Blog

IA, a Real Driver for Ad Performance?

Published on 16 Feb 2021

Digital advertising has often had to deal with many changes. This time, two major issues are at the forefront: the disappearance of third-party cookies and data confidentiality. And since every new phenomenon comes with many questions and challenges, ad targeting is no exception: how to maintain high performance levels with less or no user data? What kind of ad targeting to resort to while respecting privacy? Which long-term strategy to implement?
To accompany these transformations, solutions made possible by progress in artificial intelligence have emerged. Among them, semantic targeting, which, thanks to AI, makes it possible to emancipate from user data while maintaining high standards of advertising efficiency.

From user targeting to hyper-contextualization

There are many fields of application for AI and we find it in many aspects of everyday life: chatbots, voice-controlled connected objects, image recognition… the possibilities are endless. Although artificial intelligence is not a recent concept, the improvements in computing capacity that occurred some fifteen years ago with the emergence of GPUs (Graphics Processing Units), which capacities are much greater than those of traditional CPUs (Central Processing Units), have made it possible to transform huge volumes of data into insights which is a real asset.

The world of digital advertising, which possesses and exploits a large amount of data, both contextual and personal, has naturally benefited greatly from AI progresses in recent years. The emergence of Deep Learning with the use of GPUs has made it possible to overcome the limits of Machine Learning. Deep Learning, which makes it possible to process much larger volumes of data, thanks to a neural network approach that looks for patterns and correlations, has made it possible to develop less “directed” algorithms, thus reducing human bias.

Deep Learning has contributed to the analysis of editorial content and brand suitability of each of these web pages. This analyzed data, known as semantic data, is then used to carry out ad targeting in a controlled delivery context. Millions of web pages are thus analyzed to define a precise classification of contents. The resulting multi-scoring makes it possible to identify the interconnection of various subjects within the same editorial content. This hyper-contextualization is made possible using NLU (Natural Language Understanding) algorithms that can understand the exact sense of a web page, thus providing a clearer and more granular vision of the delivery contexts.

This solution not only frees you from the use of any personal data but also offers a better memorization of the advertising message pushed in a relevant delivery context. Artificial intelligence applied to semantic is then capable of targeting the user’s attention, unlike data user-based targeting, which focuses on intention.

Can the limits of IA be overcome?

Nevertheless, for this artificial intelligence to develop and optimize itself, a large volume of data is required. In a cookieless and consentless world where the available user data is decreasing, semantic data, which reflects the immediate interests of Internet users, will be worth its weight in gold for those who know how to exploit it.

An ad addressed to the right person at the right time, nothing new under the marketing sun. However, ultra-personalization is one of the new challenges of AI. The greater the volume of data analyzed, the more we will be able to understand user behavior and personalize the content of an advertising message. An ethical question arises: should all available data be used? The evolution of the legal framework, by reducing the amount of user data available, party answers this question. The evolution of the technological framework also implies that this collected data is less shared between the different players, and therefore its access is much more concentrated around the market giants. This is why AI applied to delivery contexts allows the creation of new ultra-personalized advertising strategies that do not depend on this disappearing data. No more contextual as we knew it in the early days of digital advertising. The high priestess of our time, artificial intelligence now makes it possible to understand complex subjects more precisely thanks to a fine and precise analysis, making semantic more than an alternative to the gradual disappearance of user data, but a real ad targeting strategy.

Today’s digital advertisement is faced with major challenges, but this does not mean that ad targeting is in danger. IA progress will increasingly allow us to favor semantic targeting over behavioral targeting. The players in the advertising industry must consider the opportunities offered by technology and be ready to adopt a more ethical philosophy to build the future of digital advertising without personal data.

Want to know more about cookieless targeting?

Geoffrey Berthon
CEO & Founder @Qwarry