C4.5: programs for machine learning
C4.5: programs for machine learning
A statistical perspective on data mining
Future Generation Computer Systems - Special double issue on data mining
Probabilistic Estimation-Based Data Mining for Discovering Insurance Risks
IEEE Intelligent Systems
Systems for Knowledge Discovery in Databases
IEEE Transactions on Knowledge and Data Engineering
Business applications of data mining
Communications of the ACM - Evolving data mining into solutions for insights
Sequential cost-sensitive decision making with reinforcement learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Data-intensive analytics for predictive modeling
IBM Journal of Research and Development
Mathematical sciences in the nineties
IBM Journal of Research and Development
Cross channel optimized marketing by reinforcement learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
On optimizing the selection of business transformation projects
IBM Systems Journal
Inducing a marketing strategy for a new pet insurance company using decision trees
Expert Systems with Applications: An International Journal
A probabilistic estimation framework for predictive modeling analytics
IBM Systems Journal
Optimizing debt collections using constrained reinforcement learning
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Analyzing compliance of service-based business processes for root-cause analysis and prediction
ICWE'10 Proceedings of the 10th international conference on Current trends in web engineering
Supporting smart interactions with predictive analytics
The smart internet
Supporting smart interactions with predictive analytics
The smart internet
Using context to improve the effectiveness of segmentation and targeting in e-commerce
Expert Systems with Applications: An International Journal
Business leadership through analytics
IBM Journal of Research and Development
Service Oriented Computing and Applications
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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 EventsTM (IBM ATM-SETM) 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.