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.

The last decade has seen rapid advances in telecommunications technology in an increasingly deregulated and competitive market place. Companies operating in these various markets have relied on demand forecasts to justify the considerable investment needed to ensure capacity availability at the right time. These new markets are typically composed of new consumers taking up a product or service for the first time, established users changing their usage patterns, users of competing services shifting to the alternative service and those exiting from this segment of the market altogether. This paper describes various models that have been used to understand market dynamics. Markets discussed include both established and new: mobile, the internet, and PSTN (public switched telephony network). Cross-sectional choice models of the mode of accessing the service are discussed along with models for usage in established markets. These models typically include price (and perceived price) differentials and use standard econometric methods, focusing on price elasticity estimation. Forecasting accuracy has been neglected. New product models may include additional 'drivers' such as aspects of service quality and the attributes of the products themselves. Both choice models of adoption of new products and Bass-type diffusion models have been used in forecasting. Because of the complexity of the 'drivers' of the adoption process, the successful modelling of these new markets has been limited, not least by inadequate data. Simulation models have been proposed to structure the problem more completely and overcome these inadequacies. Both these classes of model have not been effectively validated, researchers having been content just to propose a new approach without thoroughly testing it against alternatives. The only class of telecommunications forecasting problem that has been more thoroughly analysed are those needed to support operations such as call centres. This review paper describes the research that has been carried out on the three problem areas of established products, new products and operations, highlighting areas where further research is needed. The paper also serves as an introduction to the Special Issue on Telecoms Forecasting by describing how the papers contribute to the developing research agenda.

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.