Video marketing is being revolutionized by fast data, machine learning, and artificial intelligence. The dawn of data-driven video is upon us. Video takes the lion’s share of marketing spend and fast-growing mobile video is surpassing all other marketing methods.
Video industry leaders who embrace these advanced technologies will establish a formidable competitive advantage.
Understanding behavior and content consumption is key in optimizing mobile video. Brands have an insatiable appetite for consumer engagement, as evident in brands’ adoption of video, report YouTube, Facebook and InMobi.
The industry is moving away from the video interruption ad model and premium video is taking a key spot. A major battle is brewing between video networks, publishers, and content creators. Those who have intelligent data will win the video marketing revolution.
With few exceptions, old school person-to-person media buying is fading fast. Machine learning is being used to ensure the optimal deal is always reached in programmatic video placement. We are seeing a torrent of data coming in from ad platforms, beacons, wearables, IoT, and so forth. This data tsunami is compounding daily, creating what the industry calls “fast data”. Video and human action on video is a big challenge due to consumption volume. The competitive weapons are now speed and agility when building an intelligent video arsenal.
In July, I attended the launch of Miip by InMobi, an intelligent video and ad unit experience. These units are like Facebook’s left and right slider units, but Miip has also implemented discovery. Check out the video to see more of what I’m talking about:
All programmatic networks use fast data composed of human personas, actions, and connected devices. This data explosion is forming big data, and it’s happening at a massive scale. It’s not surprising that programmatic targeting leveraged machine learning and big data management. There is a lot of hype around Real-Time Bidding (RTB) and programmatic targeting.
With all this technology, the one thing that remains true is content still must resonate with the consumer – and machine learning is creating a huge opportunity to match the right content with the right consumer.
We see big players like Tubmoguel seeing massive growth, as described in Mobile Programmatic Buying Is Taking Off. Programmatic spend in mobile now surpasses desktop by 56.2%, eMarketer points out.
Video Creation & Growth
Video creation tools like Magisto, PowToon, and iMovie are simplifying the process. The decreasing hardware costs have also lowered the barrier of entry. The iPhone 6, Hero4, and video drone technology are great leaps forward in video capture.
Low-cost broadcast-quality video is here with iMovie HD and Camtasia Studio 8. Full commercials are edited on iPhones only. There is an explosion of professional content now. What was once cost-prohibitive is now the industry norm. With all this video technology unleashed, hundreds of YouTube stars were born. The cable cord-cutting acceleration is upon the cable networks now. As more high-quality digital video hits the scene this will fuel grater choice on the consumer’s terms.
Peter Fasano, from Ogilvy, and Allison Stern of Tubular, did a great job presenting The Rise of Multi-Platform Video. Here they reveal the differing advantages of Facebook and YouTube.
In both cases, content engineering is a must-have (see 5 Hypnotic Mobile Native Video Content Marketing Methods).
Data Driven Video Storytelling
This year, Cannes Lions was all about VIDEO storytelling with a big focus on data. Visual and mobile content experiences are personal. I am seeing a massive shift to data-driven journalism. Companies like Google News Lab, Facebook’s Publishing Garage, and Truffle Pig (a content creation agency) are all working with Snapchat, Daily Mail, and WPP – all powering scaled content creation.
[pullquote]“The power of digital allows content, platform, and companies to test and learn in real time before scaling.” -Max Kalehoff[/pullquote]
Hear more on this movement from David Leonhard from New York Times’ The Upshot, Mona Chalabi from Facebook Garage, and Ezra Klein and Melissa Bell from Vox:
Video is Not Spandex
Consumers are not one-size-fits-all when it comes to how they consume content. The creation of content is a natural progress for using artificial intelligence (AI) technology. Machine learning has the ability to connect many data elements and test many hypotheses in real-time. Using humans to adjust the algorithms is “supervised learning”. “Unsupervised learning,” a self learning and constantly improving system, is the holy grail in AI.
The exciting part is when machine can create by themselves. We are witnessing this at Google: see Inceptionism: Going Deeper into Neural Networks.
Getting the right message to the right person is critical in obtaining a positive response. The delivery process and decision will impact the responsiveness. Each platform requires a different strategy. Companies like Tubemogul, Tremor Video, and Hulu all have programmatic video management.
Now broadcasters are starting to embrace data, which enables advertisers to target a more specific audience. Soon we’ll have AI video distribution based on the actual content inside the video.
The following are three examples of machine learning techniques being used to enhance video engagement levels:
- Fast data requires advanced algorithmic learning to process: Identify what demographic responds well to which content type (e.g. video). Segment your audience by the type of content consumed. Look at what was shared when most comments were generated. Combine these data points and see what drove most action. These steps will help you learn what logical groupings achieve highest targeting response.
- Identify what visual objects induce habitual responses: What visual objects allow for higher consumer engagement? Visual content can then be grouped and that knowledge can be used over and over in later videos.
- Machine learning predicts video consumption habits: What people watch tells you a great deal about their preferences. Measuring audience behavior across video types creates a consumption map. Consumption maps predict things like video placement and cycle times.
The type of visual content affects the reaction of a targeted segment. Machine learning can track the visual preference of the video segments. Each brand and content creator structure can achieve a new level of understanding. What does the audience find most appealing? Is there a large-scale pattern you can identify?
The next frontier of mobile video is intelligence – the ability to predict, as well as adapt, content based on all the data available. We are seeing companies like IRIS.TV indexing video libraries to recommend content. Netflix and Amazon have the capability to “predict” using supervised learning human curators. All this metadata in video is providing a treasure trove of information: now we’re connecting with the social graph changing the game.
Finding content that viewers will enjoy is the ultimate goal and extended deep video engagement is a big opportunity. Achieving this level of nirvana has its challenges: see Why Websites Still Can’t Predict Exactly What You Want. We are just scratching the learning algorithms surface of artificial intelligence.
As technology advances, more intelligent visual content marketing will emerge. Machine learning will soon dominate the data-driven marketing landscape. We are moving toward story creation with technologies like Dramatis. People like Brian O’Neill at Western New England University are leading the way (see With Expanding Roles, Computers Need To Add ‘Storyteller’ To Resume). Video networks, content creators, and publishers have a grand opportunity, but all are going to need to collaborate and incorporate a more sophisticated offering if they plan on competing over Facebook and YouTube. The big question is, will they maintain control of their content destiny?
In the age of intelligent data, audience insight is always a winning strategy. Those who tune their video content with intelligence will achieve higher levels of revenue.
How do you see machine learning impacting video in the future? Share your thoughts in the comments!