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Forecasting Market Shares from Models for Sales

Author

Listed:
  • Fok, D.
  • Franses, Ph.H.B.F.

Abstract

Dividing forecasts of brand sales by a forecast of category sales, when they are generated from brand specific sales-response models, renders biased forecasts of the brands' market shares. In this paper we therefore propose an easy-to-apply simulation-based method which results in unbiased forecasts of the market shares. An illustration for five tuna fish brands emphasizes the practical relevance of the advocated method.

Suggested Citation

  • Fok, D. & Franses, Ph.H.B.F., 2000. "Forecasting Market Shares from Models for Sales," ERIM Report Series Research in Management ERS-2000-03-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    • Handle: RePEc:ems:eureri:12
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    Cited by:

    1. Alina Popa & Shahrazad Hadad & Robert Paiusan & Marian Nastase, 2018. "A New Method for Agricultural Market Share Assessment," Sustainability, MDPI, vol. 11(1), pages 1-13, December.
    2. Kanellos, Nikolaos & Katsianis, Dimitrios & Varoutas, Dimitrios, 2022. "Forecasting a telecommunications provider's market share," 31st European Regional ITS Conference, Gothenburg 2022: Reining in Digital Platforms? Challenging monopolies, promoting competition and developing regulatory regimes 265639, International Telecommunications Society (ITS).
    3. Fok, D. & Paap, R. & Franses, Ph.H.B.F., 2003. "Modeling Dynamic Effects of the Marketing Mix on Market Shares," ERIM Report Series Research in Management ERS-2003-044-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    4. Marusia Ivanova, 2007. "Genesis and Evolution of Market Share Predictive Models," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 117-148.
    5. Franses, Philip Hans, 2006. "Forecasting in Marketing," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 18, pages 983-1012, Elsevier.
    6. Zuidwijk, R.A. & Kroon, L.G., 2000. "Integer Constraints for Train Series Connections," ERIM Report Series Research in Management ERS-2000-05-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    7. Sanders, Nada R. & Manrodt, Karl B., 2003. "The efficacy of using judgmental versus quantitative forecasting methods in practice," Omega, Elsevier, vol. 31(6), pages 511-522, December.

    More about this item

    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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