Colleagues:

Gregory Piatetsky-Shapiro: colleagues

Brij Masand: colleagues

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