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2007 ACM SIGKDD Data Mining Practice Prize Winners

Organizers. Brendan Kitts (co-chair), iProspect Gabor Melli (co-chair), Simon Fraser University Gregory Piatetsky-Shapiro, PhD., KDNuggets Richard Bolton, PhD., KnowledgeBase Marketing, Inc. Pip Courbois, PhD., Amazon Simeon J. Simoff, PhD., University of Technology Sydney

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2007 ACM SIGKDD Data Mining Practice Prize Winners

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  1. Organizers Brendan Kitts (co-chair), iProspect Gabor Melli (co-chair), Simon Fraser University Gregory Piatetsky-Shapiro, PhD., KDNuggets Richard Bolton, PhD., KnowledgeBase Marketing, Inc. Pip Courbois, PhD., Amazon Simeon J. Simoff, PhD., University of Technology Sydney Gang Wu, PhD., Microsoft Teresa Mah, PhD., Microsoft Tom Osborn, PhD., Verism Inc. Ed Freeman, Washington Mutual Luis Adarve-Martin, Microsoft Karl Rexer, PhD., Rexer Analytics John Elder, PhD., Elder Research Jing Ying Zhang, PhD., Microsoft 2007 ACM SIGKDD Data Mining Practice Prize Winners Special Thanks Mary Crissey, SAS Faye Merrideth, SAS Francoise Soulie Fogelman,PhD., KXEN

  2. Practice Prize Runner Up Synopsis OnTarget automates sales force by estimating the probability of purchase at the product-brand level, and provides field-validated analytical estimates of future revenue opportunity in each operational market Revenue impact 7,000 users, 0.2MM report downloads, used in 29% of won sales, 5x ROI, 2 years in operation Technique Probabilty of Purchase Prediction, Quartile regression Authors Richard Lawrence Clauda Perlich Saharon Rosset Ildar Khabibrakhmanov Shilpa Mahatma Sholom Weiss Paper Analytics-driven solutions for customer targeting and sales force allocation

  3. Practice Prize Winner Synopsis: “We describe a system that has been in operation for the past two years that builds and monitors over 15,000 separate baseline models and the process that is used for generating and investigating alerts using these baselines…” Revenue impact: $2 billion, ROI 10x Technique: Anomaly detection Authors Joseph Bugajski Robert L. Grossman Chris Curry David Locke Steve Vejcik Paper Data Quality Models for High Volume Transaction Streams

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