Sales forecasting using combination of diffusion model and forecast market: An adaption of prediction/preference markets

Forecasting sales accurately for a new product is difficult and complex due to non-availability of past data. However, such forecast information is crucial for successful introduction of new products which, in turn, determines the survival of companies, in many cases. Decisions relating to new products depend critically on reliable period-by-period sales forecasts (otherwise called forecast time series) as early as possible in the new product development cycle. This information is crucial in assessing cash flow and NPV relating to the new product. There have been many attempts to use growth curves (otherwise called diffusion models), such as the Bass model. These models made use of past data about analogous products to do this task. However, this method, although considered the best method, available, has many problems, such as identifying analogous products which can reliably mimic the new product in its sales characteristics. These difficulties explain why the accuracy of forecasts reported by such methods is, at best, 50%. Here we propose an innovative conceptual framework to obtain time series data required for forming the growth curve for the new product by bootstrapping the growth curve models with a novel 'Forecast market' mechanism. The effectiveness of the 'Forecast market' in obtaining accurate estimates of the time series data itself is likely to be enhanced by letting the 'Forecast market' participants use product information ranging from simple pictures of the product to high-end virtual reality systems which enable them to visualise and appreciate the features of the new product.