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Forecasting the Primary Demand for a Beer Brand Using Time Series Analysis

Author

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  • Bratina Danijel

    (Faculty of Management Koper, University of Primorska, Cankarjeva 5, 6000 Koper, Slovenia)

  • Faganel Armand

    (Faculty of Management Koper, University of Primorska, Cankarjeva 5, 6000 Koper, Slovenia)

Abstract

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. We use time series analysis to forecast the demand for beer on the Slovenian market using scanner data from two major retail stores. Before our analysis, only broader time spans have been used to perform time series analysis (weekly, monthly, quarterly or yearly data). In our study we analyse daily data, which is supposed to carry a lot of ‘noise’. We 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. Our analysis also confirms the effect of short term sales promotions on beer demand, which is in conformity with other studies in this field.

Suggested Citation

  • Bratina Danijel & Faganel Armand, 2008. "Forecasting the Primary Demand for a Beer Brand Using Time Series Analysis," Organizacija, Sciendo, vol. 41(3), pages 116-124, May.
    • Handle: RePEc:vrs:organi:v:41:y:2008:i:3:p:116-124:n:5
    DOI: 10.2478/v10051-008-0013-7
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    References listed on IDEAS

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    1. Keane, Michael P, 1997. "Modeling Heterogeneity and State Dependence in Consumer Choice Behavior," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 310-327, July.
    2. Dean W. Wichern & Richard H. Jones, 1977. "Assessing the Impact of Market Disturbances Using Intervention Analysis," Management Science, INFORMS, vol. 24(3), pages 329-337, November.
    3. Marnik G. Dekimpe & Dominique M. Hanssens, 1995. "The Persistence of Marketing Effects on Sales," Marketing Science, INFORMS, vol. 14(1), pages 1-21.
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    Cited by:

    1. Benjamin Volland, 2013. "The History of an Inferior Good: Beer Consumption in Germany," Papers on Economics and Evolution 2012-19, Philipps University Marburg, Department of Geography.
    2. Armand Faganel, 2011. "Recognized Values and Consumption Patterns of Post-Crisis Consumers," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 9(2), pages 151-170.

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