The return of contextual as the primary cookieless targeting alternative comes as no surprise. The difference between contextual and user-based is down to the intent of that user. Placing advertisements in environments where it is most relevant in terms of content vs. delivering advertisements to users based on their past behaviors. Both methods have their merits, but whether you realise it or not, you are targeted regularly in your daily life – fitness advertisements in the sports section of a newspaper or luggage ads on a travel blog. 

However, a lot of the time, brands overlook the importance of placing a contextually relevant ad over just following or reaching a user who has an interest in a particular category regardless of their environment. 

Contextual targeting has become more advanced with the introduction of machine learning – targeting is much more intelligence than just targeting “sports” content. Advanced targeting platforms now leverage multiple data points whereby elements such as sentiment as well as keyword and category can be taken into account. Users feel less “intruded” and “followed”, which reduces ad fatigue as rather than following a user around the Internet, brands can identify a relevant environment and deliver an ad within that content. 

Placing advertisements with higher relevance to the content of the page is also more natural, and less disruptive to the end user whilst increasing their engagement as they are in-market in that moment of time specifically. Not only this, but it increases brand suitability and avoids pitfalls of advertising mishaps between a brand and content.