Before you can build great ads, you need to get past average. The first thing I do is swipe a collection of the best videos in the industry (i.e. from TikTok ad library), and label them to identify what they have in common.
Once you know what’s working in your category, you can make a video at least as good...More
Why don’t we use Google’s Video Intelligence API?
It’s actually really good, and won’t take more than a few seconds per video, for short videos like this.
It’ll spit out the labels of what it detects is in the video
Then we can pivot them by label to see which labels are most popular
Shall we try it with the top 20 videos?
This course is a work of fiction. Unless otherwise indicated, all the names, characters, businesses, data, places, events and incidents in this course are either the product of the author's imagination or used in a fictitious manner. Any resemblance to actual persons, living or dead, or actual events is purely coincidental.
Computer vision has progressed exponentially in recent year, to the point where we now have the processing power to dissect videos frame by frame and detect what’s in each shot. Machine learning algorithms are trained on millions of videos and their labels, so that they can learn to recognize objects, faces, and landmarks. The technology can be used to automatically tag videos with metadata, so that they can be easily searched and indexed. There are a range of applications from security and surveillance to image and video search, and decomposition analysis: breaking the best ads in your industry down into their component parts to analyse what’s working.
Companies like Google have democratised these techniques through open source libraries and commercial APIs bringing the cost of experimenting with them down considerably. Now anyone can tag any image they like in a few seconds without owning a supercomputer or knowing anything about machine learning. Marketers who want to go a level deeper with industry analysis, can use video intelligence to automatically tag each video in their category, and then pivot to see what labels are present across most of the top performing videos. Then these ideas can be used as new hypotheses to test in your creative testing plan, to validate if they work. This approach will help you at least get to ‘good enough’ and serve as a foundation for further testing.
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