Attribution of Customers’ Actions Based on Machine Learning Approach

Timur Kadyrov and Dmitry Ignatov ()

MPRA Paper from University Library of Munich, Germany

Abstract: A multichannel attribution model based on gradient boost-ing over trees is proposed, which was compared with the state of theart models: bagged logistic regression, Markov chains approach, shapelyvalue. Experiments on digital advertising datasets showed that the pro-posed model is better than the solutions considered by ROC AUC metric.In addition, the problem of probability prediction of conversion by theconsumer using the ensemble of the analyzed algorithms was solved,the meta-features obtained were enriched with consumers and offlineactivities of the advertising campaign data.

Keywords: Multi-touch attribution; Gradient boosting; Digital advertising; Data-driven marketing (search for similar items in EconPapers)
JEL-codes: C45 M31 (search for similar items in EconPapers)
Date: 2019-09-23, Revised 2019-09-23
New Economics Papers: this item is included in nep-big and nep-ore
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/97312/1/project1.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:97312

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().