AI expected to impact advertising technology

Artificial intelligence (AI) could be set to impact significantly on advertising and marketing, according to expert opinion. Opportunities to personalise content to meet the ever-evolving demands of digital-first readers comes in the form of semantic technologies such as advanced taxonomies. Backed by open-standards schemes like those published by Google's Schema.org, using AI to tag and categorise content would enable publishers to provide an advertising exchange with more granular detail, while advertisers could achieve greater precision in target marketing based on specific article content.

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Today, it seems like there is little AI is not going to “revolutionise”, and advertising is no exception. Only last month the CMO of Unilever, speaking at Davos, described AI as a “huge wave” coming down on advertising and marketing, making a big impact on the way brands interact with customers and prospects.

For veteran marketers however, it is easy to dismiss this as old news: internet-related technologies have been promising ever-greater advertising personalisation for years. Now is a good time to delve into greater detail about where today’s (and tomorrow’s) adtech developments are stemming from, and what their implications are.

Categorisation is good, but is it enough?

Companies such as the BBC and the Financial Times are becoming more interested in personalising their content in order to meet the changing desires of digital-first readers. What this requires is more accurate categorisation of content so that it can be better matched with the right reader.

The same rules that apply to publishers also apply to advertisers: advertising is just content by another name after all. If advertisers want to match their ads to the right readers as accurately as possible, they’ve got to make sure they’ve tagged and categorised the content of those ads adequately.

The great thing about advertising and marketing is that these sectors already have a bit of a head start. Institutions such as the IAB have advanced taxonomies to categorise types of advertising segments, such as ‘Colesterol’ within ‘health and fitness’, or ‘fine art’ within ‘arts and entertainment’.

Organisations have expanded and amended these categorisations over time, which gives us a great platform from which to develop. However, as detailed as these categories are, they are imperfect, and can lead to inappropriate or insensitive ad placements – think ads for flights on a news page about a plane crash. In this scenario, advertisers would have to create specific rules barring this type of placement around the time of an air disaster, but clearly this model is not scalable if it requires constant human intervention. Categories have serviced advertising fine up until now, however they are simply not dynamic enough to keep up with the evolution of content online.

Semantics: bringing nuance to advertising

What’s needed is a more detailed and robust way of describing the digital landscape to the programmatic ad exchanges, and this is where semantics comes in. Semantic technologies are often misrepresented as AI for somewhat understandable reasons – these technologies allow machines to “understand” written content on a much more human level.

Suites of semantic technologies – such as our Publishing Platform – can now pair natural language processing with graph databases to allow computer databases to identify entities within sentences, read and understand grammatical relationships between those entities, and then, crucially, infer relationships between entities that the computer hasn’t been explicitly informed of yet. For example, the word ‘apple’ can have several different meanings: it can mean the fruit, the singer Fiona Apple, and the electronics company. Using a semantic graph database powered by natural language processing it is possible for a CMS to recognise the fact that these entities are separate, in other words, to disambiguate. Taking this further, semantics can also allow a CMS to infer that in a story with the phrase “earnings report” is relevant to Apple the company, whereas an article frequently using the word “nutrition” is relevant to apple the fruit.

This becomes highly relevant to advertising when it is applied to tagging and categorising content. Imagine a publisher is able to provide an ad exchange with granular detail of the context in which a particular ad will be displayed: exactly what topics are discussed in the articles, their timeliness and relevancy to other news articles and related topics. At the same time, the advertiser could also use this technology to tag their own ads with open standard languages that publishers would then be able to use to create an accurate fit between ad and impression. Advertisers would be able to target ads based on not just the content of an article, but on specific pieces of content within that article.

Open standards such as those published by Google-backed Schema.org show that this isn’t outlier technology but is already in wide adoption. This is how Google sees content on the web; if advertisers’ infrastructure could plug into this kind of nuance, advertisers would gain unheard of levels of control over placements.

The most arresting part of all this is that the technology is not even new: it’s here, it’s enterprise strength and is in active use in other verticals. More and more savvy companies are beginning to appreciate the potential of this technology. Buttressed by advertising-specific implementations, with legacy taxonomies and controlled vocabularies, don’t be surprised to see extremely fast uptake in the advertising space.