The technology for managing customer relationships has gotten fairly sophisticated. Companies can draw on databases that tell them how much each customer has purchased and how often, which they may supplement with detailed demographic profi les. By applying statistical models, they can predict not only when each customer is likely to make a future purchase but also what he or she will buy and through which channel. Managers can use these data to estimate a potential lifetime value for every customer and to determine whether, when, and how to contact each one to maximize the chances of realizing (and even increasing) his or her value.

Using store-level scanner data, the authors investigate the effect of retail store price promotion, featuring, and displays on sales of brands of disposable diapers within a city. A hierarchical, cross-sectional, and time-series modeling procedure is used to identify the competitive structure among retail stores within a test market city. Models are developed for pooled store pairs to investigate the effect of promotion on store substitution. The specification of these models is based on findings from within-store brand substitution, followed by featuring and displays. Similarly, these activities produced store substitution in certain instances. However which specific promotional variables are significant and the magnitude of the effect are a function of the geographic proximity of the stores.

This study tests and contrasts the ability of multidimensional scaling (MDS) and nonlinear mapping (NLM) in recovering complex data structures in attribute space, and aiding researchers and practitioners in making neighborhood interpretations. The relative merits of both MDS and NLM for product positioning are explored and discussed. A formal comparison of the performance of NLM versus MDS is presented using both simulated and actual data. The results of this study provid3e direction as to the conditions under which a nonlinear mapping algorithm is preferable over MDS.

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.