Evaluating the feasibility of supervised machine learning models for multi-touch attribution.

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Keywords	Data Science; Bagged Logistic Regression Model; Random Forest; Recurrent Neural Network; Long Short-Term Memory; Customer Journey Analysis; Marketing Analytics; Heuristic Models; Data-Driven Models; Single-Touch Attribution; Multi-Touch Attribution; First-Touch Attribution; Last-Touch Attribution; Linear Attribution; Time-Decay Attribution; SHAP Values; Markov Model; Performance Metrics; E-commerce Dataset; Multi-Channel Attribution.
Thesis Advisor	Gruber, K
Persistent URL	hdl.handle.net/2105/72540
Series	Business Economics
Organisation	Erasmus School of Economics
Citation APA APA Style APA-ALL Style AAA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Tol, D.S. van. (2024). Evaluating the feasibility of supervised machine learning models for multi-touch attribution. In Business Economics.http://hdl.handle.net/2105/72540

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