Optimising targeted advertising thanks to data science
Although the phasing out of third-party cookies will not necessarily go hand in hand with the disappearance of data and advertising performance, these radical changes in the world of digital advertising do mean that new strategies are required for targeted advertising. Data will have to be sought, understood, and deftly exploited to become a powerful driver for performance and innovation. Data science straddles the fields of information technology, customer service and mathematical modelling, seeking to build statistical models and foster machine learning. It therefore allows the exploration and analysis of raw data with the goal of converting it into relevant information that can address a business need, such as targeted advertising. Data science thus offers a concrete and tailored solution to the current issue of optimising targeted advertising.
A model based on artificial intelligence and machine learning
For several years now, advertising has been guided by artificial intelligence. Artificial intelligence allows pinpoint analyses which can tailor the targets and lead to a closer understanding of the contexts in which advertisements are shown. Thanks to the enormous advances in cloud computing offers, machine learning algorithms can be trained on bigger and bigger datasets, thereby continually increasing their performance. These models can then be used to automatically classify millions of lines of data in record time to allow decisions to be made in real time. This technology, when combined with NLP algorithms (natural language processing), is highly useful for the online advertisement market. Such self-learning systems allow both performance and investments in advertisements to be optimised while guaranteeing that the entity broadcasting the advertisement does so in a way that perfectly suits its image and its field. AI is driving targeted advertising into a new era, and the trend will only continue in light of the cookieless world to come.
The era of semantic targeting
Today, AI allows data to be enriched with “contextual”, or “semantic” metadata by combining them with an analysis of meaning and brand suitability based on editorial content. This approach throws the data into a new light, migrating from targeting based on personal data to targeting based on semantic data. In concrete terms, users will no longer be targeted based on their personal data or browsing history, but rather based on the immediate context around the content they are accessing. This type of targeting brings better results, as the message is tailored and is shown at the precise time when the user’s attention is most focussed on a specific subject. Furthermore, it can reach users with no restrictions in terms of format or browser and needs only attention data to function. Semantic targeting also has the advantage of bringing meaning back to advertisement. By associating the offer in the advertisement with the content of the website, the product or service shown takes on a new meaning and the editorial content is better received.
Cookieless and consentless targeting made possible by data science is bringing new opportunities for performance, and it means that a data-driven approach can be maintained without the use of third-party cookies. In the future, the “zero user data” approach will no longer be a mere alternative, but rather a recognised digital driver where the question of “who” will be addressed via an overall and contextual strategy.