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  • Writer's pictureOmri Yaniv


By Michael Avon, CEO & Founder - November 19, 2018

To cut through the noise of today’s endless stream of content, marketers are turning to data science and artificial intelligence to learn more about their consumers and create content tailored to their interests. This has perpetuated a data arms race based on which insights can be most impactful for marketers. The challenge for marketers is that not all data is created equal. So, as a marketer, what data is most relevant for your strategy? In this search for data to inform audience intelligence, I propose the answer may be consumers’ video consumption. Every marketer today is using data to inform their decisions to some degree. We all have seen the impact that “Moneyball” and data-informed analytics has had on sports over the past two decades. Now we see that same evolution happening in the marketing world with the ways marketers are sourcing and creating content for their campaigns. In evaluating which audience insights are relevant, marketers need data that is both holistically informative and predictive. The data needs to be informative in that it is an accurate representation of the audience you’re analyzing. When this is done holistically, the data will encompass a wide range of metrics to analyze. Predictive data will not only be representative but will also provide the type of value that can lead to your “a-ha moment”. An individual’s consumption trends can provide both informative and predictive insights. And with the proliferation of video across the digital landscape, what consumers are watching can be the key audience insight marketers are looking for. An estimated 80% of all internet traffic will be video by 2019. With all this video being watched, viewers are leaving metadata cookie crumbs that are rich in informative insights for marketers to analyze. Social video data is an amazing source of real-time data of what people are watching and what people are saying about a specific topic. This can just be traditional social listening, but it can be a lot deeper looking at specific engagement with videos. Machine learning and artificial intelligence are useful to figure out what data points are actually useful and predictive and which data points to ignore. According to Nielsen, U.S. consumers watch nearly three hours of video on their computers or smart devices each week. The time spent viewing social videos is what makes this data so valuable and predictive. Time is the ultimate resource. We all have the same amount, so where you spend your time shows a lot about your interests and priorities. Basically, you are what you watch. Viewers spending their time on video are giving strong, predictive indications of what they like. Listening to social video data lets you drag in a wide net of information about an audience and A.I. can help decipher what pieces of that huge amount of data are actually relevant. Imagine the content you can create with this deep understanding of an audience’s behaviors and interests. Marketers who apply A.I. and machine learning to social video data will be ahead of their counterparts in the audience-intelligence arms race.

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