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Forecasting market shares from models for sales

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  • Fok, Dennis
  • Franses, Philip Hans

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.
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Suggested Citation

  • Fok, Dennis & Franses, Philip Hans, 2001. "Forecasting market shares from models for sales," International Journal of Forecasting, Elsevier, vol. 17(1), pages 121-128.
    • Handle: RePEc:eee:intfor:v:17:y:2001:i:1:p:121-128
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    1. Foekens, Eijte W. & Leeflang, Peter S. H. & Wittink, Dick R., 1994. "A comparison and an exploration of the forecasting accuracy of a loglinear model at different levels of aggregation," International Journal of Forecasting, Elsevier, vol. 10(2), pages 245-261, September.
    2. Arino, Miguel A. & Franses, Philip Hans, 2000. "Forecasting the levels of vector autoregressive log-transformed time series," International Journal of Forecasting, Elsevier, vol. 16(1), pages 111-116.
    3. Hill, R Carter & Cartwright, P A, 1994. "The Statistical Properties of the Equity Estimator," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(2), pages 141-147, April.
    4. Wittink, Dick R., 1987. "Causal market share models in marketing: Neither forecasting nor understanding?," International Journal of Forecasting, Elsevier, vol. 3(3-4), pages 445-448.
    5. Kumar, V., 1994. "Forecasting performance of market share models: an assessment, additional insights, and guidelines," International Journal of Forecasting, Elsevier, vol. 10(2), pages 295-312, September.
    6. Hill, R Carter & Cartwright, P A, 1994. "The Statistical Properties of the Equity Estimator: A Rejoinder," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(2), pages 155-155, April.
    7. Brodie, Roderick J. & De Kluyver, Cornelis A., 1987. "A comparison of the short term forecasting accuracy of econometric and naive extrapolation models of market share," International Journal of Forecasting, Elsevier, vol. 3(3-4), pages 423-437.
    8. Allenby, Greg M. & Rossi, Peter E., 1998. "Marketing models of consumer heterogeneity," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 57-78, November.
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    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.

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    More about this item

    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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