Application of Bayesian modelling & computations for media mix models

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Marketers seek to properly measure the effectiveness of different marketing channels. The purpose of this research report is to optimize a business’s marketing budget through a combination of Bayesian computations and Media Mix Models. A Bayesian framework approach helps incorporate prior knowledge within the model description, as opposed to the traditional Frequentist media mix modelling approach that fails to account for uncertainties that might occur within multiple advertisement channels. Most of the Media Mix Modelling (MMM) research conducted fails to incorporate the knowledge domain and account for the uncertainties within a business’s marketing channels before modelling. This research project attempts to solve the problem advertisers face when measuring the effectiveness of different marketing channels. The solution was achieved through the use of Bayesian methods for approximation, such as the Markov Chain Monte Carlo (MCMC), and media mix models for optimizing a marketing budget. Overall, we successfully applied Bayesian modelling to Media Mix Models for optimizing the marketing budget to generate the most sales. This was achieved by developing a Bayesian linear regression model that considers probability distributions, as opposed to training data alone. When compared to the Frequentist approach models, our Bayesian model provides reliable estimates and confidence intervals for sales based on the funds allocated to multiple marketing channels because it accounts for uncertainties and prior knowledge. The Bayesian media mix model was evaluated using Mean Square Error (MSE) and Mean Absolute Error (MAE) metrics to ensure the outcomes were reliable when compared to those generated by the Frequentist approach.
A research report submitted in partial fulfilment of the requirements for the degree Master of Science to the Faculty of Science, School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, 2023
Probabilistic programming, Business’s marketing, Bayesian computations