Marketing Mix Modelling from the multiple regression perspective
Sammanfattning: The optimal allocation of the marketing budget has become a difficult issue that each company is facing. With the appearance of new marketing techniques, such as online advertising and social media advertising, the complexity of data has increased, making this problem even more challenging. Statistical tools for explanatory and predictive modelling have commonly been used to tackle the problem of budget allocation. Marketing Mix Modelling involves the use of a range of statistical methods which are suitable for modelling the variable of interest (in this thesis it is sales) in terms of advertising strategies and external variables, with the aim to construct an optimal combination of marketing strategies that would maximize the profit. The purpose of this thesis is to investigate a number of regression-based model building strategies, with the focus on advanced regularization methods of linear regression, with the analysis of advantages and disadvantages of each method. Several crucial problems that modern marketing mix modelling is facing are discussed in the thesis. These include the choice of the most appropriate functional form that describes the relationship between the set of explanatory variables and the response, modelling the dynamical structure of marketing environment by choosing the optimal decays for each marketing advertising strategy, evaluating the seasonality effects and collinearity of marketing instruments. To efficiently tackle two common challenges when dealing with marketing data, which are multicollinearity and selection of informative variables, regularization methods are exploited. In particular, the performance accuracy of ridge regression, the lasso, the naive elastic net and elastic net is compared using cross-validation approach for the selection of tuning parameters. Specific practical recommendations for modelling and analyzing Nepa marketing data are provided.
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