The challenges of pre-launch forecasting of adoption time series for new durable products

The successful introduction of new durable products plays an important part in helping companies to stay ahead of their competitors. Decisions relating to these products can be improved by the availability of reliable pre-launch forecasts of their adoption time series. However, producing such forecasts is a difficult, complex and challenging task, mainly because of the non-availability of past time series data relating to the product, and the multiple factors that can affect adoptions, such as customer heterogeneity, macroeconomic conditions following the product launch, and technological developments which may lead to the product's premature obsolescence. This paper provides a critical review of the literature to examine what it can tell us about the relative effectiveness of three fundamental approaches to filling the data void : (i) management judgment, (ii) the analysis of judgments by potential customers, and (iii) formal models of the diffusion process. It then shows that the task of producing pre-launch time series forecasts of adoption levels involves a set of sub-tasks, which all involve either quantitative estimation or choice, and argues that the different natures of these tasks mean that the forecasts are unlikely to be accurate if a single method is employed. Nevertheless, formal models should be at the core of the forecasting process, rather than unstructured judgment. Gaps in the literature are identified, and the paper concludes by suggesting a research agenda so as to indicate where future research efforts might be employed most profitably.