Article

Segmentation-based modeling for advanced targeted marketing

Authors:

C. Apte,

F. Tipu,

B. NelsonAuthors Info & Claims

KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 408 - 413

https://doi.org/10.1145/502512.502573

Published: 26 August 2001 Publication History

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Abstract

Fingerhut Business Intelligence (BI) has a long and successful history of building statistical models to predict consumer behavior. The models constructed are typically segmentation-based models in which the target audience is split into subpopulations (i.e., customer segments) and individually tailored statistical models are then developed for each segment. Such models are commonly employed in the direct-mail industry; however, segmentation is often performed on an ad-hoc basis without directly considering how segmentation affects the accuracy of the resulting segment models. Fingerhut BI approached IBM Research with the problem of how to build segmentation-based models more effectively so as to maximize predictive accuracy. The IBM Advanced Targeted Marketing-Single Events^TM (IBM ATM-SE^TM) solution is the result of IBM Research and Fingerhut BI directing their efforts jointly towards solving this problem. This paper presents an evaluation of ATM-SE's modeling capabilities using data from Fingerhut's catalog mailings.

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Gu YWu YLu HLu XQian HZhou JZhou ABaeza-Yates RBonchi F(2024)LASCA: A Large-Scale Stable Customer Segmentation Approach to Credit Risk AssessmentProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671550(5006-5017)Online publication date: 25-Aug-2024
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Index Terms

Segmentation-based modeling for advanced targeted marketing
1. Information systems
  1. Information systems applications

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Information

Published In

KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining

August 2001

493 pages

ISBN:158113391X

DOI:10.1145/502512

Conference Chair:
Doheon Lee
Chonnam National University, Korea
,
General Chair:
Mario Schkolnick
SGI
,
Program Chairs:
Foster Provost
New York University
,
Ramakrishnan Srikant
IBM Almaden Research Center

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 August 2001

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KDD01

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KDD01: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 26 - 29, 2001

California, San Francisco

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KDD '01 Paper Acceptance Rate 31 of 237 submissions, 13%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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August 3 - 7, 2025

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https://dl.acm.org/doi/10.1145/3637528.3671550
Lee GPippy JHobbs M(2022)Optimizing the Feature Set for Machine Learning Charitable PredictionsAI 2022: Advances in Artificial Intelligence10.1007/978-3-031-22695-3_44(631-645)Online publication date: 3-Dec-2022
https://doi.org/10.1007/978-3-031-22695-3_44
Lee GRaghavan AHobbs M(2021)Deep Learning the Donor Journey with Convolutional and Recurrent Neural NetworksDeep Learning Applications, Volume 310.1007/978-981-16-3357-7_12(295-320)Online publication date: 13-Nov-2021
https://doi.org/10.1007/978-981-16-3357-7_12
Lee GRaghavan AHobbs M(2020)Improving the Donor Journey with Convolutional and Recurrent Neural Networks2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA51294.2020.00149(913-920)Online publication date: Dec-2020
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