Communications of the ACM - Special issue on parallelism
Instance-Based Learning Algorithms
Machine Learning
Statistics and data mining techniques for lifetime value modeling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Density-based indexing for approximate nearest-neighbor queries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
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Determining and setting maximal revenue expectations or other business performance targets---whether it is for regional company divisions or individual customers---can have profound financial implications. Operational techniques are changed, staffing levels are altered and management attention is re-focused---all in the name of expectations. In practice these expectations are often derived in an ad hoc manner. To address this unsupervised task, we combine nearest neighbor methods and classical statistical methods and derive a new solution to the classical econometric task of frontier analysis. We apply our methodology to two real world business problems in Verizon, a major telecommunications provider in the United States, more specifically in the print yellow page division Verizon Information Services: (1) identifying under marketed customers for targeted upselling campaigns and focused sales attention, and (2) benchmarking regional directory divisions to incent performance improvements. Our analysis uncovers some commercially useful aspects of these domains and by conservative estimates can increase revenue by several million dollars in each domain.