Description
Compared with online advertising industry, there is an even faster increase of ad blocker usage, which influence badly on publishers’ and advertisers’ business. Thus more and more companies initialize their counter-ad blocking strategies, in which customers choose to either disable their ad blockers or leave without seeing the content. There are also companies which abandon their counter-ad blocking strategies after conducting them for a while due to insufficient understanding of users’ ad blocking behavior. In this study, we employed a quasi-experiment framework and collected a large-size data with the cooperation with Forbes Media. We aim to identify factors influencing ad blocker usage. Furthermore, we will model the interaction effects among user profile, online behavior patterns, device features on ad blocker usage propensity. Our study contributes the literature of understanding ad blocker usage by evaluating those principles using big amount of real-world data.
Recommended Citation
Zhao, Shuai; Wang, Chong; Kalra, Achir; Vaks, Leon; Borcea, Cristian; and Chen, Yi, "Ad Blocking and Counter-Ad Blocking: Analysis of Online Ad Blocker Usage" (2017). AMCIS 2017 Proceedings. 29.
https://aisel.aisnet.org/amcis2017/DataScience/Presentations/29
Ad Blocking and Counter-Ad Blocking: Analysis of Online Ad Blocker Usage
Compared with online advertising industry, there is an even faster increase of ad blocker usage, which influence badly on publishers’ and advertisers’ business. Thus more and more companies initialize their counter-ad blocking strategies, in which customers choose to either disable their ad blockers or leave without seeing the content. There are also companies which abandon their counter-ad blocking strategies after conducting them for a while due to insufficient understanding of users’ ad blocking behavior. In this study, we employed a quasi-experiment framework and collected a large-size data with the cooperation with Forbes Media. We aim to identify factors influencing ad blocker usage. Furthermore, we will model the interaction effects among user profile, online behavior patterns, device features on ad blocker usage propensity. Our study contributes the literature of understanding ad blocker usage by evaluating those principles using big amount of real-world data.