• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.825-832
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• 2012

We propose a novel way of forecasting the market shares of several brands simultaneously in a multiplicative competitive interaction model, which uses kernel regression technique incorporated with kernel machine technique applied in support vector machines and other machine learning techniques. Traditionally, the estimations of the market share attraction model are performed via a maximum likelihood estimation procedure under the assumption that the data are drawn from a normal distribution. The proposed method is shown to be a good candidate for forecasting method of the market share attraction model when normal distribution is not assumed. We apply the proposed method to forecast the market shares of 4 Korean car brands simultaneously and represent better performances than maximum likelihood estimation procedure.

• Asia Marketing Journal
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• v.6 no.2
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• pp.55-68
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• 2004

After Nakanishi and Cooper(1982) suggested a way of transforming the complicated nonlinear MCI model into a simple linear form, the application of MCI model has been increased. However, the use of MCI model in Korea is quite limited. The goal of this paper is to demonstrate the practical application of MCI(Multiplicative Competitive Interaction) model to a consumer goods industry. MCI model is a form of the attraction model explaining the relation between marketing mix variables and market share. In this study, multiple sources of empirical data are incorporated in the model formulation stage. In the estimation process, the equity estimation is applied to solve the possible multi-collinearity problem among marketing mix variables. Results from the fitted model suggest meaningful managerial implications for the management of brand equity and the allocation of resources among marketing mix variables.