FORECASTING THE PRIMARY DEMAND FOR A BEER BRAND USING TIME SERIES ANALYSIS
Languages of publication
Market research often uses data (i.e. marketing mix variables) that is equally spaced over time. Time series theory is perfectly suited to study this phenomena's dependency on time. It is used for forecasting and causality analysis, but their greatest strength is in studying the impact of a discrete event in time, which makes it a powerful tool for marketers. This article introduces the basic concepts behind time series theory and illustrates its current application in marketing research. The authors use time series analysis to forecast the demand for beer on the Slovenian market using scanner data from two major retail stores. Before their analysis, only broader time spans have been used to perform time series analysis (weekly, monthly, quarterly or yearly data). In their study they analyse daily data, which is supposed to carry a lot of 'noise'. They show that - even with noise carrying data - a better model can be computed using time series forecasting, explaining much more variance compared to regular regression. The author's analysis also confirms the effect of short term sales promotions on beer demand, which is in conformity with other studies in this field.
Publication order reference
CEJSH db identifier