Digital video consumption has surged, igniting new monetization opportunities for these modern distribution outlets and content makers, a point I explored in a previous essay. The challenge I didn’t touch on, however, is how to optimize these monetization events in the most efficient manner. Machine learning is at the root of the answer.
We’re experiencing a global explosion of personal content, a deluge of digital data that’s partially curated, deleted or in the case of moments that often really matter -- lost in the ceaseless current of photos and videos. Consumers only have so much time to collect, and more importantly highlight, their important memories. Photos sent to social media represent a very incomplete picture of our lives, even though 350 million photos are uploaded each day on Facebook.
It’s hard to imagine a world where advertising is perfectly targeted to know exactly how you feel and what you need or want. If it were to happen, we may find it incredibly creepy and encroaching of our privacy as there’s a yin and yang about data we want to share. Still, people do share; their preferences are increasingly known; their behavior is teased out to lead them to act. Content marketing is the reason a lot of this is happening.
When Snap made headlines last year for surpassing Facebook’s video viewership, its achievement of the 10-billion-video-views-per-day milestone underscored not only Snap’s staying power, but the growing importance of video to social media. What will be interesting to see is if this is a winner-takes-all match, or one where each of the players decides to own a different corner of the ring.