Energy is today the most critical environmental challenge. The amount of carbon emissions contributing to climate change is significantly influenced by both the production and consumption of energy. Measuring and reducing the energy consumption of services is a crucial step toward reducing adverse environmental effects caused by carbon emissions. Millions of websites rely on online advertisements to generate revenue, with most websites earning most or all of their revenues from ads. As a result, hundreds of billions of online ads are delivered daily to internet users to be rendered in their browsers. Both the delivery and rendering of each ad consume energy. This study investigates how much energy online ads use in the rendering process and offers a way for predicting it as part of rendering the ad. To the best of the authors’ knowledge, this is the first study to calculate the energy usage of single advertisements in the rendering process. Our research further introduces different levels of consumption by which online ads can be classified based on energy efficiency. This classification will allow advertisers to add energy efficiency metrics and optimize campaigns towards consuming less possible.

Filter lists play a large and growing role in protecting and assisting web users. The vast majority of popular filter lists are crowd-sourced, where a large number of people manually label resources related to undesirable web resources (e.g. ads, trackers, paywall libraries), so that they can be blocked by browsers and extensions.

Viewability is one of the most important metrics used in ad-tech to measure the performance quality of ad campaigns. The viewability standard defines the visibility conditions an ad impression must meet to achieve a sufficient marketing effect to be considered viewed. The ad-tech industry offers opaque measures of viewability whose performance is questionable. To address this issue, we propose a novel methodology for measuring viewability in ad campaigns. The disclosure of the functional details of this technique makes it reproducible and auditable. Our solution has been deployed in production by a Demand Side Platform (DSP) to measure the viewability rate of the ad campaigns. Leveraging the infrastructure of this DSP, we compare the performance of our methodology with a commercial solution. Both techniques report a similar overall viewability rate of 50%. However, our solution measured the viewability in 93% of the ads served by the DSP, unlike to 74% of the ads measured by the commercial solution. A rough estimation indicates that this increase in the measured rate may lead to a revenue increase of $3.5 million per year for a mid-sized DSP serving 100M of ads per day.

Invalid ad traffic is an inherent problem of programmatic advertising that has not been properly addressed so far. Traditionally, it has been considered that invalid ad traffic only harms the interests of advertisers, which pay for the cost of invalid ad impressions while other industry stakeholders earn revenue through commissions regardless of the quality of the impression. Our first contribution consists of providing evidence that shows how the Demand Side Platforms (DSPs), one of the most important intermediaries in the programmatic advertising supply chain, may be suffering from economic losses due to invalid ad traffic. Addressing the problem of invalid traffic at DSPs requires a highly scalable solution that can identify invalid traffic in real time at the individual bid request level. The second and main contribution is the design and implementation of a solution for the invalid traffic problem, a system that can be seamlessly integrated into the current programmatic ecosystem by the DSPs. Our system has been released under an open source license, becoming the first auditable solution for invalid ad traffic detection. The intrinsic transparency of our solution along with the good results obtained in industrial trials have led the World Federation of Advertisers to endorse it.