Profile Manager

The Model

Professor Kotler suggest that marketing managers evaluate models drawn with the same modellingtechnique applied by Profile Manager when they want to make explicit the individual effect variables exert on brand switching behaviour.

Professor Kotler assumes the characteristics of different brands are summarized, at time t, in a competitive marketing mix matrix similar to the one we use in the section Anatomy of a Report. The measures of relative attractiveness of each brand on one dimension are shown in the rows. The higher one row value the better that brand performs on that variable. If we were working with price, the highest relative attractiveness would be attributed to the lowest price.

Kotler calls brand probability purchase vector the market shares estimated with the model, which are obtained by multiplying the competitive marketing mix matrix and the marketing response vector. The resulting vector will add up to one and its outcome is influenced by (1) the relative attractiveness of the attributes and (2) the weight customers attach to each brand characteristic. Profile Manager uses the formula MMULT() for multiplying matrixes in Excel.

Among limitations, this model is linear and does not allow modelling interaction effects among variables. One of the benefits Kotler mentions is that it helps in finding better ways to scale relative awareness and attitudes toward brand differences.

The sensitivity analysis is run altering iteratively the content of the input data. Each input value of the matrix is set first to zero and then to 1, while other row values are rescaled accordingly. The shares obtained with the fictitious parameters are displayed in the columns Min and Max of the table below, and the column Max Min of the matrix shows the width of the impact each variable exerts on the estimated share.