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A comparison of approaches to exploit budget allocation data in cross-sectional maximum likelihood estimation of multi-attribute choice models

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  • González-Benito, Óscar
  • Santos-Requejo, Libia

Abstract

In this paper, four calibration approaches to exploit budget allocation data in maximum likelihood estimation of multi-attribute choice models are proposed. They differ on the implicit meaning of the dependent variable: (A) share of consumers according to the preferred alternative; (B) share of sales; (C) average share of consumer's budget; and (D) share of sales according to the preferred alternative. Differences between them can be conceived as depending on two circumstances: customer loyalty and customer selectivity. These are tested in the context of spatial consumer behavior, market response to hypermarket chains being represented as a function of their location strategies. Results show that different nuances on the definition of the dependent variable lead to slightly different relationships with the explanatory variables and to different predictive capabilities.

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  • González-Benito, Óscar & Santos-Requejo, Libia, 2002. "A comparison of approaches to exploit budget allocation data in cross-sectional maximum likelihood estimation of multi-attribute choice models," Omega, Elsevier, vol. 30(5), pages 315-324, October.
    • Handle: RePEc:eee:jomega:v:30:y:2002:i:5:p:315-324
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    1. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
    2. McCurley Hortman, Sandra & Allaway, Arthur W. & Barry Mason, J. & Rasp, John, 1990. "Multisegment analysis of supermarket patronage," Journal of Business Research, Elsevier, vol. 21(3), pages 209-223, November.
    3. Manrai, Ajay K., 1995. "Mathematical models of brand choice behavior," European Journal of Operational Research, Elsevier, vol. 82(1), pages 1-17, April.
    4. Cliquet, Gerard, 1995. "Implementing a subjective MCI model: An application to the furniture market," European Journal of Operational Research, Elsevier, vol. 84(2), pages 279-291, July.
    5. Marcel L. Corstjens & David A. Gautschi, 1983. "Formal Choice Models in Marketing," Marketing Science, INFORMS, vol. 2(1), pages 19-56.
    6. Borgers, Aloys & Timmermans, Harry, 1987. "Choice model specification, substitution and spatial structure effects : A simulation experiment," Regional Science and Urban Economics, Elsevier, vol. 17(1), pages 29-47, February.
    7. S Reader, 1993. "Unobserved Heterogeneity in Dynamic Discrete Choice Models," Environment and Planning A, , vol. 25(4), pages 495-519, April.
    8. Batsell, Richard R, 1980. "Consumer Resource Allocation Models at the Individual Level," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 7(1), pages 78-87, June.
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    1. Óscar González-Benito & César Bustos-Reyes & Pablo Muñoz-Gallego, 2007. "Isolating the geodemographic characterisation of retail format choice from the effects of spatial convenience," Marketing Letters, Springer, vol. 18(1), pages 45-59, June.
    2. Gonzalez-Benito, Oscar, 2005. "Spatial competitive interaction of retail store formats: modeling proposal and empirical results," Journal of Business Research, Elsevier, vol. 58(4), pages 457-466, April.

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