Application of machine learning to link click predictions in Facebook Family of Apps advertising

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School of Business | Master's thesis
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Social media networks gather a major share of global marketing advertisement spend whilst simultaneously comprising a major share of time spent online by people with the means to participate in one. The marketing budgets globally are shifting towards digital where social media marketing is one of the key beneficiaries. One of the key metrics used to assess digital marketing effectiveness across all digital marketing mediums is clicks that has been investigated academically from several perspectives. A comparison of different supervised machine learning (ML) algorithms was performed to predict link clicks in Facebook Family of Apps advertising campaigns. Retrospectively collected data was from completed Facebook Family of Apps campaigns over the course of two years between February 2018 until February 2020 targeted to Finland. Four different ML algorithms were tested, including linear regression, decision tree, random forest, and artificial neural network (multi-layer perceptron). Estimation of predictive performance was done using repeated k-fold cross-validation. Comparison between different algorithms was done using root mean square error (RMSE) as the loss function and coefficient of determination (R-squared) as the model fit metric. The relative importances of the features were also estimated from the same cross-validation procedure through algorithm specific approaches. This study approached the prediction problem from the media buyer’s perspective through the use of features that are widely accessable to all the people working within this domain. The test results show that the artificial neural network algorithm performs the best out of the selected algorithms across the three different datasets producing both the smallest loss scores and highest R-squared values. The implications of the findings and suggestions for potential future studies are further discussed.
Thesis advisor
Gloukhotsev, Alexei
Jung, Alexander
digital marketing, social media marketing, supervised learning, machine learning
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