A generation ago, media conglomerates tightly controlled content production and distribution, deciding when, where, and how content was consumed. That’s all changed. Gone are the days of linear television channels and a single-television household. Today’s consumers decide when and where to consume content across multiple platforms.
With the average attention span of an adult hovering at eight seconds, down from 15 seconds in 2000, the media industry is fighting for increasingly smaller slivers of consumer attention. Media companies need a solution for monetizing content and delivering the right content to the right consumer at the right moment. Advanced analytics, machine learning, and blockchain are three disruptive technologies that can solve the twin problems of volume overload and content monetization.
How advanced analytics and machine learning solve the “paradox of choice”
For media companies, consumers drive demand. It’s all about what they want, when they want it, and which device they want it on.
“We went from a very analog-driven, subscriber numbers rated world to a world where it’s about engagement, and about data, and about consuming the content when you want,” says Richard Whittington, senior vice president, Media Industry Cloud Solutions, in the S.M.A.C. Talk Technology Podcast.
Of course, if consumers can’t find they content they want, they can’t consume it. In a world with nearly infinite choices, consumers are increasingly paralyzed by the “paradox of choice.” This theory states that there is a tipping point for choice, a point where more choices cease to provide an advantage and instead become a hindrance. It’s akin to the feeling of mindlessly scrolling Netflix looking for something to watch, but not finding anything. One-third of consumers say that they frequently cannot find anything to watch, according to a Cord Cutting Survey conducted for Rovi.
Media companies are experimenting with new machine learning algorithms to better understand consumer behavior, preference, and social cues. With machine learning is it easier to utilize metadata through intuitive, creative applications, rather than simply recommending a movie based on genre or actor preference. For example, machine learning enables language processing for a deeper understanding of content based on mood, emotion or intensity. Coupled with social signals, such as a conversation on Facebook around a new movie, machine-learning powered content recommendations could boost viewer engagement, satisfaction, and loyalty. Relevancy and timing are paramount: media companies that can provide consumers with a perfectly curated shortlist may outperform the companies offering an endless list of options that miss the mark.
New monetization pathways with blockchain and machine learning
Since consumers moved away from physical products like CDs or DVDs, media companies have struggled to monetize their content. According to Whittington, blockchain offers a new path forward, addressing problems associated with rights management, payment, and distribution.
“Blockchain gives media companies the ability to track content and create events when content is consumed,” says Whittington in the S.M.A.C. Talk Technology Podcast. “For example, if I send you a football match to view, this will trigger an event that indicates that you consumed the match. Money is then paid to whoever owns the rights to that match, rather than having to go through the traditional controlled, linear model. Blockchain has the ability to turn the whole business model upside down.”
In addition to using blockchain to monetize content distribution and consumption, Whittington says machine learning may also play an important role in content monetization.
“We’ve always heard about product placement in shows,” says Whittington in the S.M.A.C. Talk Technology Podcast. “But [product placement] has never been able to be measured to such an extent with heat mapping and knowing exactly what people looked at, and did they notice it, and how long did they look at it for, and what is the value of those impressions compared to other media avenues that they might have put those dollars into. I love the example, for instance, of how can you use machine learning to say, hey, on this episode of ‘Modern Family’ we had this many times that we showed XYZ’s product and we’re going to charge you for this. If you don’t see any value in this company A, company B might. We can actually create competition there.”
Next steps: Gaining the first-mover advantage
The media industry has undergone a transformation from a distribution model to a direct-to-consumer model and continues to evolve at a rapid pace. By 2020, Gartner predicts that artificial intelligence (AI) bots, rather than humans, will manage 85 percent of customer interactions. There will be over 82 million US millennial digital video customers. As media companies grapple with the challenge of getting the right content to the right consumer at the right time, companies that proactively invest in advanced analytics, machine learning and blockchain will gain a critical first-mover advantage. These companies will be best positioned to turn data into insights, monetizing the delivery of the right piece of content at the right moment to the right consumer.
To learn more about how digital transformation is disrupting content distribution and monetization in the media industry, listen to the S.M.A.C. Talk Technology Podcast with Richard Whittington.
Hear the full podcast episode here. For more insight on digital leaders, check out the SAP Center for Business Insight report, conducted in collaboration with Oxford Economics, “SAP Digital Transformation Executive Study: 4 Ways Leaders Set Themselves Apart.”