Computer Vision for Ad Tech, by Ad Tech.
Founded by Kristopher Kubicki in 2017, Deep.ad provides brand attribution and market intelligence software for brands, agencies, and market research organizations.
Our solutions automate the discovery and extraction of brand, product, and advertising data and insight in paid and organic visual media.
Using Machine Learning, we detect, classify, and measure appearances of brandprints – logos, trademarks, products, offers, and text – within visual media and digital content.
We bring established Ad Tech practices and scale to emerging media like TikTok, Instagram, Twitch, YouTube TV and Disney+, giving agencies and brands the data they need to identify, analyze, and engage with visual content that features brand mentions and appearances.
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Failure to track video is not an option.
20 Billion
Videos are viewed on the internet every day. Facebook leads the pack with more than 8 billion daily video views, followed by YouTube (≥5 billion) and TikTok (≥3 billion). This excludes Snapchat’s additional 10 billion.
79 Percent
Of people make purchasing decisions based on User-Generated Content, whereas 96% of consumers are wary of traditional advertising. Users trust UGC much more than any brand content – no matter how sociable.
500 Billion
Minutes were viewed on the live stream platform Twitch in 2020 for the top 1,000 streamers alone. Similarly, by 2024, a third of American households will cord-cut, shifting entirely to online video services like YouTube TV, Hulu, and Sling.
82 Percent
Of all consumer internet traffic will be video-oriented by this time next year (2022). Not too surprising considering we upload 500 hours of content to YouTube every minute and share 500 million stories on Instagram every day.
You can collect content and competitive intelligence from visual media.
One-size-fits-all Machine Vision AI isn’t working.
There’s intense competitive pressure on businesses to quickly adopt and monetize Enterprise Machine Learning.
According to Deloitte, over 50% of global data and analytics executives report that they’ve implemented ML technology or are in the process of doing so. Tipping point… ✓.
So it probably won’t surprise anyone that there are more than 100 Optical Character Recognition (OCR) ML companies in the US alone.
Nor should it be surprising that this ML gold rush makes it difficult for organizations to find the right partners. It doesn’t help that many of these vendors look and sound identical to one another: they cast wide nets, catering to countless industries and celebrating the infinite use cases of their software.
In reality, jack-of-all-trades ML platforms are always masters of none.
Even the top OCR solutions in the field do a fine job of automating object detection, labeling and transcription. But without extensive pre- and post-processing, their data languishes in silos and provides no real value to businesses.
There’s a lot of evidence to support that claim.
Deloitte’s 2021 Tech Trends report found that only 8% of respondents considered their ML programs “sophisticated.” 47% of all projects didn’t make it out of an experimentation phase, and 28% of them failed.
This specialization is what separate us from other ML providers in the Ad Tech space.
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