With the growth of images and image-based social platforms, it becomes increasingly important to have a visual strategy in place. In a webinar hosted by AdWeek, our CMO Ben Plomion presents Seen & Unseen, discussing how computer vision addresses the growing needs of a visual marketing strategy, despite the challenges that make it difficult for marketers to take advantage of our increasingly visual landscape. You can watch the live recording of the webinar presentation below.

There are a number of technological and societal drivers that perpetuate the growth of visual content in the digital landscape. Between the increase in number of embedded cameras in the market, and the proliferation of social media platforms that rely on images to communicate concepts and information quickly, a visual strategy is becoming essential to harnessing the power of visual content. Leveraging computer vision as a tool, marketers are able to capture relevant visual content in scalable and brand safe ways.

A recent Digiday survey of over 300 industry stakeholders revealed that 80% described visual content as either “very” or “somewhat” important to their marketing strategy. In 2019 they have more avenues than ever through which to place that content where it will yield the highest return. Virtual reality, augmented reality, 360-degree video and wearable devices are four cutting-edge visual content ecosystems that have already surged in popularity. Computer vision—an umbrella term for any technology that allows a computer to process and analyze imagery with a high degree of understanding—remains a dark horse in spite of its wide-ranging applications, from medical imaging devices to self-driving cars. 

Marketers are missing out. According to a GumGum survey, an overwhelming majority of marketers confine their visual content to limited ecosystems: 72% post images and video on platforms they own and operate; 29% use visual content in social media posts; 36% use augmented social media graphics, such as lenses and geofilters; while only 19% use images or video in display ads. The advantages of sticking to one’s own platform, of course, are plentiful. The approach ensures a brand’s content doesn’t compete with its competitors, while allowing the brand to control its own infrastructure and customize content to its various platforms; it also helps prevent unfavorable adjacencies, and more or less guarantees that an ad is reaching users already interested in it. But the disadvantages are also plentiful. Marketers that stick to their own ecosystems lose out on the opportunity to expand brand awareness beyond core devotees, capturing new users in the platforms where they live most.

A related challenge is the declining utility of demographic targeting. It’s not just that the census data marketers rely on has limited value—it can tell you how old someone is but not what their interests are, what motivates them—but that new laws in the European Union and California have dramatically hindered marketers’ ability to collect demographic data from a wide range of users. Contextual targeting can fill the gaps and then some. By placing marketing materials adjacent to related content, or in complementary environments, it captures users who are already likely to be interested in the brand. It requires little to no demographic information, and expands marketers’ reach well beyond their own platforms. And while contextual targeting poses its own challenges—namely that it is near-impossible to do manually, and risks placing content in brand-unsafe environments—these have a common solution: computer vision, also known as image recognition. 

Image recognition technology allows marketers to comb efficiently through millions of existing images and videos, then place contextually relevant marketing adjacent to the right ones. And it helps avoid brand-unsafe exposures by giving marketers strict control over where their content appears. As Sean McInerney, group vice president of technology at the ad agency Huge, explained, “If you say, for instance, ‘I don’t want to be next to any trucks,’ [image recognition] would have a large degree of success with that.” The same goes for, say, a vegan food brand that doesn’t want its ads appearing beside media about a barbecue cook-off; or for a burger brand that does. In other words, computer vision empowers marketers to use contextual targeting with unprecedented ease and security, which in turn allows them to reach—and capture—users well outside of their traditional ecosystems. As the digital landscape fragments into endless niches within niches, these tools will prove essential to an impactful marketing strategy.

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