The need for contextual and technical knowledge in judgmental forecasting

Abstract The purpose of this research is to assess the extent to which judgmental forecasts are improved by having more contextual and technical knowledge. Contextual information is knowledge gained by practitioners through experience on the job, consisting of general forecasting experience in the industry as well as specific product knowledge. Technical knowledge is knowledge about data analysis and formal forecasting procedures, including information on how to analyze data judgmentally. We directly compared judgmental forecasts of business practitioners with those generated by students, using 22 real‐world time series. The practitioners had considerable contextual but no technical knowledge. The students had no contextual but two different levels of technical knowledge. We also generated forecasts with statistical methods to benchmark performance. Results show that contextual knowledge is particularly important in making good judgmental forecasts, while technical knowledge has little value. Practitioner forecasts are better than student forecasts in almost all comparisons. A decisive factor affecting forecast performance appears to be data variability, measured by the coefficient of variation of the time‐series data. As the variability of a time series increases, the performance of all forecasts deteriorates, but judgmental forecasts by practitioners become more preferable. Statistical methods have difficulty achieving reasonable forecasts when the data are more variable, whereas judgemental forecasts reinforced by contextual information do relatively well. Data variability is one explanation for the mixed findings of past studies, relative to how well statistical techniques compare with judgment as a forecasting method.