Users often perceive ads annoying (when they are unrelated to their interests), or on the other hand they find ads creepy or scary (when matched to their interests and activities). Assuming the recommendation systems will become able to better and better match ads to our interests, how will the users react?
Many apps and internet services are free-to-use. The cost of developing and running these services is paid by bringing advertisement to our attention. However, ads are often annoying an unrelated to our interests.
Hence, services collect more and more information about us to better target and personalize those ads. While research shows that personalized ads can be less annoying and more effective, they require the logging of our demographics and our behavior, such as our searches, the content we browse, or our whereabouts and movements.
Personalization has raised concerns: users worry about the personal data that is being used to create personalized ads. In previous studies, when given a choice, many people expressed hesitation to share information: their concerns of sharing personal data –such as browsing and location history– outweigh the perceived usefulness of personalized ads.
If such concerns persist, investing effort into further personalizing ads would not be worthwhile, as people would not accept them.
We therefore set out to explore the research question: “would people be willing to share their personal data in exchange for highly-personalized online ads?”
To answer this question, we conducted a so called Wizard-of-Oz deception study, i.e., a study in which we simulated a system that can generate highly-personalized ads. Our volunteers were exposed via a web browser to three different highly-personalized ads, designed by people who knew them well. They were made believe that the ads had been generated automatically by an Artificial Intelligence engine on the basis of their browsing & location history and/or personal traits.
The participants’ reactions were surprisingly favorable:
- in more than 50% of the cases, the ads triggered spontaneous positive emotional reactions;
- almost 90% of participants would share at least two of the three data sources with advertisers; and
- about 50% would share all data sources.
Our results provide evidence that highly-personalized ads may offset the concerns that people have about sharing their personal data. Thus further efforts in building increasingly personalized online ads would represent a worthwhile endeavor.
The work was presented at ACM UMAP ’17, the ACM International Conference on User Modelling, Adaptation and Personalization, which took place in July 2017 in Bratislava, Slovakia.
“OMG! How did it know that?” Reactions to Highly-Personalized Ads.
Aleksandar Matic, Martin Pielot, Nuria Oliver
UMAP ’17 (Adjunct): ACM International Conference on User Modelling, Adaptation and Personalization, 2017.