Digital Advertising in a Post-Cookie World: Charting the Impact of Google's Topics API
- URL: http://arxiv.org/abs/2409.14073v1
- Date: Sat, 21 Sep 2024 09:04:16 GMT
- Title: Digital Advertising in a Post-Cookie World: Charting the Impact of Google's Topics API
- Authors: Jesús Romero, Ángel Cuevas, Rubén Cuevas,
- Abstract summary: Integrating Google's Topics API into the digital advertising ecosystem represents a significant shift toward privacy-conscious advertising practices.
This article analyses the implications of implementing Topics API on ad networks, focusing on competition dynamics and ad space accessibility.
- Score: 0.38233569758620056
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Integrating Google's Topics API into the digital advertising ecosystem represents a significant shift toward privacy-conscious advertising practices. This article analyses the implications of implementing Topics API on ad networks, focusing on competition dynamics and ad space accessibility. Through simulations based on extensive datasets capturing user behavior and market share data for ad networks, we evaluate metrics such as Ad Placement Eligibility, Low Competition Rate, and solo competitor. The findings reveal a noticeable impact on ad networks, with larger players strengthening their dominance and smaller networks facing challenges securing ad spaces and competing effectively. Moreover, the study explores the potential environmental implications of Google's actions, highlighting the need to carefully consider policy and regulatory measures to ensure fair competition and privacy protection. Overall, this research contributes valuable insights into the evolving dynamics of digital advertising and highlights the importance of balancing privacy with competition and innovation in the online advertising landscape.
Related papers
- SOMONITOR: Explainable Marketing Data Processing and Analysis with Large Language Models [43.28262218695844]
We introduce an explainable AI framework SoMonitor.
SoMonitor aims to synergize human intuition with AI-based efficiency.
It helps marketers across all stages of the marketing funnel.
arXiv Detail & Related papers (2024-07-18T02:55:52Z) - Click Without Compromise: Online Advertising Measurement via Per User Differential Privacy [22.38999810583601]
We introduce Ads-BPC, a novel user-level differential privacy protection scheme for advertising measurement results.
Ads-BPC achieves a 25% to 50% increase in accuracy over existing streaming DP mechanisms applied to advertising measurement.
arXiv Detail & Related papers (2024-06-04T16:31:19Z) - Social Dynamics of Consumer Response: A Unified Framework Integrating Statistical Physics and Marketing Dynamics [0.0]
This study examines the complex nature of consumer behaviour by applying theoretical frameworks derived from physics and social psychology.
We present an innovative equation that captures the relation between spending on advertising and consumer response, using concepts such as symmetries, scaling laws, and phase transitions.
arXiv Detail & Related papers (2024-04-01T11:23:31Z) - Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances [76.34037366117234]
We introduce a new dataset called Robot Control Gestures (RoCoG-v2)
The dataset is composed of both real and synthetic videos from seven gesture classes.
We present results using state-of-the-art action recognition and domain adaptation algorithms.
arXiv Detail & Related papers (2023-03-17T23:23:55Z) - Persuasion Strategies in Advertisements [68.70313043201882]
We introduce an extensive vocabulary of persuasion strategies and build the first ad image corpus annotated with persuasion strategies.
We then formulate the task of persuasion strategy prediction with multi-modal learning.
We conduct a real-world case study on 1600 advertising campaigns of 30 Fortune-500 companies.
arXiv Detail & Related papers (2022-08-20T07:33:13Z) - Personality-Driven Social Multimedia Content Recommendation [68.46899477180837]
We investigate the impact of human personality traits on the content recommendation model by applying a novel personality-driven multi-view content recommender system.
Our experimental results and real-world case study demonstrate not just PersiC's ability to perform efficient human personality-driven multi-view content recommendation, but also allow for actionable digital ad strategy recommendations.
arXiv Detail & Related papers (2022-07-25T14:37:18Z) - AI-Driven Contextual Advertising: A Technology Report and Implication
Analysis [0.0]
Programmatic advertising consists in automated auctioning of digital ad space.
The interest in contextual advertising is in part a counterreaction to the current dependency on personal data.
Developments in Artificial Intelligence (AI) allow for a deeper semantic understanding of context.
arXiv Detail & Related papers (2022-05-02T13:44:58Z) - Aggregate effects of advertising decisions: a complex systems look at
search engine advertising via an experimental study [26.218512292529635]
We develop and validate a simulation framework that supports assessments of various advertising strategies and estimations of the impact of mechanisms on the search market.
We conduct three experiments on the aggregate impact of electronic word-of-mouth, the competition level, and strategic bidding behaviors.
arXiv Detail & Related papers (2022-03-04T09:16:15Z) - Lessons from the AdKDD'21 Privacy-Preserving ML Challenge [57.365745458033075]
A prominent proposal at W3C only allows sharing advertising signals through aggregated, differentially private reports of past displays.
To study this proposal extensively, an open Privacy-Preserving Machine Learning Challenge took place at AdKDD'21.
A key finding is that learning models on large, aggregated data in the presence of a small set of unaggregated data points can be surprisingly efficient and cheap.
arXiv Detail & Related papers (2022-01-31T11:09:59Z) - Do Interruptions Pay Off? Effects of Interruptive Ads on Consumers
Willingness to Pay [79.9312329825761]
We present the results of a study designed to measure the impact of interruptive advertising on consumers willingness to pay for products bearing the advertiser's brand.
Our results contribute to the research on the economic impact of advertising, and introduce a method of measuring actual (as opposed to self-reported) willingness to pay in experimental marketing research.
arXiv Detail & Related papers (2020-05-14T09:26:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.