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.

Customer lifetime value (LTV)-which is a measure of the profit generating potential, or value, of a customer-is increasingly being considered a touchstone for customer relationship management.The central challenge in predicting LTV is the production of estimated customer tenures with a given service supplier, based on information contained in company databases.Classical survival analysis techniques like proportional hazards regression focus on covariate effects frequently presumed to be linear, and examination of age-wise effects can be difficult.Further, segments of customers, whose lifetimes and covariate effects can vary widely, are not necessarily easy to detect.A new neural network model for hazard prediction is used to free proportional hazards-like models from their linearity and proportionality constraints, and clustering tools are applied to identify segments of customer hazard patterns.Using the proportional hazards and neural network models in tandem, we demonstrate how data mining tools can be apt complements of the classical statistical models, and show that their combined usage overcomes many of the shortcomings of each separate tool setresulting in a LTV tenure prediction model that is both accurate and understandable.