Mathematical models are often used to describe the sales and adoption patterns of products in the years following their launch and one of the most popular of these models is the Bass model. However, using this model to forecast sales time series for new products is problematical because there is no historic time series data with which to estimate the model's parameters. One possible solution is to fit the model to the sales time series of analogous products that have been launched in an earlier time period and to assume that the parameter values identified for the analogy are applicable to the new product.
Research has been conducted which investigated the effectiveness of this approach by applying four forecasting methods based on analogies (and variants of these methods) to the sales of consumer electronics products marketed in the USA. It was found that all of the methods tended to lead to forecasts with high absolute percentage errors, which is consistent with other studies of new product sales forecasting. The use of the means of published parameter values for analogies led to higher errors than the parameters estimated from the research data. When using this data averaging the parameter values of multiple analogies, rather than relying on a single most-similar, product led to improved accuracy. Moreover, there was little to be gained by using more than 5 or 6 analogies.
Based on this research, a new product forecasting methodology has been developed, which helps achieving an accuracy rate of 60%, which is reported to be highly accurate in the domain.
A research partner is sought to fund further research on consumer data in order to generalise the results obtained and to commercialise the method as a software tool.
Inventor: Dr. Karima Dyussekeneva