Cross-channel attribution modelling

This bachelor thesis focuses on attribution in the context of online marketing and studies available evaluation models for the performance of advertising campaigns. This performance is measured on the basis of the campaigns' effectiveness in catching the attention of customers and generating revenue. Data containing information about users' interactions with real advertising campaigns were used for the analysis. Prior to solving the attribution problem, data from the AdForm platform were cleansed and transformed into the required structures. This process is automated by the ETL tool called Keboola. Afterwards, data are analyzed using various attribution modelling techniques such as simple heuristics, the Shapley Value or Markov chains. The thesis discusses the theoretical side, as well as the actual application of these models. In the last section, the results of individual models are interpreted, taking into account the campaign costs. The interpretation is performed in the Tableau visualization software, using metrics such as the return on advertisement spending. This thesis presents a critical assessment of attribution models based on predetermined criteria. A scheme of data transformations, which can be used for future analyses of advertisement campaigns, was also created as part of this work. The thesis further includes a chapter discussing issues potentially leading to inaccuracies in the models' results.