Want to learn what type of video content KRAKEN’s Video Machine Learning technology works with? Read on!
So, you already have video on your site and you’re asking yourself, “can KRAKEN help me get more video plays??? I mean, my content is pretty special and unique!”
Let’s get right down to answering that with these 3 easy steps:
Step 1: Come to our blog. Wait… you’re already here. Good job! You’ve done Step 1 successfully, so check it off your list.
Optimized thumbnails with 198% lift
Step 2: Ask yourself—what kind of content do I have and how is it monetized? I mean, you could just have video and not make money on it. You could run pre-roll beforehand, or you could get paid with every video play by the producer/creator (this last one is called “premium video”).
Step 3: Scratch Step 2, because KRAKEN can help increase play rates with all three types! Check out the following examples!
Quick recap of what’s happening with each video below:
Video is broken down into a bunch of “best possible” thumbnails (using crazy smart algorithms)
Your audience selects their favorite thumbnails via A/B testing & machine learning (favorite=thumbnail they WANT to click on)
We intelligently rotate through the best (aka favorite) thumbnails to ensure a high click-to-play rate
Click play… notice that the video begins playing? This is premium video where the content is the advertising medium. Notice the video is about a product, so there’s no need to run a pre-roll ahead of time.
What do I do next?
As you can see, KRAKEN increases play rates on just about any type of video content. You just need to release the KRAKEN!
Want to see some awesome examples where optimized thumbnails performed up to 425% better than the original? Check out this article.
Publishers are under financial assault and video performance is a white hot topic with brands doubling down on mobile video spend. It’s all about revenue, consumer win and getting the most out of your videos assets.
Boosting video performance on existing content is not simple, however, video machine learning provides a unique and scalable way to accelerate video engagement like never before.
Here we will dive into how one of the top 20 news site uses hyper video machine learning to boost play rates on the lion’s share (70%) of videos published.
The David Bowie video release achieved an average 92% boost and in the first 3 days hit 104% boost (We’ll miss you David)
We kicked in the hyper drive on The Force Awakens movie trailer delivering 41% boost and describing what’s behind visual learning. News oriented video content has shown tremendous lift rates up to 425%, with many videos achieving 100%+ lift in play rates. Breaking away from old school thinking, in this post you will learn what’s driving higher video revenue beyond image recommendation (selection) technology to full on creating more engaged video watchers via intelligence.
How it’s done
KRAKEN’s video machine learning API connects directly within the publisher’s video player. The video thumbnail images are optimized using real time A/B testing and image recognition algorithms. There is tons of evidence on the immense power of visuals and first impression is everything for viewers.
The key component of KRAKEN is learning algorithms that understand placement and visual elements within the video that resonate with a particular audience. Consumers are guzzling content at a hyper rate and in a world full of distractions, content “images” that can quickly capture attention will achieve a higher share of time.
In the case of video: more plays =
more share =
more overall engagement = MORE REVENUE.
Another key attribution of KRAKEN is helping videos moving away from an unresponsive static image that retained no intelligence. KRAKEN’s image selection is not random and incorporates the sequencing of images “Image Rotation.” This translates into showing more visual depth and further stimulates visual cognition. Hey, we patented KRAKEN and here are some solid number to prove it works!
The ability to tell a visual story based on behavioral engagement assures the maximum possible engagement levels, which would otherwise be lost. That’s right—you’re losing money!
That lost video play is lost revenue and in most cases, it’s a great deal of money being left on the table. For the fans of Game of Thrones this video hit an astonishing 169% LIFT proving the right visuals drive higher revenue (lift= performance of dynamic visuals over the original thumbnail).
Video play decay over time. Note that after two days, the video gets a bump. KRAKEN created a 92% avg LIFT on the David Bowie video.
All published videos experience a time to live. Even viral videos will decay over time.Video play engagement decay on high CTR videos is displayed in the graphs above and below.
Time to live is a function of:
Video placement on main sections
Placement above or below the fold on published page
Mobile feed depth (how many times to scroll to see the video)
Mobile in view (how long is the video in view)
How long it’s displayed in the editorial pick or trending section
Social share magnitude
This video achieved 141% LIFT demonstrating that human faces don’t generate greater action over visual scenes that depict the video content.
Time to live variables have an impact on how long content can achieve high engagement and for how long. Obviously video performance is a function of site traffic, however, the wrong image causes massive consumer engagement loss due to the speed at which humans can process visuals and determine relevance. This speed is on the order of a blink of an eye. Are you adjusting your visual at a blink of an eye? That coupled with Forrester Research, one minute of video is worth 1.8 million words and there you have a perfect reason to make sure every consumer engagement counts.
I’ve never met anyone who intentionally picked a bad video thumbnail—but they’re everywhere.
To be clear, bad ≠ ugly. Bad thumbnails are sometimes beautiful. Bad means that people don’t WANT to click on them. After all, the point of a thumbnail is to get people to click “play” or “stop scrolling” long enough for the video to start to playing.
Editors and content creators with years of experience spend a lot of time picking “best” thumbnails. And publishers posting hundreds of videos daily rely on content management systems (CMS) that suggest or auto-pick thumbnails.
Guess what? They’re usually wrong.
Almost always, there is a better thumbnail for any given video or set of thumbnails.
Because “best” is defined by your audience, not you. You bring your experience and baggage with you every time you pick a thumbnail—and you are different from your audience. Why not take the guess work out of the equation and use data, not opinion, to choose the right thumbnails every time?
Let’s say you’re an editor in LA and pick a thumbnail for a video about the latest breaking news topic. You might choose this image to the right:
Now what if your viewer is from Texas? What if that image doesn’t speak to them at all? That doesn’t mean they’re not interested in the topic or wouldn’t want to see the video content, it means that the thumbnail doesn’t make them WANT to click “play.”
If you had asked your viewers, they would have told you that they preferred seeing the images on the left—all taken from the very same video.
Our recent post “The Force Awakens” shows another great example and the science behind data-chosen thumbnails.
Your audience isn’t one-size-fits-all. Your thumbnails shouldn’t be either.
Here are 52 videos from last month that prove intelligent selection of images can greatly improve video play rates. Each has an optimized set of thumbnails that performed 101%–425% BETTER than the original thumbnail.
Quickly though—what is an optimized thumbnail?
Optimized thumbnails are dynamic and rely on machine learning and audience feedback. Our product called KRAKEN does this all in real-time
So, what the heck does that mean in english???
It means that our computers examine a video and pick a bunch of ‘best possible’ thumbnails, then A/B tests them to determine what ones people actually click on. It will serve different images to different people depending on a variety of factors, including device and placement. Hey, it’s a patented process!
Said another way, we crowdsource what thumbnails people actually engage with, then show them to future visitors.
Results – Before & After
Think sports fans will click on any video related to their team? Think again. Optimized thumbnails performed 198% better than the original: Original Thumbnail KRAKEN Optimized Visuals
Optimized thumbnails work for ‘hard’ news videos, too. This video about Enrique Marquez’s ties to the San Bernardino gunmen had a 205% lift: Original Thumbnail KRAKEN Optimized Visuals
Kardashians—love them or hate them, right? It turns out that optimized thumbnails can produce a 128% lift in video play rates: Original Thumbnail KRAKEN Optimized Visuals
From earlier in the article: the Rikers Island Guard video saw a 157% lift, while the video of a Teacher under fire for her lesson on Islam saw a 127% lift.
Our top performing video of December saw a 425% lift. Here’s an overview of all 52:
What could you do with double the video plays (or 3X or 4X)?
Would it double your video revenue? Satisfy your audience because more of them are seeing your awesome video content (after all, that’s why they’re on your site in the first place)?
The good news is your “best” thumbnails already exist and are buried in your existing videos. You just need to release the KRAKEN and get them to the surface.
Leave a comment below and tell us your thoughts. If you are interested in links to all 52 top performing videos, send me an email at email@example.com—I like talking with new people.
Star Wars: The Force Awakens Video Machine Learning Trailer achieves a massive boost (41% gain) using visual sequence story telling. Optimizing video is now a must for publishers looking to maximize their video assets and engage customers with content relevant to them. Embrace the “FORCE” Above is a live example of KRAKEN’s “Image Rotation” in action powered by video machine learning seen on NYDailyNews. The image sequencing is created by KRAKEN and is integrated directly inside the video player via the KRAKEN API.
The impression a video makes on a consumer is everything, especially with mobile. Typically seen is a still image with a large play button overlay in video players. This thumbnail image has been stuck in a static world for over 15 years. The old school static thumbnail on video is dead and auto play is frankly annoying.
Image quality is important but our findings prove that consumers select images and prefer not the best image but the ones that cause the human mind to have intrigue.
However, the static thumbnail selection is still dependent on the person who uploads a video. This process does not scale to thousands of videos over a short period of time. That is why the majority of commercial video platforms auto select from a fixed time slice from the video and hope for the best.
Static thumbnail selection with customized thumbnail upload. All video platform provide this manual feature as well as a auto default is selected.
Humans cannot optimize or adjust creative on the fly to increase video performance. Many attempts to do A/B testing have proven to be helpful, however they produce limited results due to their manual nature.
Video machine learning has come of age because it is cost effective and enables publishers to use the FORCE. Image sequencing is not a new ideal and has been used for centuries for depicting visual story telling.
Video machine learning makes it possible to scale image sequencing over thousands of video placements and millions of plays. Video has gone from a static world to a dynamic and intelligent world. Star Wars: The Force Awakens Trailer benefited tremendously from video machine learning with a lift of 41%.
Another major bonus of video machine learning is the ability to scale and combat image fatigue (decreasing engagement over time).
Capturing a consumer’s attention has never been harder than now. Consumers are glued to their smartphones and every millisecond counts. Publishers are reverting to the annoying auto play tactic, however, consumers are pushing back and complaining. Fox has responded to consumer feedback by offering a feature to turn auto play off. The growth of mobile video will continue to increase massively for publishers optimizing video. Machine learning will continue to help them benefit and maximize their valuable video assets.
Do you want to learn more about KRAKEN and hear what others are saying about video machine learning? Check out our testimonials and intro below. Thanks for your input and thoughts on our our journey in video machine learning.
Ryan Shane VP of Sales
Want to increase your video play rates and increase revenue? Contact us for a 1:1 demo and access customer use cases and see live examples on both mobile/desktop implementations.
Introducing Baglan Rhymes, Chief Digital Officer at AnchorFree with Chase McMichael, CEO of InfiniGraph, discussing the recent success of video machine learning KRAKEN on AnchorFree video ads page. Video Machine Learning Customer Testimonial – Case Studies discussed in this video are Fifty Shades of Grey, American Sniper and Birdman.
Chase: Hi I’m Chase McMichael, CEO and Co- Founder of Infinigraph and I’m here today with Baglan Rhymes, the Chief Digital Officer of AnchorFree. Hi Baglan. Baglan: Hi Chase. Chase: So tell us a little about AnchorFree. Baglan: Of course. AnchorFree is the world’s largest internet freedom platform and our mission is to provide secure and uncensored access to the world’s information for every single person on the planet. To date, we’ve been installed 300 million times. We have 30 million monthly active users and we secure approximately 5 billion page views.
Chase: That’s excellent. Obviously, we got connected with the video machine learning technology—a technology called Kraken. Baglan: Yes. Chase: And you know one of the things was that you are using a monetization page with video on the free sites. Baglan: Correct.Chase: Tell us a little more about that.
Baglan: Yes, because we have a free service and subscription-based service and the revenue stream for the free service is our content sponsors—be it movie studios, be it news organizations. And we have our own content discovery platform where we have tiles of video content and also static content where we present the users upon connect. And the videos—we don’t make any revenue off of the videos unless the users click on it. So how do we get the users to click on a video when we have maybe 5 or 10 seconds of their attention right upon connection and that’s when we connected. So we partnered with you on click to play videos to increase click to play rate because unless those videos are played we don’t get paid and through your machine learning algorithms we were able to increase the click rate.
Click to view rate grew 20 to 30 times on videos overall, movies, overall movies and we ran a test on Fifty Shades of Grey and American Sniper afterwards we did and we did Birdman where we got 3,000% that ridiculous number [increase in click to play rate]. A fight scene in tighty whities. I actually remember I asked you to remove that. We can’t show it there and you kept it and that tighty whities that fight scene.Chase: That was the best one! Baglan: Exactly. 3,000% increase [in click to play rates] and I’m so happy we kept it.
Chase: That’s the one that boost the most revenue. So you know right now, where you seeing you going, especially around the consumer in mobile. Baglan: Yeah, video is the way users consume content now. And then whenever we see a video associated with a brand, we see a 96% increase on purchase intent, 139% increase on brand recall and even our conversations are now in the form of a video with your friends and it is just a video. So the whole communication is changing from voice to audio, visuals and emotions—which is video. Chase: Thank you so much Baglan. So please be sure to click on the (i) above to get more information. Thank you.
Video machine learning technology called KRAKEN skyrockets mobile consumer engagement by 16.8X for the Interstellar Trailer (case study).
Social networking for influential moms
SocialMoms began in 2008 as a popular community site for moms looking to build their reach and influence through social networking, traditional media opportunities, and brand sponsorships. It now boasts over 45,000 bloggers, reaches more than 55 million people each month, and has a network of influencers with more than 300 million followers on Twitter.
Create engaging mobile digital media campaigns for women 25-49 SocialMoms brings top brands to millions of women each month. They are responsible for ensuring that each campaign not only reaches the intended audience, but also that it be engaging and meaningful. However, it was challenging to get meaningful audience engagement with video campaigns on their smartphones.
Responsive visuals optimized for mobile
KRAKEN replaces a video’s old, static default thumbnail with a responsive set of “Lead Visuals” taken from the video. It treats each endpoint differently, so it can optimize a movie with one set of visuals for a desktop site and another set of visuals for a mobile site—because people respond differently depending on which device they use for viewing.
Maximum lift of 16.8X on mobile for the Interstellar Campaign After KRAKEN’s “Lead Visuals” optimization, engagement via mobile skyrocketed. SocialMoms saw over 16.8X increased engagement compared to the original default thumbnail that was chosen for the desktop site. They also reported higher completion rates when running KRAKEN.
“We’re seeing the highest engagement levels for our customers using InfiniGraph’s KRAKEN powered content.” – Jim Calhoun COO SocialMoms
Video marketing is being revolutionized by machine learning, fast data 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. 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, reported by YouTube, Facebook and InMobi.
Video industry leaders who embrace these advanced technologies will establish a formidable competitive advantage.
The market is moving away from the video interruption ad model and premium video is taking center stage. Battle for middle earth is being waged between video networks, publishers, and content creators. Those who have intelligent data will win the video marketing thunder-dome.
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.
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.
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.
“The power of digital allows content, platform, and companies to test and learn in real time before scaling.” -Max Kalehoff
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.
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.
This graph shows real-time A/B testing from video launch and KRAKEN machine learning optimization in action. Machine learning makes it possible to stabilize and achieve lift.
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.
Video machine learning technology called KRAKEN drives 40% additional revenue for the Birdman Trailer (case study).
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Increase revenue from video longform placements In order to keep Hotspot Shield free, AnchorFree is constantly looking for ways to increase their customers’ engagement levels and average revenue per user (ARPU). Regardless of premium placement on the AnchorFree launch page, the video ads were producing less than desired click-to-start and completion rates. Before KRAKEN, AnchorFree tested with various forms of static default thumbnails attached to the video promos.
Responsive visuals at programmatic scale KRAKEN uses machine learning technology to optimize “Lead Visuals” in a programmatic structure, enabling the highest video engagement possible. KRAKEN became the preferred platform to maximize video revenue yield from their current advertiser base.
40% revenue gain for the Birdman campaign KRAKEN boosted click to play rates for the Birman trailer video campaign by a staggering 3,000%. This increase in click to play rates directly resulted in a 40% gain in revenue. After realizing such profound revenue gains, AnchorFree does not run high value video campaigns without KRAKEN.
“InfiniGraph’s Kraken technology is the first real breakthrough we have seen in many years. I can see Kraken being implemented by digital broadcast networks, publishers, ad networks and video player platforms in the very near future. Early adopters will turbo charge their video ad revenues on desktop and mobile.”
– Baglan Nurhan Rhymes Chief Digital Officer, SVP Global Revenue
As video consumption increases, the need for a more intelligent, learning and adaptive technology is necessary to remain competitive in video marketing today. Data driven marketing is a digital differentiation. Those that have harnessed video insights to increase video yield will lead the way. SEE Case Study on Birdman (PDF)
In our previous post 5 Ways Machine Learning Accelerates Mobile Video [VIDEO] we describe the behavioral properties of content interactions within the video stream and how to sustain consumer engagement over various video networks. The above video is a quick intro by Chase McMichael, CEO of InfiniGraph and co-inventor of KRAKEN, the world’s first video machining learning technology. In this video, he describe KRAKEN’S value proposition for both video networks and publishers.
Video machine learning is no longer science fiction. But the technology is only part of the equation; scalability is required to process and display massive numbers of videos. For big publishers, just managing the video distribution process appears daunting; video optimization is an afterthought. However, thanks to advanced algorithms and fast computing, the ability to learn what “Lead Visuals” are most engaging and then optimize videos can be done real-time and at scale.
Creating great video takes time, and time is money. Maximizing all your video assets requires rethinking a post-and-pray strategy. Just as algorithms are used to target consumers, they can now be used to optimize videos and increase engagement.
Request a demo and let us show you how KRAKEN video machine learning will increase your video yield. Access our case studies here for more depth on video machine learning.
In Mobile Video Machine Learning KRAKEN, the “Birdman” Case study demonstrates video lift engagement powered by machine learning. In “5 Ways Machine Learning Accelerates Mobile Video”, we dive into why brands are embracing video as a key marketing and storytelling tool and how machine learning can be used to drive higher engagement.
The hard reality is video is STILL LINEAR. Even so, some are attempting to make them interactive like Jack White’s Interactive Video that allows viewers to choose their own adventure.
While the majority of brand videos are still stuck in a 15s / 30s pre-roll with a force fed content model, we’re starting to see a clear migration to long form and sponsored content that’s not just an interruption but instead it IS the story. Video machine learning is new and millions of videos can benefit from programmatic visual control. Why machine learning? Marketers don’t care what algorithms you’re using they just want to see:
Case study on the movie trailer “Birdman” Click to play lift achieved 3000% using machine learning technology.
Publishers are looking to achieve high KPI’s in order to increase overall spend while the media buyer is looking to lower CPA, without increasing costs. Publishers are trying to increase inventory and get the most out of their customer’s engagement. Machine learning enables both parties to achieve their goal by impacting revenue, efficiency and effectiveness simultaneously. With this technology publishers are empowered to keep the user video engagement high over significantly longer periods of time which is proving to be an invaluable tool that will become imperative to all successful video marketing efforts.
Gone are the days of simply tracking web page hits. A more sophisticated marketer has emerged where data is king. However, video distribution and analytics are complicated. Machine learning facilitates the systems ability to learn behavior and automatically adjust marketing efforts based on active feedback loops. This virtual neural network driven by human interaction with video content creates a meaningful data set providing the foundation for mobile video intelligence.
Graph shows real-time A/B testing of static image and KRAKEN image driven by machine learning. Machine learning makes it possible to stabilize and achieve lift.
Programmatic targeting reached an all time high of sophistication with it’s own machine learning and big data approach. Companies like RockFuel, Turn and eXelate have all perfected audience based targeting with advanced machine learning methods of aggregating massive sums of data to ensure that the right content is placed in front of the right people at the right time. The following are examples of machine learning techniques being used to enhance content engagement levels.
1. Algorithmic learning is used to determine what demographic segment responds well with specific content (e.g. videos).
2. Identification of habitual responses to visual objects by region allows for higher confidence of consumer engagement with content.
3. The type of content greatly affects the reaction of a targeted segment. Machine learning can track the visual preference of the video segments to give brands and content creators a new level of understanding as to what an audience will find most appealing.
4. Machine Learning can predict audience consumption. Plotting audience behavior across video types creates a consumption map, which can be used to predict things like video placement and cycle times.
5. Reduce video fatigue and increase engagement by rotation of static video images (thumbnails). Static starting images face image fatigue due to a lack of visual changes, color and motion alterations. Continuous and dynamic changes in a static video image will increase audience interest and result in higher click to play rates as well as completion rates.
Netflix has the capability to “predict” what you would watch next based on past viewing habits. Information like show/movie title and genre are compiled to help select Netflix’s recommendations. These algorithms are an example of something that pulls from the surface level information vs actual content within the video.
Visual content marketing is a very powerful method of attracting and retaining customers. Building a content story arch is key to perpetuating engagement and video is the most effective means to accomplish this. Publishers that leverage their audience to tune the video will achieve higher levels of revenue on their existing assets.
How do you see machine learning impacting video in the further and what video KPIs do you track that aren’t on the list? Let us know in the comments!