The computer vision revolution is here.
IT’S A TECHNOLOGY THAT IS ALREADY IMPACTING DIGITAL ADVERTISING AND WILL CONTINUE TO DO SO FOR YEARS TO COME.
In this blog post, we’ll take a look at the basics of computer vision as it relates to digital advertising and how it can be used in your marketing strategy.
This is computer vision in digital advertising, explained.
Computer vision has an unrivaled ability to process and analyze digital images and video content in real-time at an unlimited scale, making it ideal for use with digital ads. From there computer vision can be combined with AI (artificial intelligence) and machine learning (ML) to convert broadcast content – from both programs and commercial breaks – into usable data that is processed, contextualized, and organized. This enables advertisers to create targeted ad campaigns that are more effective because they’re based on the context of what’s happening within a program or break instead of simple demographics like age range or gender.
As mentioned above, computer vision quotients text, as well as objects found within digital images and video, are able to process this data in real-time. Combined with computer vision’s ability to convert broadcast content into usable data for marketers, ACR (Automatic Content Recognition) and OCR (Optical Character Recognition) can be used together to turn on-screen text from both programs and commercial breaks into searchable metadata that advertisers can use at scale across digital platforms like YouTube, Instagram, TikTok or Facebook. This enables more effective ad targeting because the ads are not only based on what’s happening within a program but also on who appears in the frame as well as any relevant keywords mentioned throughout the program such as movie titles or brand names. Not all computer vision algorithms function identically so it’s important when selecting software providers to consider which areas they specialize in and what computer vision tasks they can handle. Generally speaking, computer vision is good at analyzing and recognizing objects within digital images or videos as well as text found within those same images and videos; but computer vision isn’t effective for everything.
Computers see the world very differently than humans do – which means that there’s a lot of information about our online behavior that we’re missing out on because it doesn’t register with standard computer vision algorithms (yet). As technology develops, however, businesses will be able to glean increasingly granular insights from their data by using computer vision software combined with other analytical tools like NLP (Natural Language Processing) to better understand consumer behavior online such as intent and sentiment. This type of analysis provides more accurate profiles of each customer and can be used to create a holistic view of each consumer that allows marketers to better target and market their products or services.
As computer vision technology continues to develop, computer vision quotients will become increasingly effective at analyzing online behavior like intent and sentiment – providing businesses with much-needed insights about customers which can then lead to creating holistic customer profiles for increased ad targeting and marketing success.
In conclusion, computer vision is at the forefront of digital advertising and will redefine how we see ads in the future. As computer vision technology continues to develop, computer vision quotients will be able to analyze online behavior like intent and sentiment – providing businesses with much-needed insights about customers which can then lead to creating holistic customer profiles for increased ad targeting and marketing success.