Viewability prediction for display advertising

As a massive industry, display advertising delivers advertisers’ marketing messages to attract customers through graphic banners on webpages. Display advertising is also the most essential revenue source of online publishers. Currently, advertisers are charged by user response or ad serving. However, recent studies show that users barely click or convert display ads. Moreover, about half of the ads are actually never seen by users. In this case, advertisers cannot enhance their brand awareness and increase return on investment. Publishers also lose much revenue. Therefore, the ad pricing standards are shifting to a new model: ad impressions are paid if they are viewable, not just being responded to or served. The Media Ratings Council’s standard for a viewable display impression is a minimum of 50% of pixels in view for a minimum of one second. To implement viewable impressions as pricing currency, ad viewability should be accurately predicted. Ad viewability prediction can improve the performance of guaranteed ad delivery, real-time bidding, as well as recommender systems. This research is the first to address this important problem of ad viewability prediction. Inspired by the standard definition of viewability, this study proposes to solve the problem from two angles: 1) scrolling behavior and 2) dwell time. In the first phase, ad viewability is predicted by estimating the probability that a user will scroll to the page depth where an ad is located in a specific page view. Two novel probabilistic latent class models (PLC) are proposed. The first PLC model computes constant use and page memberships offline, while the second PLC model computes dynamic memberships in real-time. In the second phase, ad viewability is predicted by estimating the probability that the page depth will be in-view for certain seconds. Machine learning models based on Factorization Machines (FM) and Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) are proposed to predict the viewability of any given page depth in a specific page view. The experiments show that the proposed algorithms significantly outperform the comparison systems

A survey of the role of viewability within the online advertising ecosystem

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Within the online advertising ecosystem, viewability is defined as the metric that measures if an ad impression had the chance of being viewable by a potential consumer. Although this metric has been presented as a potential game-changer within the ad industry, it has not been fully adopted by the stakeholders, mainly due to disagreement between the different parties on the standards to implement and measure it, and its potential benefits and drawbacks. In this study, we present a survey of the role that viewability can have on the main challenges of the online advertising ecosystem depicting the main applications, benefits and issues. With this objective, we provide an overall picture of how viewability can fit within the ecosystem, which can help the different stakeholders to work on its adoption, integration and establishing a research agenda.This work was supported by the Plan de Doctorados Industriales de la Secretaría de Universidades e Investigación del Departamento de Empresa y Conocimiento de la Generalitat de Catalunya under the Grant 2018-DI-059 and by ExoClick. Furthermore, this work received support from the Fellowship through ‘‘la Caixa’’ Foundation under Grant ID100010434, from the European Union’s Horizon 2020 Research and Innovation Program through Marie Skłodowska-Curie Grant under Agreement 847648, from the Fellowship under Grant CF/BQ/PR20/11770009, from the Spanish Ministry of Economy and Competitiveness through Juan de la Cierva Formación Program under Grant FJCI-2017-34926 and from the Spanish Government under Grant PID2020-113795RB-C31 ‘‘COMPROMISE’’.Peer ReviewedPostprint (published version

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.This work was possible thanks to the support of “Plan de Doctorados Industriales de la Secretaría de Universidades e Investigación del Departamento de Empresa y Conocimiento de la Generalitat de Catalunya” and the Spanish Ministry of Economy and Competitiveness through the Juan de la Cierva Formación program (FJCI-2017-34926). We also want to thank ExoClick for their support in conducting thisresearchPeer ReviewedPostprint (published version

The construction of marketing measures: the case of viewability

This study seeks to develop a critical understanding of marketing measures. Marketing measures inform a variety of marketing practices and have been subject to ethical, discursive and epistemological critique. Informed by a range of theoretical work, this study sheds light on the construction of a key marketing measure in digital advertising: viewability. It shows how a range of competing interests can be mobilized and aligned; how an object of interest can be stabilized; and how standards for measurement can be reconciled. Across this account, we can see how issues of accuracy, ideology and ethics are bracketed off as participants agree on which things matter and which things count

Predicting Audio Advertisement Quality

Online audio advertising is a particular form of advertising used abundantly in online music streaming services. In these platforms, which tend to host tens of thousands of unique audio advertisements (ads), providing high quality ads ensures a better user experience and results in longer user engagement. Therefore, the automatic assessment of these ads is an important step toward audio ads ranking and better audio ads creation. In this paper we propose one way to measure the quality of the audio ads using a proxy metric called Long Click Rate (LCR), which is defined by the amount of time a user engages with the follow-up display ad (that is shown while the audio ad is playing) divided by the impressions. We later focus on predicting the audio ad quality using only acoustic features such as harmony, rhythm, and timbre of the audio, extracted from the raw waveform. We discuss how the characteristics of the sound can be connected to concepts such as the clarity of the audio ad message, its trustworthiness, etc. Finally, we propose a new deep learning model for audio ad quality prediction, which outperforms the other discussed models trained on hand-crafted features. To the best of our knowledge, this is the first large-scale audio ad quality prediction study.Comment: WSDM '18 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 9 page

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection

Study on ad metrics fraud: evolution, analysis, and mitigation tools

El objetivo de este trabajo es el análisis del fraude presente en las métricas que sirven como valor de referencia en la comercialización de la publicidad digital. Los medios digitales necesitan optimizar los ingresos captados y una de sus principales apuestas son los modelos de negocio basados en publicidad que se enfrentan al fenómeno del fraude. Este trabajo se centra en analizar los aspectos que frenan las inversiones publicitarias, especialmente los problemas que conlleva el fraude de métricas y las medidas que se implementan para mejorar la transparencia y la calidad de los medios como soportes publicitarios. Se utiliza una metodología cualitativa, basada en entrevistas en profundidad a profesionales del sector, que parten del análisis de los diferentes tipos de fraude y las estrategias de prevención llevadas a cabo por los soportes digitales. Los resultados revelan una desigualdad en la gestión y la adopción de una visión conservadora ante este fenómeno.This study seeks to analyse fraud in those metrics serving as a reference value in the commercialisation of digital advertising spaces. Digital media need to optimise revenue and a major recourse is the business models based on advertising facing the phenomenon of fraud. This work focuses on analysis of the aspects that deter advertising investments, especially the problems that metrics fraud entails, and measures implemented to improve the transparency and quality of media like the advertising media. It is based on the idea that the control of metric fraud makes it possible to attract the attention of advertisers, improve advertising efficiency and optimise the benefits of digital media. A qualitative methodology afforded in-depth interviews to professionals in the sector who analyse the different types of fraud and the prevention strategies carried out by digital media. The results reveal inequality in the management of investment in digital media for advertising and a conservative vision