Analyzing and Testing Viewability Methods in an Advertising Network

Many of the current online businesses base completely their revenue models in earnings from online advertisement. A problematic fact is that according to recent studies more than half of display ads are not being detected as viewable. The International Advertising Bureau (IAB) has defined a viewable impression as an impression that at least 50% of its pixels are rendered in the viewport during at least one continuous second. Although there is agreement on this definition for measuring viewable impressions in the industry, there is no systematic methodologies on how it should be implemented or the trustworthiness of these methods. In fact, the Media Rating Council (MRC) announced that there are inconsistencies across multiple reports attempting to measure this metric. In order to understand the magnitude of the problem, we conduct an analysis of different methods to track viewable impressions. Then, we test a subset of geometric and strong interaction methods in a webpage registered in the worldwide ad-network ExoClick, which currently serves over 7 billion geo-targeted ads a day to a global network of 65000 web/mobile publisher platforms. We find that the Intersection Observer API is the method that detects more viewable impressions given its robustness towards the technological constraints that face the rest of implementations available. The motivation of this work is to better understand the limitations and advantages of such methods, which can have an impact at a standardisation level in online advertising industry, as well as to provide guidelines for future research based on the lessons learned.

[1]  N. Moray Attention in Dichotic Listening: Affective Cues and the Influence of Instructions , 1959 .

[2]  J. Duncan,et al.  Visual search and stimulus similarity. , 1989, Psychological review.

[3]  Jan Panero Benway,et al.  Banner Blindness: The Irony of Attention Grabbing on the World Wide Web , 1998 .

[4]  Heike Schaumburg,et al.  Why Are Users Banner-Blind? The Impact of Navigation Style on the Perception of Web Banners , 2006, J. Digit. Inf..

[5]  Michelle E. Bayles,et al.  Designing online banner advertisements: should we animate? , 2002, CHI.

[6]  Naveen Donthu,et al.  The Impact Of Content And Design Elements On Banner Advertising Click-Through Rates , 2003 .

[7]  Guy W. Mullarkey,et al.  Factors Affecting Online Advertising Recall: A Study of Students , 2003, Journal of Advertising Research.

[8]  X. Drèze,et al.  Internet advertising: Is anybody watching? , 2003 .

[9]  Mohamed Saber Chtourou,et al.  Effects of configuration and exposure levels in responses to web advertisements , 2003, Journal of Advertising Research.

[10]  Sriram Kalyanaraman,et al.  AROUSAL, MEMORY, AND IMPRESSION-FORMATION EFFECTS OF ANIMATION SPEED IN WEB ADVERTISING , 2004 .

[11]  W. Stroebe,et al.  Beyond Vicary's fantasies: The impact of subliminal priming and brand choice , 2006 .

[12]  Ying Li,et al.  First International Workshop on Data Mining and Audience Intelligence for Advertising , 2007, KDD '07.

[13]  Chris Hand,et al.  Internet advertising effectiveness , 2007 .

[14]  Linda M. Gallant,et al.  Emerging Trends in Online Advertising , 2007 .

[15]  Przemyslaw Kazienko,et al.  AdROSA - Adaptive personalization of web advertising , 2007, Inf. Sci..

[16]  Andrea Everard,et al.  The effects of online advertising , 2007, Commun. ACM.

[17]  Louisa Ha,et al.  Online Advertising Research in Advertising Journals: A Review , 2008 .

[18]  David S. Evans The Economics of the Online Advertising Industry , 2008 .

[19]  Michael D. Smith,et al.  Location, Location, Location: An Analysis of Profitability of Position in Online Advertising Markets , 2008 .

[20]  Yu Pricing of Online Advertising : Cost-per-Click-through vs . Cost-per-Action , 2009 .

[21]  R. Porcher,et al.  P Value and the Theory of Hypothesis Testing: An Explanation for New Researchers , 2010, Clinical orthopaedics and related research.

[22]  Zhulei Tang,et al.  Pricing of Online Advertising: Cost-Per-Click-Through Vs. Cost-Per-Action , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[23]  Kuo-Hsiang Chen,et al.  How different information types affect viewer's attention on internet advertising , 2011, Comput. Hum. Behav..

[24]  Matthew Richardson,et al.  Predictive client-side profiles for personalized advertising , 2011, KDD.

[25]  Mohammad Esmaeil Ansari,et al.  An Investigation of TV Advertisement Effects on Customers' Purchasing and their Satisfaction , 2011 .

[26]  Kai-Christoph Hamborg,et al.  The effect of banner animation on fixation behavior and recall performance in search tasks , 2012, Comput. Hum. Behav..

[27]  M. McHugh Interrater reliability: the kappa statistic , 2012, Biochemia medica.

[28]  Ana Margarida Barreto Do users look at banner ads on Facebook , 2013 .

[29]  Ryan Stevens,et al.  MAdFraud: investigating ad fraud in android applications , 2014, MobiSys.

[30]  Chong Wang,et al.  Viewability Prediction for Online Display Ads , 2015, CIKM.

[31]  Manfred Mareck Is Online Audience Measurement Coming of Age , 2015 .

[32]  Jun Wang,et al.  An Empirical Study on Display Ad Impression Viewability Measurements , 2015, ArXiv.

[33]  Anindya Ghose,et al.  Towards a Digital Attribution Model: Measuring the Impact of Display Advertising on Online Consumer Behavior , 2015 .

[34]  David Bounie,et al.  Advertising Viewability in Online Branding Campaigns , 2016 .

[35]  Kang Li,et al.  The impacts of banner format and animation speed on banner effectiveness: Evidence from eye movements , 2016, Comput. Hum. Behav..

[36]  Anindya Ghose,et al.  Towards a Digital Attribution Model: Measuring the Impact of Display Advertising on Online Consumer Behavior , 2015 .

[37]  Chong Wang,et al.  Probabilistic Models for Ad Viewability Prediction on the Web , 2017, IEEE Transactions on Knowledge and Data Engineering.

[38]  David Bounie,et al.  Do You See What I See? Ad Viewability and the Economics of Online Advertising , 2017 .

[39]  Thomas Steiner What is in a Web View: An Analysis of Progressive Web App Features When the Means of Web Access is not a Web Browser , 2018, WWW.

[40]  Rongbin Xu,et al.  Optimally Connected Deep Belief Net for Click Through Rate Prediction in Online Advertising , 2018, IEEE Access.

[41]  Hongxia Bie,et al.  Wide & ResNet: An Improved Network for CTR Prediction , 2018 .

[42]  Chong Wang,et al.  Webpage Depth Viewability Prediction Using Deep Sequential Neural Networks , 2019, IEEE Transactions on Knowledge and Data Engineering.

[43]  Yuping Liu-Thompkins,et al.  A Decade of Online Advertising Research: What We Learned and What We Need to Know , 2019, Journal of Advertising.

[44]  Tereza Semerádová,et al.  Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting , 2019, IEEE Access.

[45]  Ming Hong,et al.  Online ad effectiveness evaluation with a two-stage method using a Gaussian filter and decision tree approach , 2019, Electron. Commer. Res. Appl..

[46]  Xiaohui Zhao,et al.  Research on CTR Prediction Based on Deep Learning , 2019, IEEE Access.

[47]  Nilay Kanti Das,et al.  P-Value Demystified , 2019, Indian dermatology online journal.

[48]  Rubén Cuevas Rumin,et al.  Large-Scale Analysis of User Exposure to Online Advertising on Facebook , 2018, IEEE Access.

[49]  Yang Liu,et al.  A Revenue-Maximizing Bidding Strategy for Demand-Side Platforms , 2019, IEEE Access.

[50]  Kristen M. Liu The Attention Crisis of Digital Interfaces and How to Consume Media More Mindfully , 2019 .

[51]  Jordi Forné,et al.  Measuring Online Advertising Viewability and Analyzing its Variability Across Different Dimensions , 2020, WIMS.