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 Style AAA Style APA 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, September 12). Evaluating the feasibility of supervised machine learning models for multi-touch attribution.. Business Economics. Retrieved from http://hdl.handle.net/2105/72540

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