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Is Automatic Video Classification a Silver Bullet for Advertiser’s Woes?

Is Automatic Video ...

Our friends (nay, partners) at the Internet Advertising Bureau UK asked Sam Kayum to weigh in on the increase in visual online content and how automated visual classification can be a game changer for advertisers.

As Chief Commercial Officer for the innovative web data classification company, WeSEE, he’s the perfect guy to write this compelling read!


Online video is nothing new for savvy brand advertisers; indeed, most marketing professionals worth their salt already incorporate it into their global marketing and advertising campaigns. Sites like YouTube as well as videos shared through social networks are one of the few areas of the Web that is experiencing an increase in dwell-time, which presents advertisers with a unique and effective means of reaching an engaged audience online. In fact, a recent Comscore report revealed that online video reached 34 million UK internet users in 2012, representing 80% of the total UK internet audience, with 64.1 per cent of audiences having been exposed to video ads.

However, as many video ads online are skippable, it means that advertisers have to ensure they are well-targeted and engaging. A recent survey we undertook revealed that of 1,000 UK consumers, a quarter are actively bothered by ads in video content, but that a third are willing to engage with online advertising if it introduces them to a new product or service that is relevant to their interest. These should be cause for thought around how advertisers can best deal with this influx of user-generated visual content in order to target ad placements appropriately and drive ROI.

The key to ROI is delivering the most relevant, targeted ads for each piece of content. In order for advertising to be impactful, it needs to be well tailored to the online image or video content alongside which the ad is being served. Until now, advertisers have relied on keyword tagging in order to place ads that are relevant to content. However, 70% of all video content online is deemed non-premium, much of which is user-generated. Relying on keyword tagging, therefore, is not always the most effective method of targeting ads, as keywords can be incorrect, misleading or incomplete and are unhelpful for real-time uploads and quickly changing content. Geo-targeting, behavioural, site audience targeting as well as 3rd party audience data is another important consideration in video advertising but similarly, it does not guarantee that targeted ad delivery either. Such targeting is useful, but without being able to properly identify content, it is incomplete at best.

As the amount of user-generated visual content grows, what can advertisers do to tailor ads accordingly and maintain click or view-through rates? How can they ensure that the ads served around this content are relevant? The answer is that today’s marketers must move ‘outside the keywords’ and look at new technologies cropping up in the space, such as automatic video classification. Its utility centres around being able to automatically identify and classify the visual content in order to provide relevant, targeted advertising, even if that content is user-generated and poorly tagged. Such technology removes much of the need for the (often erroneous) keyword tagging and can optimise ad placement, facilitating the success of campaigns.

Automatic visual classification and verification is posed to be a massive game-changer, not only in terms of its innovative capabilities to correctly identify and classify visual content online, but also by acting as a tool for brand advertisers looking to serve up companion ads based on what a user is viewing on television in real-time. With ‘second screening’ on the rise, there is great potential for brands and advertisers to leverage automatic visual classification to recognise visual TV programming using a mobile device and respond by providing targeted mobile advertisements in real-time. This capability may still be in its infancy, but it’s not unbelievable that this is a next stop on the evolution of automatic visual classification. The future is visual and offers many exciting opportunities for those brands who can act quickly to make the most of it.


Have you used automatic visual classifications? If so, please weigh in on the pros and cons!

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