An accurate forecast of adoption can be the cornerstone of your investment and strategic planning decisions. Prior to commercialization, the forecaster must rely on analogues, research and expert judgment to establish parameter estimates for the adoption forecast. After commercialization, history begins to accumulate and any future representation(s) of adoption MUST be consistent with historical sales.
The ability of the Bass diffusion model to forecast a variety of technologies across innumerable industries is well established in the literature. In the pre-requisite course, Fitting a Best Estimate Forecast of Adoption Through a Historical Data Series, we demonstrated the utility of the Bass diffusion model for creating a forecast of adoption by fitting parameters to a historical data set. Once uncertainty is introduced into the forecast, however, introduce significant challenges in ensuring all possible representations of future adoption are consistent (i.e., are plausible manifestations of) the adoption that has occurred in the past. While we have observed, in practice, several work-around solutions to this issue, each of them simply masks a sampling of forecasts that are implausible given history.
In this course we share a patent-pending algorithm for forecasting probabilistic adoption while maintaining fidelity to history. This distance-learning course will outline how to use historical data to inform credible and reliable forecasts of probabilistic adoption, given even a few historical data points. Topics covered include:
- An overview of the importance of incorporating uncertainty in adoption forecasts
- A description of the challenges introduced when forecasting uncertain futures through historical adoption data
- A review of common work-arounds that produce faulty forecasts
- Manual construction of a curve-fitting framework, with a detailed walk-through of the patent-pending algorithm developed by Applied Quantitative Sciences, Inc.
- Method of curve-fitting to uncertain future levels of adoption through a historical data series
- How to evaluate the utility of a fitted forecast
- Incorporation of fitted results into a larger model
All attendees will receive a FREE TEMPLATE for fitting a probabilistic adoption forecast with adherence to historical data, which can be used as either a stand-alone application or as an add-in to AQS Model Builder.
Fitting a Best Estimate Forecast of Adoption Through a Historical Data Series
Modality: Didactic and experiential
Prerequisites: None
Fitting a probabilistic Forecast of Adoption with Adherence to Historical Data
Prerequisites: Fitting a Best Estimate Forecast of Adoption Through a Historical Data Series
Register now for BOTH: $548, A SAVINGS OF $100!
