Modelling seasonality in innovation diffusion

The ability to forecast new product growth is especially important for innovative firms that compete in the marketplace. Today many new products exhibit very strong seasonal behaviour, which may deserve specific modelling, both for producing better forecasts in the short term and for better explaining special market dynamics and related managerial decisions. By considering seasonality as a deterministic component to be estimated jointly with the trend through Nonlinear Least Squares methods, we have developed two extensions of the Guseo–Guidolin model that are able to simultaneously describe trend and seasonality. Such models are based on two different but equally reasonable approaches: in one case we consider a simple additive decomposition of a time series and design a model in which seasonality is directly added to the trend and jointly estimated with it; in the other we design a more complex structure, mimicking that of a Generalized Bass model and embed two separate seasonal perturbations within the dynamic market potential and the corresponding adoption process. The different characteristics of two products, a pharmaceutical drug and an IT device, make it possible to appreciate empirically various modelling options and performances. Both models are quite simple to implement and to interpret from a managerial point of view.