1-1-1999 Forecasting for Marketing

Research on forecasting is extensive and includes many studies that have tested alternative methods in order to determine which ones are most effective. We review this evidence in order to provide guidelines for forecasting for marketing. The coverage includes intentions, Delphi, role playing, conjoint analysis, judgmental bootstrapping, analogies, extrapolation, rule-based forecasting, expert systems, and econometric methods. We discuss research about which methods are most appropriate to forecast market size, actions of decision makers, market share, sales, and financial outcomes. In general, there is a need for statistical methods that incorporate the manager's domain knowledge. This includes rule-based forecasting, expert systems, and econometric methods. We describe how to choose a forecasting method and provide guidelines for the effective use of forecasts including such procedures as scenarios. Comments Reprinted from Quantitative Methods in Marketing, edited by Graham J. Hooley and Michael K. Hussey (London: International Thomson Business Press, 1999), pages 92-120. The authors assert their right to include this material in ScholarlyCommons@Penn. This book chapter is available at ScholarlyCommons: http://repository.upenn. edu/marketing_papers/85 Published in Graham J. Hooley and Michael K. Hussey (Eds.), Quantitative Methods in Marketing, Second Edition. London: International Thompson Business Press, 1999, pp. 92-119. Forecasting for Marketing J. Scott Armstrong The Wharton School, University of Pennsylvania Roderick J. Brodie Department of Marketing, University of Auckland Research on forecasting is extensive and includes many studies that have tested alternative methods in order to determine which ones are most effective. We review this evidence in order to provide guidelines for forecasting for marketing. The coverage includes intentions, Delphi, role playing, conjoint analysis, judgmental bootstrapping, analogies, extrapolation, rule-based forecasting, expert systems, and econometric methods. We discuss research about which methods are most appropriate to forecast market size, actions of decision makers, market share, sales, and financial outcomes. In general, there is a need for statistical methods that incorporate the manager's domain knowledge. This includes rule-based forecasting, expert systems, and econometric methods. We describe how to choose a forecasting method and provide guidelines for the effective use of forecasts including such procedures as scenarios. INTRODUCTION Forecasting has long been important to marketing practitioners. For example, Dalrymple (1987), in his survey of 134 U.S. companies, found that 99 percent prepared formal forecasts when they used formal marketing plans. In Dalrymple (1975), 93 percent of the companies sampled indicated that sales forecasting was one of the most critical' aspects, or a ‘very important’ aspect of their company's success. Jobber, Hooley and Sanderson (1985), in a survey of 353 marketing directors from British textile firms, found that sales forecasting was the most common of nine activities on which they reported. Managers' forecasting needs vary considerably. They may need to forecast the size and growth of a market or product category. When strategic issues are being considered, they need to forecast the actions and reactions of key decision makers such as competitors, suppliers, distributors, governments, their own actions, and complementors' (organizations with whom they cooperate) actions. These actions can help to forecast market share. The resulting forecasts allow one to calculate a sales forecast. If strategic issues are not important, one can extrapolate sales directly. Finally, by forecasting costs and using the sales forecast, one can forecast profits and other financial outcomes. These forecasting needs and their relationships are illustrated in Figure 6.1. The purpose of this chapter is to provide guidance to managers about the use of formal forecasting methods in marketing. In developing the guidelines, it is recognized that managers may have negative attitudes towards the usefulness of formal forecasting. This may have occurred because they have used poor forecasts in the past, because they had unrealistic expectations about forecasting accuracy, or because they do not like it when forecasts conflict with their beliefs about the future.