Abstract
By reviewing the literature we developed principles to guide market analysts in their use of econometric models to forecast market share. We rely on the general principles for econometric forecasting developed by Allen and Fildes (2001) to arrive at specific principles. The theoretical and empirical evidence indicates that they should use econometric market share models when
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1.
effects of current marketing activity are strong relative to the carryover effects of past marketing activity,
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2.
there are enough observations,
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3.
the models allow for variation in response for individual brands,
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4.
the models are estimated using disaggregate (store-level) data rather than aggregate data,
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5.
the data exhibit enough variability, and
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6.
competitors’ actions can be forecast with reasonable accuracy.
In most situations the first five conditions can be satisfied. Condition 6 is more difficult to satisfy and is a priority area for further research. If one or more of the conditions are not satisfied then an extrapolation or judgment forecasting method may be more appropriate.
Keywords
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References
Aaker, D. A. and R. Jacobson (1987), “The sophistication of `naïve’ modelling,” International Journal of Forecasting, 3, 449–451.
Agrawal, D. and C. Schorling (1996), “Market share forecasting: An empirical comparison of artificial networks and multinomial logit model,” Journal of Retailing, 72, 383–407.
Allen, P. G. and R. Fildes (2001), “Econometric forecasting” in J. S. Armstrong (ed.), Principles of Forecasting. Norwell, Mass: Kluwer Academic Publishers.
Alsem, K. J. and P. S. H. Leeflang (1994), “Predicting advertising expenditure using intention surveys,” International Journal of Forecasting, 10, 327–337.
Alsem, K. J., P. S. H. Leeflang and J. C. Reuyl (1989), “The forecasting accuracy of market share models using predicted values of competitive marketing behaviour,” International Journal of Research in Marketing, 6, 183–198.
Armstrong, J. S. (2001), “Selecting forecasting methods,” in J. S. Armstrong (ed.), Principles of Forecasting. Norwell, Mass: Kluwer Academic Publishers.
Armstrong, J. S. and R. J. Brodie (1999), “Forecasting for marketing,” in G. Hooley and M. Hussey (ed), Quantitative Methods in Marketing. International Thomson Business Press, 2nd ed., pp. 18–40.
Bell, D. E., R. L, Keeney and J. D. C. Little (1975), “A market share theorem,” Journal of Marketing Research, 12, 136–141.
Brodie, R. J. and A. Bonfrer (1994), “Conditions when market share models are useful for forecasting: Further empirical results,” International Journal of Forecasting, 10, 277–285.
Brodie, R. J., A. Bonfrer and J. Cutler (1996), “Do managers overreact to each others’ promotional activity? Further empirical evidence,” International Journal of Research in Marketing, 13, 379–387.
Brodie, R. and C. A. de Kluyver (1984), “Attraction versus linear and multiplicative market share models: An empirical evaluation,” Journal of Marketing Research, 21, 194–201.
Brodie, R. and C. A. de Kluyver (1987), “A comparison of the short term forecasting accuracy of econometric and naive extrapolation models of market share,” International Journal of Forecasting,3, 423–437.
Cattin, P. and D. Wittink (1982), “Commercial use of conjoint analysis: A survey,” Journal of Marketing, 46, 44–53.
Chatfield, C. (1993), “Neural networks: Forecasting breakthrough or passing fad?” International Journal of Forecasting, 9, 1–3.
Chen, Y., V. Kanetkar and D. L. Weiss (1994), “Forecasting market share with dissagregate or pooled data: A comparison of attraction models,” International Journal of Forecasting, 10, 263–276.
Christen, M., S. J. C. Gupta, R. Porter, R. Staelin and D.R. Wittink (1997), “Using market-level data to understand promotion effects in a nonlinear model,” Journal of Marketing Research, 24, 322–334.
Cooper, L. G. (1993), “Market-share models,” in J. Eliashberg and G. L. Lilien (eds.), Handbooks on Operations, Research and Management Science: Marketing. Amsterdam: North Holland, pp. 259–314.
Cooper, L. G. and M. Nakanishi (1988), Market Share Analysis: Evaluating Competitive Marketing Effectiveness. Norwell, MA: Kluwer Academic Publishers.
Danaher, P. J. (1994), “Comparing naive and econometric market share models when competitors’ actions are forecast,” International Journal of Forecasting, 10, 287–294.
Danaher, P. J. and R. J. Brodie (1992), “Predictive accuracy of simple versus complex econometric market share models: Theoretical and empirical results,” International Journal of Forecasting, 8, 613–626.
Foekens, E. W., P. S. H. Leeflang and D. R. Wittink (1994), “A comparison and an exploration of the forecasting accuracy of a loglinear model at different levels of aggregation,” International Journal of Forecasting, 10, 245–261.
Foekens, E. W., P. S. H. Leeflang and D. R. Wittink (1997), “Hierarchical versus other market share models for many market items, ” International Journal of Research in Marketing, 14, 359–378.
Foekens, E. W., P. S. H. Leeflang and D. R. Wittink (1999), “Varying parameter models to accommodate dynamic promotion effects,” Journal of Econometrics, 89, 249–268.
Ghosh, A., S. Neslin and R. Shoemaker (1984), “A comparison of market share models and estimation procedures,” Journal of Marketing Research, 21, 202–210.
Hagerty, M. R. (1987), “Conditions under which econometric models will outperform naïve models,” International Journal of Forecasting, 3, 457–460.
Hanssens, D. M., L J. Parsons and R. L. Schultz (1990), Market Response Models: Econometric and Time Series Models. Norwell, MA: Kluwer Academic Publishers.
Jex, C. F. (1994), “Recursive estimation as an aid to exploratory data analysis an application market share models,” International Journal of Forecasting, 10, 445–454.
Kmenta, J. (1986), Elements of Econometrics, 2nd ed. New York: Macmillan.
Kumar, V. (1994), “Forecasting performance of market share models: An assessment, additional insights, and guidelines,” International Journal of Forecasting, 10, 295–312.
Kumar, V. and T. B. Heath (1990), “A comparative study of market share models using disaggregate data,” International Journal of Forecasting, 6, 163–174.
Kumar, A., V. R. Rao and H. Soni (1995), “An empirical comparison of neural networks and logistic regression models,” Marketing Letters, 6, 251–264.
Lawrence, K., M. Geurts and I. R. Parket (1990), “Forecasting market share using a combination of time series data and explanatory variables: A tutorial,” Journal of Statistical Computation and Simulation, 36, 247–253.
Leeflang, P. S. H. and J. C. Reuyl (1984), “On the predictive power of market share attraction models, Journal of Marketing Research, 21, 211–215.
Leeflang, P. S. H. and D. R. Wittink (1992), “Diagnosing competitive reactions using (aggregated) scanner data,” International Journal of Research in Marketing, 9, 39–57.
Leeflang, P. S. H. and D. R. Wittink (1996), “Competitive reaction versus consumer response: Do managers overreact?” International Journal of Research in Marketing, 13, 103–119.
Naert, P. A. and M. Weverbergh (1981), “On the predictive power of market share attraction models, Journal of Marketing Research, 18, 146–153.
Naert, P. A. and M. Weverbergh (1985), “Market share specification, estimation, and validation: Toward reconciling seemingly divergent views,” Journal of Marketing Research, 22, 453–461.
Singer, A. and R. J. Brodie (1990), “Forecasting competitors’ actions: An evaluation of alternative ways of analyzing business competition,” International Journal of Forecasting, 6, 75–88.
Steckel, J. H. and W. R. Vanhonacker (1993), “Cross-validating regression models in marketing research,” Marketing Science, 12, 415–427.
West, P. M., P. L. Brockett and L. L. Golden (1997), “A comparative analysis of neural networks and statistical methods for predicting consumer choice,” Marketing Science, 16, 370–391.
Wittink, D. R. (1987), “Casual market share models in marketing. Neither forecasting nor understanding,” International Journal of Forecasting, 3, 445–448.
Zellner, A. (1962), “An efficient method of estimating seemingly unrelated regressions and tests for aggregate bias,” Journal of the American Statistical Association, 57, 348–368.
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Brodie, R.J., Danaher, P.J., Kumar, V., Leeflang, P.S.H. (2001). Econometric Models for Forecasting Market Share. In: Armstrong, J.S. (eds) Principles of Forecasting. International Series in Operations Research & Management Science, vol 30. Springer, Boston, MA. https://doi.org/10.1007/978-0-306-47630-3_27
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