Simply put, contextual targeting implies matching inventory with advertising demand based on the relevance of context (usually determined by a range of relevant keywords, topics, languages, and/or viewer’s location).
The Evolution of Contextual Targeting
Over the past years, contextual targeting has evolved from the basics of keyword targeting to a much more complex process, involving advanced machine learning algorithms.
In the video advertising context, however, contextual targeting is still a more challenging thing, simply because developing the taxonomy isn’t as easy, as in case with text content.
In this respect, given that contextual targeting perceives content somewhat as a proxy for the audience, the key to success is its granularity, the more the better.
Most analysts admit the future of contextual targeting is associated with intelligent predictive modeling and somewhat the real-time affinity ranking, which could potentially enable brands to deliver highly-relevant contextual content to their target audience.
Benefits of Contextual Targeting
The buzz around contextual targeting in digital advertising has been increasingly growing ever since the “upcoming death of cookies” was announced, for some obvious reasons.
The truth is, unlike in behavior advertising, deep learning algorithms used in contextual targeting don’t rely on Personal Data, especially when it comes to online activity data.
In fact, the main focus is to evaluate the precise context and so-to-speak, “read the signals” in real time, to train AI models more effectively, hence improving the precision of predictions on who is behind a device screen at a particular moment of time.
If handled properly, contextual targeting enables identifying video ad content resonant with a brand, hence enabling to amplify video ad performance, delivering engaging ad experience to viewers, in a brand safe, brand suitable environment.