The purpose of this paper is to summarize key challenges and knowledge gaps in understanding the choices that both firms and customers make in a personalization/customization environment. We start with a summary of personalization and customization in practice, and then draw on research in economics, statistical, and consumer behavior to identify what we know and do not know. We conclude with a summary of key research opportunities.

This paper describes a decision support system developed to prioritize oil and gas exploration activities. During each budgeting cycle, the exploration division of a major oil company must review a number of potential oil and gas plays, and assign personnel to the most promising ones. These personnel are teams of geologists and geophysicists who are responsible for the exploration activities on the site. The number of interesting plays always exceeded the available exploration budget and manpower, so management was concerned about the development of a consistent ranking system of oil and gas plays that could be used to guide the budgeting and manpower decision. The development of the ranking system was based on familiar concepts from decision theory, specifically, multiattribute models and the value of information. Linear approximations to more complex mathematical functions were developed to simplify the model. However, this system was designed to be used on a routine basis by personnel unfamiliar with these technical issues, so they were masked from the user. The successful implementation of the proposed decision support system is described.