Contextual targeting is a form of addressing target groups in which advertising material is displayed on the basis of keywords or context. It is also known as ‘contextual advertising’ or ‘context targeting’, but is roughly categorised as semantic targeting.
As the name suggests, the context of content takes centre stage here, as the aim is to place ads in thematically appropriate places. The focus of these placements is therefore on what content is consumed and not on the nature of the target group.
If certain predefined keywords appear on a website, it classifies them as a potential advertising environment for the respective product. In the case of yoghurt, for example, articles with the keywords ‘healthy eating’ or ‘breakfast’ are thematically suitable environments. If users then read an article on these topics, they are presented with suitable adverts at the same time. When using contextual targeting, it is therefore assumed that users who consume certain content are also interested in this type of topic. From this, an interest in the product and the brand is derived at the same time.
Advantages of contextual targeting
As adverts that are placed via contextual targeting are thematically linked to the respective environment, they are generally perceived as natural and less disruptive. Flexibility is also important for media planning. Advertising media can be replaced very quickly and easily and content can be adapted to new contexts.
One of the main advantages of contextual targeting is also the fact that it works entirely without third-party cookies. As the adverts are not targeted at specific users, this technology can be dispensed with. This makes contextual targeting an important pillar of digital marketing in the future, as targeting via third-party cookies will be more difficult or even impossible in the future ‘post-cookie era’.
Contextual targeting and brand safety
Critics of contextual targeting have often noted in the past that adverts can sometimes be placed in negative contexts, i.e. in contexts that are unfavourable for a product. A car brand, for example, will have little interest in appearing in advertising environments that are thematically centred around accidents or the emissions scandal, for example. However, there are now ways to avoid this and thus increase brand safety. The easiest way is to set up a kind of ‘blacklist’ in addition to the list of positive keywords. This lists keywords next to which an advert must not appear under any circumstances. In the example just mentioned, terms to be excluded would be ‘accident’, ‘traffic jam’, ‘exhaust’, ‘pollution’ and so on. The more precise this list is, the better it ensures that the ad is placed in a positive advertising environment.
Using artificial intelligence, it is now also possible to analyse the mood and tone of a page. This means that we not only know, for example, that a page is about pets, but also whether bad experiences with them are at the centre of the page. Thanks to machine learning, advertisers can gradually move away from keywords and white or blacklists and instead identify relevant content in a suitable context using artificial intelligence.
Is the use of contextual targeting advisable?
Contextual targeting is an important addition to data-driven targeting approaches and should therefore not be neglected in the marketing mix. However, it only develops its full efficiency when it is combined with the use of valid user information. Only when information about the target group to be reached (and not just about the context of the platform) is available can it be ensured that the company speaks the language of the people targeted with the advert. Doctors or lawyers talk to each other differently than when laypeople are involved. This principle should not be ignored when creating and placing adverts.
In addition, the focus of contextual targeting is changing. Instead of just the context, semantics are increasingly taking centre stage. The semantic evaluation of an advertising environment promises greater targeting accuracy and at the same time expands the field of potentially suitable websites. A vegan product therefore does not only have to appear on sites that explicitly deal with veganism. The semantic interpretation of pages also opens up environments that do not have to contain the keyword, but are generally thematically appropriate. After all, vegans do not only visit sites that are centred around this preference. Semantic targeting picks up the target group where it is to be found, while at the same time ensuring that the page topic and advert content match perfectly.
The most effective approach is therefore always to combine different disciplines and options with one another in order to achieve maximum efficiency for the advert.
