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Genesis and Evolution of Market Share Predictive Models

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  • Marusia Ivanova

Abstract

The time borders in market share predictive modeling evolution have been set as a result of a critical perusal of the leading scientific research papers, which cover the period from 1950 to our days. Five evolution stages have been identified: (1) Stage of origin of market share predictive models: 1951-1965; (2) Stage of realistic market share predictive models: 1966-1969; (3) Stage of logically consistent market share predictive models: 1970-1988; (4) Stage of maturity in market share predictive modeling: 1989-2004; (5) Stage of analytical reengineering of market share predictive modeling: after 2004. The types of models representative of every evolution stage, as well as some of their advantages and disadvantages, have been discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:bas:econst:y:2007:i:2:p:117-148
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    1. Philip Kotler, 1965. "Competitive Strategies for New Product Marketing Over the Life Cycle," Management Science, INFORMS, vol. 12(4), pages 104-119, December.
    2. Gregory S. Carpenter & Lee G. Cooper & Dominique M. Hanssens & David F. Midgley, 1988. "Modeling Asymmetric Competition," Marketing Science, INFORMS, vol. 7(4), pages 393-412.
    3. David J. Reibstein & Paul W. Farris, 1995. "Market Share and Distribution: A Generalization, a Speculation, and Some Implications," Marketing Science, INFORMS, vol. 14(3_supplem), pages 190-202.
    4. Gruca, TS & Klemz, BR, 1998. "Using Neural Networks to Identify Competitive Market Structures from Aggregate Market Response Data," Omega, Elsevier, vol. 26(1), pages 49-62, February.
    5. Paul Farris & James Olver & Cornelis De Kluyver, 1989. "The Relationship Between Distribution and Market Share," Marketing Science, INFORMS, vol. 8(2), pages 107-128.
    6. Brodie, Roderick J. & Bonfrer, Andre, 1994. "Conditions when market share models are useful for forecasting: further empirical results," International Journal of Forecasting, Elsevier, vol. 10(2), pages 277-285, September.
    7. John D. C. Little, 1970. "Models and Managers: The Concept of a Decision Calculus," Management Science, INFORMS, vol. 16(8), pages 466-485, April.
    8. Leeflang, P.S.H. & Wittink, Dick R., 2000. "Building models for marketing decisions: past, present and future," Research Report 00F20, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    9. Fok, Dennis & Franses, Philip Hans, 2001. "Forecasting market shares from models for sales," International Journal of Forecasting, Elsevier, vol. 17(1), pages 121-128.
    10. 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.
    11. van Oest, R.D. & Franses, Ph.H.B.F., 2003. "Which brands gain share from which brands? Inference from store-level scanner data," ERIM Report Series Research in Management ERS-2003-076-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.
    12. M. L. Vidale & H. B. Wolfe, 1957. "An Operations-Research Study of Sales Response to Advertising," Operations Research, INFORMS, vol. 5(3), pages 370-381, June.
    13. repec:dgr:rugsom:00f20 is not listed on IDEAS
    14. 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.
    15. Danaher, Peter J., 1994. "Comparing naive with econometric market share models when competitors' actions are forecast," International Journal of Forecasting, Elsevier, vol. 10(2), pages 287-294, September.
    16. Barnett, Arnold Irvin., 1975. "More on a market share theorem," Working papers 774-75., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    17. John D. C. Little, 1975. "BRANDAID: A Marketing-Mix Model, Part 2: Implementation, Calibration, and Case Study," Operations Research, INFORMS, vol. 23(4), pages 656-673, August.
    18. Marnik G. Dekimpe & Dominique M. Hanssens, 1995. "Empirical Generalizations About Market Evolution and Stationarity," Marketing Science, INFORMS, vol. 14(3_supplem), pages 109-121.
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    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • M39 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Other

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