Fingerhut Business Intelligence (BI) has a long and successful history of building statistical models to predict consumer behavior. The models constructed are typically segmentationbased 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 ™ (IBM ATM-SE™) 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.

How can we promote reuse of code, data and services? How can we make it easier to combine on-line resources to perform specific tasks? One serious impediment is the risk of relying on software that you do not control, especially the difficulty of determining whether the software is dependable enough for the specific task at hand. We concentrate on one form of economic risk mitigation, insurance, and explore its suitability for the software domain we are interested in. After reviewing the basic principles of insurance we present some feasible directions for dealing with software related issues and raise some software engineering research challenges. 1

Abstract — We describe a grid-based approach for enterprisescale data mining that leverages database technology for I/O parallelism, and on-demand compute servers for compute parallelism in the statistical computations. By enterprise-scale, we mean the highly-automated use of data mining in vertical business applications, where the data is stored on one or more relational database systems, and where a distributed architecture comprising of high-performance compute servers or a network of low-cost, commodity processors is used to improve application performance and provide the application deployment flexibility for overall workload management. The approach relies on an algorithmic decomposition of the data mining kernel on the data and compute grids, which makes it possible to exploit the parallelism on the respective grids in a simple way, while minimizing the data transfer between them. The overall approach is compatible with existing database standards for data mining task specification and results reporting, and hence external applications using these standardsbased interfaces do not have to be modified in order to realize the benefits of this grid-based approach. Index Terms—Data mining, Grid computing, Predictive modeling, Parallel databases. Data-mining technologies that automate the generation and application of statistical models from data are of interest in a variety of applications cutting across industry sectors. These applications include, for example, customer relationship management (Retail, Banking and Finance, Telecom), fraud detection (Banking and Finance, Telecom), lead generation

The Data Abstraction Research Group was formed in the early 1990s, to bring focus to the Mathematical Sciences department’s work in the emerging area of Knowledge Discovery and Data Mining (KD&DM). Most activities in this group have been performed in the technical area of predictive modeling, roughly at the intersection of machine learning, statistical modeling, and database technology. There has been a major emphasis on using business and industrial problems to drive the research agenda. Major accomplishments include advances in methods for feature analysis, rule based pattern discovery and probabilistic modeling, and novel solutions for insurance risk management, targeted marketing, and text mining. This paper presents an overview of the group’s major technical accomplishments. 1

IBM has played an active role in AI research since the field's inception more than 50 years ago. In a trend that reflects the increasing demand for applications that behave intelligently, IBM today carries out most AI research in an interdisciplinary fashion by combining AI techniques with other computing techniques to solve difficult technical problems. 1

y illustrative of the tremendous potential of KDD technology. 1.1 Risk Management and Targeted Marketing Insurance and direct-mail retail are examples of businesses that rely on effective data analysis in order to make profitable business decisions. For example, insurers must be able to accurately assess the risks posed by policyholders in order to set insurance premiums at competitive levels. Overcharging low-risk policyholders would motivate such policyholders to seek lower premiums elsewhere. Undercharging high-risk policyholders would attract more high-risk policyholders because of the lower premiums. In both cases, costs would increase and profits would decrease. Effective data analysis leading to the creation of accurate predictive models is essential in order to address these issues. In the case of direct-mail targeted marketing, retailers must be able to identify subsets of the population that are likely to respond to promotions in order to offset mailing and printing costs.