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How computer vision may impact the future of marketing
How is computer vision changing the face of marketing? Columnist Daniel Faggella takes a look at three current applications and the promise they hold for marketers.
When people think about computer vision (sometimes called “machine vision”), they often think of smartphones and autonomous cars.
Snapchat can give you a puppy dog face thanks to facial recognition (a subset of machine vision). Autonomous cars can identify a human walking across a street. But did you know that machine vision plays a role in future marketing applications as well?
In this article, we’ll explore three current applications for computer vision in marketing. It’s important to note that these applications are most likely to be found in retail or broad B2C markets — I covered the reasons for this in my “5-year trends in artificially intelligent marketing” article here on MarTechToday (which may be a useful read for people with a strong interest in AI’s wider marketing applications).
1. Contextual ads/in-image ads
When Google AdSense or Google Display Network is embedded on a site, users will see a text or image ad that’s either (a) relevant to the text on that page, or (b) based on retargeting data of that particular user.
But what about images? As it turns out, there are companies (GumGum is one of them) that can display advertisements over images, by contextually identifying what is in the image and displaying relevant ads on the image itself.
For example, an image featuring playing kittens might be a good place to advertise a cat food brand — or an image of a tropical beach might be a good place to advertise vacation rentals in the Bahamas. One of GumGum’s YouTube videos shows this technology in action in a short highlight reel:
This is a challenging task that hasn’t been possible until relatively recently — thanks to major developments in machine vision in the last two to three years.
“Until very recently, it hasn’t been possible for a computer to get a semantic — that is to say, a human-level understanding of pictures,” machine vision guru Nathan Hurst, a distinguished engineer at Shutterstock, told me. In a recent interview, he explained how past approaches almost always boiled down to tagging images to identify their contents — until engineers built machine learning models that could be trained on massive image data sets.
With algorithms that can distinguish not just a “car,” but a “2004 Honda Civic,” and not just a “dog,” but a “cocker spaniel,” advertisers now have the ability to target specific image contexts to target their ads. An e-commerce business targeting Honda owners can not only target branded search terms (in Google AdWords, for example), but might also target only the images of Honda cars on related websites.
2. Programmatically generating advertising creatives
The online world is moving to video — with Cisco research predicting that 80 percent of web traffic by 2019 will be from engagement with video. Because of this trend, not only are major journalistic sites (such as Mic and Verge) pivoting to video, but brands are also aiming to win in the video game — but it’s not easy.
If a sunglasses brand has 100 images of its newest design, how does the company know which of those images should be used to garner clicks or purchases from users on Facebook, Twitter or Pinterest?
Montreal-based Envision.ai is working on applications to parse through myriad image and video options to match the right media to the right user at the right time. Because a certain user or demographic group may change click-through behavior depending on the time of day, an AI system could be trained to adjust advertising media on these real-time factors.
For instance, Unilever’s Axe body spray has run social media campaigns with 100,000 different versions of its “Romeo Reboot” video, according to a post by the project’s visual effects director. As this kind of deep “calibration” to users and segments becomes the norm, large consumer brands may be forced to follow suit to match the innovators in online media engagement.
3. Facial recognition for advertising feedback
One of the benefits of online advertising is the fact that it’s trackable. Advertisers know how many sessions, users, clicks and so on happen in a given day or minute. They can calibrate specific ads to certain types of users or geolocations or days of the week and so on. This digital “footprint” allows for a tremendous amount of data to be collected to help optimize an advertiser’s efforts.
But outdoor advertising hasn’t been able to keep up. Tracking “users” and tracking “number of people who walk within 10 feet of this signage” are very, very different — the latter being much more challenging. Tracking “number of clicks to video content” and tracking “number of passersby who look at this outdoor advertisement for more than 3 seconds” are very different — again with the latter being much more challenging.
The limitations of the physical world are being overcome, however, by innovative companies that are taking the principles of online testing and variation and bringing them offline. London’s M&C Saatchi has experimented with outdoor advertisements that track physical equivalents of “engagement” and varies its outdoor signage in real time based on the responses of the people who walk past.
In the future, advertisements on desktop and (especially) mobile may gather details about attention and emotion through facial recognition — and take this feedback into account to help determine what ads should be shown next or what details the advertisers themselves should change.
Some opinions expressed in this article may be those of a guest author and not necessarily MarTech Today. Staff authors are listed here.