Marketing Papers
Document Type
Working Paper
Date of this Version
10-2012
DOI
10.2139/ssrn.3063308
Abstract
In recent decades, much comparative testing has been conducted to determine which forecasting methods are more effective under given conditions. This evidence-based approach leads to conclusions that differ substantially from current practice. This paper summarizes the primary findings on what to do – and what not to do. When quantitative data are scarce, impose structure by using expert surveys, intentions surveys, judgmental bootstrapping, prediction markets, structured analogies, and simulated interaction. When quantitative data are abundant, use extrapolation, quantitative analogies, rule-based forecasting, and causal methods. Among causal methods, use econometrics when prior knowledge is strong, data are reliable, and few variables are important. When there are many important variables and extensive knowledge, use index models. Use structured methods to incorporate prior knowledge from experiments and experts’ domain knowledge as inputs to causal forecasts. Combine forecasts from different forecasters and methods. Avoid methods that are complex, that have not been validated, and that ignore domain knowledge; these include intuition, unstructured meetings, game theory, focus groups, neural networks, stepwise regression, and data mining.
Keywords
checklist, competitor behavior, forecast accuracy, market share, market size, sales forecasting
Recommended Citation
Green, K. C., & Armstrong, J. S. (2012). Demand Forecasting: Evidence-Based Methods. http://dx.doi.org/10.2139/ssrn.3063308
Included in
Advertising and Promotion Management Commons, Business Administration, Management, and Operations Commons, Business Intelligence Commons, Marketing Commons, Sales and Merchandising Commons
Date Posted: 15 June 2018
Comments
This is an unpublished manscript.