Research and Modelling of the Google AdSense Business Tool in the Context of Selected Website Metrics Using a Machine Learning Approach
Silvia KOMARA – Marián ČVIRIK– Martin KUCHTA
https://doi.org/10.18267/pr.2026.vol.2587.16
Abstract: This paper contributes to the field of digital business marketing by focusing on the factors that lead websites to adopt Google AdSense. The aim is to examine the use of Google AdSense in the context of selected website metrics using a machine learning approach. We focus in particular on how traffic characteristics and user behaviour are reflected in monetisation strategies. We incorporate the SHapley Additive exPlanations method into our analysis, which allows us to go beyond the usual measures of feature importance. We gain a more detailed view of how individual web traffic indicators complement each other and jointly contribute to monetisation results. We can see not only which variables are important to the model but also how they contribute to predicting AdSense adoption in specific cases. This gives us a better understanding of the interactions behind the decision to deploy an advertising platform and allows us to link them to practical website monetisation issues.
Keywords: Google AdSense, business intelligence, machine learning, customer journey, websites metrics
JEL Classification codes: M15, M31, C10
Fulltext: PDF
Published by: Prague University of Economics and Business, Oeconomica Publishing House
Year of publication: 2026
Online publication date: 20 May 2026
Copyright: Authors of the papers
ISBN 978-80-245-2587-7
ISSN 2453-6113
Pages 189-200