IDEAS home Printed from https://ideas.repec.org/a/vrs/organi/v41y2008i3p116-124n5.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/v10051-008-0013-7
    Download Restriction: no
    File URL: https://libkey.io/10.2478/v10051-008-0013-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Marnik G. Dekimpe & Dominique M. Hanssens, 1995. "The Persistence of Marketing Effects on Sales," Marketing Science, INFORMS, vol. 14(1), pages 1-21.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ramkumar Janakiraman & Shantanu Dutta & Catarina Sismeiro & Philip Stern, 2008. "Physicians' Persistence and Its Implications for Their Response to Promotion of Prescription Drugs," Management Science, INFORMS, vol. 54(6), pages 1080-1093, June.
    2. Mohamed Lachaab & Asim Ansari & Kamel Jedidi & Abdelwahed Trabelsi, 2006. "Modeling preference evolution in discrete choice models: A Bayesian state-space approach," Quantitative Marketing and Economics (QME), Springer, vol. 4(1), pages 57-81, March.
    3. Richard Paap & Philip Hans Franses, 2000. "A dynamic multinomial probit model for brand choice with different long-run and short-run effects of marketing-mix variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 717-744.
    4. Putsis, William Jr. & Dhar, Ravi, 2001. "An empirical analysis of the determinants of category expenditure," Journal of Business Research, Elsevier, vol. 52(3), pages 277-291, June.
    5. Dan Horsky & Sanjog Misra & Paul Nelson, 2006. "Observed and Unobserved Preference Heterogeneity in Brand-Choice Models," Marketing Science, INFORMS, vol. 25(4), pages 322-335, 07-08.
    6. Mira Frick & Ryota Iijima & Tomasz Strzalecki, 2019. "Dynamic Random Utility," Econometrica, Econometric Society, vol. 87(6), pages 1941-2002, November.
    7. Chen, Chien-Ming & van Dalen, Jan, 2010. "Measuring dynamic efficiency: Theories and an integrated methodology," European Journal of Operational Research, Elsevier, vol. 203(3), pages 749-760, June.
    8. Cambra-Fierro, Jesús & Polo-Redondo, Yolanda & Trifu, Andreea, 2021. "Short-term and long-term effects of touchpoints on customer perceptions," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    9. Hazel Bateman & Christine Eckert & Fedor Iskhakov & Jordan Louviere & Stephen Satchell & Susan Thorp, 2017. "Default and naive diversification heuristics in annuity choice," Australian Journal of Management, Australian School of Business, vol. 42(1), pages 32-57, February.
    10. Susan Athey & Guido W. Imbens, 2007. "Discrete Choice Models With Multiple Unobserved Choice Characteristics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1159-1192, November.
    11. Ataman, B.M., 2007. "Managing brands," Other publications TiSEM 462dcbba-2ac1-46d1-a61c-f, Tilburg University, School of Economics and Management.
    12. Robert Noland & Mohammed Quddus & Washington Ochieng, 2008. "The effect of the London congestion charge on road casualties: an intervention analysis," Transportation, Springer, vol. 35(1), pages 73-91, January.
    13. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2020. "How much do consumers know about the quality of products? Evidence from the diaper market," The Japanese Economic Review, Springer, vol. 71(4), pages 541-569, October.
    14. Jürgen Maurer & Katherine M. Harris, 2016. "Learning to Trust Flu Shots: Quasi‐Experimental Evidence from the 2009 Swine Flu Pandemic," Health Economics, John Wiley & Sons, Ltd., vol. 25(9), pages 1148-1162, September.
    15. Vincent R. Nijs & Shuba Srinivasan & Koen Pauwels, 2007. "Retail-Price Drivers and Retailer Profits," Marketing Science, INFORMS, vol. 26(4), pages 473-487, 07-08.
    16. Kamel Jedidi & Carl F. Mela & Sunil Gupta, 1999. "Managing Advertising and Promotion for Long-Run Profitability," Marketing Science, INFORMS, vol. 18(1), pages 1-22.
    17. Bart J. Bronnenberg & Jean-Pierre Dubé & Chad Syverson, 2022. "Marketing Investment and Intangible Brand Capital," Journal of Economic Perspectives, American Economic Association, vol. 36(3), pages 53-74, Summer.
    18. Cleeren, Kathleen & Geyskens, Kelly & Verhoef, Peter C. & Pennings, Joost M.E., 2016. "Regular or low-fat? An investigation of the long-run impact of the first low-fat purchase on subsequent purchase volumes and calories," International Journal of Research in Marketing, Elsevier, vol. 33(4), pages 896-906.
    19. Tülin Erdem & Susumu Imai & Michael Keane, 2003. "Brand and Quantity Choice Dynamics Under Price Uncertainty," Quantitative Marketing and Economics (QME), Springer, vol. 1(1), pages 5-64, March.
    20. Avi Goldfarb, 2006. "State Dependence at Internet Portals," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 15(2), pages 317-352, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:organi:v:41:y:2008:i:3:p:116-124:n:5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.