Video discovery is one of the best ways to increase video lifetime value. Learning what video content is relevant increases greater time on site.
All video publishers are looking to increase their video’s lifetime value. Creating video can be expensive and the shelf life of most video is short. Maximizing those videos assets and their lifetime value is a top priority. With the advent of new technologies such as Video Machine Learning, publishers can now increase their video’s lifetime value by intelligently generating more time on site. Identifying the best image to lead with (thumbnail) and recommending relevant videos drive higher lifetime value through user experience and discovery.
This combination of visual identification and recommendation is like the Reese’s of video. By linking technologies like artificial intelligence and real-time video analytics, we’re changing the video game through automated actionable intelligence.
Ryan Shane, our VP of Sales, describes the advantages of knowing what visual (video thumbnail) (context) produces the most engagement and what video business models benefit the most from video machine learning.
Hear from our CEO, Chase McMichael, who talks about the advanced use of machine learning and deep learning to improve video take rates by finding and recommending the right images consumers engage with the most.
Here are two examples of how video machine learning increases revenue on your existing video assets.
Yield Example #1: Pre-roll
If you run pre-roll on your video content, you likely fill it with a combination of direct sales and an RTB network. For this example, assume you have a 10% CTR, which translates to 1 million video plays each day. That means that you are showing 1,000,000 pre-roll ads each day. Now assume that you run KRAKEN on your videos, and engagement jumps to by 30% to a 12% CTR. That means that you will be showing 1,300,000 pre-roll ads each day. KRAKEN has effectively added an additional 300,000 pre-roll spots for you to fill! This is an example of increasing the video value on your existing consumers.
Yield Example #2: Premium Content
For our second example, assume you monetize with premium content. You have an advertising client who has given you a budget of $100,000 and expects their video to be shown 5 million times. With your current play rates, you determine it will take four days to achieve that KPI. Instead, you run KRAKEN on their premium content, and engagement jumps 2X. You will hit your client’s KPI in only two days. You now have freed up two days of premium content inventory that you can sell to another client! Maximizing your existing video consumers and increase CTR reduces the need to sell off network.
Below is a Side by Side example of Guardians Of the Galaxy Default Thumbnail vs. KRAKEN Rotation powered by Deep Learning. Boosting click rates generates more primary views. While leveraging known images that induce response is logical to insert into a video recommendation (Reese’s). The two together now drive primary and secondary video views.
As you can see from both examples, using KRAKEN actually increases lifetime value as well as advertising yield from your video assets. Displaying like base content sorted by Deep Learning and video analytics by category delivers greater relevance. Organizing video into context is key to increasing discovery. Harnessing artificial intelligence with image selection and recommendation brings together the best of both digital video intelligent worlds.
Bite into a Reese’s and see how you can increase your video lifetime value. Request a demo and we’ll show you.
Video viewability is a top priority for video publishers who are under pressure to verify that their audience is actually watching advertisers’ content. In a previous post How Deep Learning Video Sequence Drives Profits, we demonstrated why image sequences draw consumer attention. Advanced technologies such as Deep Learning are increasing video Viewability through identifying and learning which images make people stick to content. This content intelligence is the foundation for advancing video machine learning and improving overall video performance. In this post, we will explore some challenges in viewability and how deep learning is boosting video watch rates.
Side by Side Default Thumbnail vs. KRAKEN Rotation powered by Deep Learning
In the two examples above, which one do you think would increase viability? The video on the right has images selected by deep learning and automatically adjusted image rotation. It delivered a whopping 120% more plays than the static image on the left, which was chosen by an editor. Higher viewability is validated by the fact that the same video with the same placement at the same time achieved a greater audience take rate with images chosen by machine learning.
This boost in video performance was powered by KRAKEN, a video machine learning technology. KRAKEN is designed to understand what visuals (contained in the video) consumers are more likely to engage with based on learning. More views equals more revenue.
A/B testing is required when looking to verify optimization. For decades, video players have been void of any intelligence. They have been a ‘dumb’ interface for displaying a video stream to consumers. The fact was that without intelligence, the video player was just bit-pipe. Very basic measurements were taken, such as Video Starts, Completes, Views as well as some advanced metrics such as how long a user watched, etc. A new thinking was required to be more responsive to the audience and take advantage of what images people would reacted on. Increasing reaction increase viewability.
So how does KRAKEN do its A/B Testing? The goal was to create the most accurate measurement foundation possible to test for visuals consumers are more likely to engage with and measure the crowds response to one image vs another. KRAKEN implemented 90/10 splitting of traffic whereby 10% of traffic shows the default thumbnail image (the control) and 90% of traffic to the KRAKEN selected images. It is very simple to see why testing video performance through A/B testing is possible. Now that HTML5 is the standard and Adobe Flash has been deprecated, the ability to run A/B testing within video players has been furthered simplified.
Making sure a video is “in view” is one thing, but the experience has a great deal to do with legitimate viewability. A bigger question is: Will a person engage and really want to watch? People have a choice to watch content. It’s not that complex. If the content is bad, why would anyone want to watch it? If the site is known for identifying or creating great content then that box can be checked off.
Understanding what visual(s) makes people tick and get engaged is a key factor to increase viewability. Consumers have affinities to visuals and those affinities are core to them taking action. Tap into the right images and you will enhance the first impression and consumer experience.
What is Visual Cognitive Loading?
How the brain recognizes objects – MIT Neuroscientists find evidence that the brain’s inferotemporal cortex can identify objects. Visual induce human response using the right visuals increase attraction and attention. Photo: MIT
A single image is very hard to convey a video story with a single image. Yes, an image is worth a 1000 words but some people need more information to get excited. Video is a linear body of work that tells a story. Humans are motivated by emotion, intrigue and actions. Senses of sight and motion create a visual story that can be a turn on or turn off. Finding the right turn on images that tells a story is golden. Identifying what will draw them into a video is priceless.
The human visual cortex is connected to your eyes via the optic nerve; it’s like a super computer. Your ability to detect faces and objects at lightning speed is also how fast someone can get turned off to your video. Digital expectations are high in the age of digital natives. For this very reason, the right visual impression is required to get a video to stick, i.e. “sticky videos”. If you’re video isn’t sticky you will loose massive numbers of viewers and be effectively ignored just like “Banner Blindness”. The more visual information shown to a person the higher the probability of inducing an emotional response. Cognitive loading thereby gives them more information about what’s in the video. If you’re going to increase viewability you have to increase cognitive loading. It’s all about whether the content is worthy of their time.
Why Deep Learning
Deep Learning layers of object recognition. Understanding whats in the images is as valuable as the meta data and title. Photo: VICOS
The ability to identify what images and why are a big deal over the previous method of “plug a pray”. Systems now can recognize what’s in the image and linking that information back in real time with consumer behavior creates a very powerful leaning environment for video. Its now possible to create a hierarchical shape vocabulary for multi-class object representation further expanding a meaningful data layer.
Quality video and actuate measurement are paramount when optimizing video. Many ask, Why are KRAKEN images better? The reality is they are because using deep learning to select the right starting images increases the probability of nailing the right images that consumers will want to engage with. Over time, the system gets smarter and optimizes faster. A real time active feedback mechanism is created continuously adjusting and sending information back into the algorithm to improve over time.
Because KRAKEN consists of consumer curated actions, proactive video image selection is made possible. We make the assertion that optimized thumbnails result in more engaged video watchers as proven by the increase in video plays. KRAKEN drives viewability and enable publishers move premium O&O rates as a result.
Viewability or go home
After the Facebook blunder or “miss calculating video plays” and other measurement stumbles major brands have taken notice …. if you want to believe this was just a “mistake.” A 3 second play in AUTO PLAY isn’t a play in a feed environment when audio is off according to Rob Norman of Group M. The big challenge is there really isn’t a clear standard, just advice on handling viewability from the IAB. However, the big media buyers like Group M are demanding more and requiring half the video plays have a click to play to meet their viewability standard. This is wake up call for video publishers to get very serious about viewability and advertiser to create better content. All agree viewability is a top KPI when judging a campaigns effectiveness. 2017 is going to be an exciting year to watch how advertisers and publishers work together to increase video viewability. See The state of video Ad viewability in 5 charts as the conversation heats up.
Beyond the deep learning hype, digital video sequence (clipping) powered by machine learning is driving higher profits. Video publishers use various images (thumbnails – poster images) to attract readers to watch more video. These “Thumbnail Images” are critical, and the visual information has a great impact on video performance. The lead visual in many cases is more important than the headline. More view equals more revenue it’s that simple. Deep learning is having significant impact in video visual search to video optimization. Here we explore video sequencing and the power of deep learning.
Having great content is required, but if your audience isn’t watching the video then you’re losing money. Understanding what images resonate with your audience and produce higher watch rates is exactly what KRAKEN does. That’s right: show the right image, sequence or clip to your consumers and you’ll increase the number of videos played. This is proven and measurable behavior as outlined in our case studies. An image is really worth a thousand words.
Below are live examples of KRAKEN in action. Each form is powered by a machine learning selection process. Below we describe the use cases for apex image, image rotation and animation clip.
KRAKEN “clips” the video at the point of APEX. Sequences are put together creating a full animation of a scene(s). Boost rates are equal to those from image rotation and can be much higher depending on the content type.
Consumer created clipping points within video
Creates more visual information vs. a static image
Highlights action scenes
Great for mobile and OTT preview
More than one on page can cause distraction
Overuse can turn off consumers
Too many on page can slow page loading performance (due to size)
Mobile LTE is slow and can lead to choppy images instead of a smooth video
Image rotation allows for a more complete visual story to be told when compared to a static image. This results in consumers having a better idea of the content in the video. KRAKEN determines the top four most engaging images and then cycles through them. We are seeing mobile video boost rates above 50%.
Smooth visual transition
Consumer selected top images
Creates a visual story vs. one image to engage more consumers
Ideal for mobile and OTT
Less bandwidth intensive (Mobile LTE)
Similar to animated clips, publishers should limit multiple placements on a single page
KRAKEN always finds the best lead image for any placement. This apex image alone creates high levels of play rates, especially in a click-to-launch placement. Average boost rates are between 20% to 30%.
Audience-chosen top image for each placement
Can be placed everywhere (including social media)
Ideal for desktop
Good with mobile and OTT
Static thumbnails have limited visual information
Once the apex is found, the image will never be substituted
Below are live KRAKEN animation clip examples. All three animations start with the audience choosing the apex image. Then, KRAKEN identifies (via deep learning) clipping points and uses machine learning to adjust to optimal clipping sequence.
HitFix Video Deep Learning Video Clipping to Action, Machine Learning adjust in real time
Video players have transitioned to HTML5 and mobile consumption of video is the fastest growing medium. Broadcasters that embrace advanced technologies that adapt to the consumer preference will achieve higher returns, and at the same time create a better consumer experience. The value proposition is simple: If you boost your video performance by 30% (for a video publisher doing 30 million video plays per month), KRAKEN will drive an additional $2.2 million in revenue (See KRAKEN revenue calculator). This happens with existing video inventory and without additional head count. KRAKEN creates a win-win scenario and will improve its performance as more insights are used to bring prediction and recommendation to consumers, thereby increasing the video process.
The elusive video search whereby you can search video image context is now possible with advanced technologies like deep learning. It’s very exciting to see video SEO becoming a reality thanks to amazing algorithms and massive computing power. We truly can say a picture is worth 1,000 words!
Content creators have fantasized about doing video search. For many years,, major engineering challenges were a road block to comprehending video images directly.
Video visual search opens up a whole new field where video is the new HTML. And, the new visual SEO is what’s in the image. We’re in exciting times with new companies dedicated to video visual search. In a previous post, Video Machine Learning: A Content Marketing Revolution, we demonstrated image analysis within video to improve video performance. After one year, we’re now embarking on video visual search via deep learning.
Behind the Deep Curtain
Video clipping powered by KRAKEN video deep learning. Identify relevance within video images to drive higher plays
Many research groups have collaborated to push the field of deep learning forward. Using an advanced image labeling repository like ImageNet has elevated the deep learning field. The ability to take video and identify what’s in the video frames and apply description opens up huge visual keywords.
What is deep learning? It is probably the biggest buzzword around along with AI (Artificial Intelligence). Deep Learning came from advanced math on large data set processing, similar to the way the human brain works. The human brain is made of up tons of neurons and we have long attempted to mimic how these neurons work. Previously, only humans and a few other animals had the ability to do what machines can now do. This is a game changer.
The evolution of what’s call a Convolution Neural Network, or CNN aka deep learning, was created from thought leaders like Yann LeCrun (Facebook), Geoffrey Hinton (Google), Andrew Ng (Baidu) and Li Fei-Fei (Director of the Stanford AI Lab and creator of ImageNet). Now the field has exploded and all major companies have open sourced their deep learning platforms for running Convolution Neural Networks in various forms. In an interview with New York Times, Fei-Fei said “I consider the pixel data in images and video to be the dark matter of the Internet. We are now starting to illuminate it.” That was back in 2014. For more on the history of machine learning, see the post by Roger Parloff at Fortune.
KRAKEN video deep learning Images for high video engagement
Image reduction is key to video deep learning. Image analysis is achieved through big number crunching. Photo: Chase McMichael created image
Think about this: video is a collection of images linked together and played back at 30 frames-a-second. Analyzing massive number of frames is a major challenge
As humans, we see video all the time and our brains are processing those images in real-time. Getting a machine to do this very task at scale is not trivial. Machines processing images is an amazing feat and doing this task in real-time video is even harder. You must decipher shapes, symbols, objects, and meaning. For robotics and self-driving cars this is the holy grail.
To create a video image classification system required a slightly different approach. You must handle the enormous number of single frames in a video file first to understand what’s in the images.
On September 28th, 2016, the seven-member Google research team announced YouTube-8M leveraging state-of-the-art deep learning models. YouTube-8M, consists of 8 million YouTube videos, equivalent to 500K hours of video, all labeled and there are 4800 Knowledge Graph entities. This is a big deal for the video deep learning space. YouTube-8M’s scale required some pre-processing on images to pull frame level features first. The team used Inception-V3 image annotation model trained on ImageNet. What’s makes this such a great thing is we now have access to a very large video labeling system and Google did massive heavy lifting to create 8M.
Google 8M Stats Video Visual Search
Top level numbers of YouTube 8M. Photo created by Chase McMichael.
The secret to handling all this big data was reducing the number of frames to be processed. The key is extracting frame level features from 1 frame-per-second creating a manageable data set. This resulted in 1.9 billion video frames enabling a reasonable handling of data. With this size you can train a TensorFlow model on a single Graphic Process Unit (GPU) in 1 day! In comparison, the 8M would have required a petabyte of video storage and 24 CPUs of computing power for a year. It’s easy to see why pre-processing was required to do video image analysis and frame segmenting created a manageable data set.
Google has beautifully created two big parts of the video deep learning trifecta. First, they opened up a video based labeling system (YouTube8m). This will give all in the industry a leg up in analyzing video. Without a labeling system like ImageNet, you would have to do the insane visual analysis on your own. Second, Google opened Tensoflow, their deep learning platform, creating a perfect storm for video deep learning to take off. This is why some call it an artificial intelligence renaissance. Third, we have access to a big data pipeline. For Google this is easy, as they have YouTube. Companies that are creating large amounts of video or user-generated videos will greatly benefit.
The deep learning code and hardware are becoming democratized, and its all about the visual pipeline. Having access to a robust data pipeline is the differentiation. Companies that have the data pipeline will create a competitive advantage from this trifecta.
Follow Google’s lead with TensorFlow, Facebook launched it’s own open AI platform FAIR, followed by Baidu. What does this all mean? The visual information disruption is in full motion. We’re in a unique time where machines can see and think. This is the next wave of computing. Video SEO powered by deep learning is on track to be what keywords are to HTML.
Visual search is driving opportunity and lowering technology costs to propel innovation. Video discovery is not bound by what’s in a video description (meta layer). The use cases around deep learning include medical image processing to self-flying drones, and that is just a start.
Deep learning will have a profound impact our daily lives in ways we never imagined.
Both Instagram and Snapchat are using sticker overlays based on facial recognition and Google Photo sort your photos better than any app out there. Now we’re seeing purchases linked with object recognition at Houzz leveraging product identification powered by deep learning. The future is bright for deep learning and content creation. Very soon we’ll be seeing artificial intelligence producing and editing video.
How do you see video visual search benefiting you, and what exciting use cases can you imagine?
Feature Image is YouTube 8M web interface screen shot taken by Chase McMichael on September 30th .
Deep Learning, image and object recognition are core elements to intelligent video visual analysis. Understanding context within and classification creates a strong use case for video deep learning. Digital video is exploding, however there are few leveraging the wealth of data and how to harness visual analysis. A true reinforced deep learning system using collective human intelligence linked with neural networks provides the foundation to a new level of video insights. We’re just at the beginnings of intelligent video and using this knowledge to improve video performance.
Chase McMichael talk at ACM on Hacking Video Via Deep Learning Photo: Sophia Viklund
Deep Learning Methods Within Video An End Game Application – We’ll explore the use cases of using deep learning to drive higher video views. The coming Valhalla of video Deep Learning is being realized in visual object recognition and image classification within video. Mobile video has and continues to transform the way video is being distributed and consumed.
We’re witnessing the largest digital land grab in video history. Mobile video advertising is the fastest growing segment projected to account for $25 billion worth of ad spend by 2021. Deep Learning and artificial intelligence are also growing within the very same companies who are jockeying for your cognitive attention. This confluence of video and deep learning has created a new standard in higher performing video content diving greater engagement, views, and revenue. In this post we’ll dive deep into how video intelligence is changing the mobile video game. Many studies showing tablet and smartphone viewing accounted for nearly 40 minutes of daily viewing in 2015 with mobile video continuing to dominate in 2016. Moreover, digital video is set to out pace TV for the first time and social / Instagram/Snapchat video is experiencing explosive growth.
The Interstellar trailer is a real example of KRAKEN in action and achieved a 16X improvement in video starts. Real-Time A/B testing between the poster image (thumbnail) and selected images pulled from visual training set provide the simultaneous measurement of what image induce engagement. All data and actions are linked with a Video Machine Learning (KRAKEN) algorithm enabling real-time optimization and sequences of the right images to achieve maximum human engagement possible.
How it works
Processing video at large scale and learning requires advanced algorithms designed to ingest real-time data. We have now entered the next phase of data insights going beyond the click and video play. Video opens the door to video consumption habits and using machine learning enables a competitive advantage.
Consumer experience and time on site are paramount when video is the primary revenue source for most broadcasting and over-the-top (OTT) sites today including Netflix, HULU, Comcast X1, and Amazon. Netflix has already put into production their version of updating poster images to improve higher play starts, discovery and completions.
It’s All Math
Images with higher object density have proven to drive higher engagement. The graph demonstrates images with high entropy (explained in this video) generated the most attraction. Knowing what images produce a cognitive response are fundamental for video publishers looking to maximized their video assets.
Top 3 video priorities we’re hearing from customers.
1) Revenue is very important, and showing more video increases revenue (especially during peak hours when inventory is already sold out)
2) More video starts means more user time on site
3) Mobile is becoming very important. Increasing mobile video plays is a top priority.
While this is good news overall, it does present a number of new challenges facing video publishers in 2016. One challenge is managing the consumer access to content on their terms and across many points. Video consumption is increasingly accessed through multiple entry-points throughout the day. These entry points, by their very nature, have context.
Broadcasters and publishers must consider consumer visual consumption as a key insight. These eye balls (neurons firing) are worth billions of dollars but its no longer a game of looking at web logs. More advance image analysis to determine what images work with customers requires insights into consumers video consumption habit. For the digital broadcasters, enabling intelligence where the consumer engages isn’t new. Using deep convolutional neural networks powers the image identification and other priority algorithms. More details are in the main video.
Visual consumer engagement tracking is not something random. Tracking engagement on video has been done for many years but when it comes to “what” within the video there was a major void. InfiniGraph created KRAKEN to enable video deep learning and fill that void by enabling machine learning within the video to optimize what images are shown to achieve the best response rates. Interstellar’s 16X boost is a great example of using KRAKEN to dive higher click to launch for autoplay on desktop and click to play in mobile resulting in higher revenue and greater video efficiency. Think of KRAKEN as the Optimizely for video.
One question that comes up often is: “Is the image rotation the only thing causing people to click play?” The short answer is NO. Rotating arbitrary images is annoying and distracting. KRAKEN finds what the customer likes first and then sequences the images based on measurable events. The right set of images is everything. Once you have the right images you can then find the right sequence and this combination makes all the difference in maximizing play rates. Not using the best visuals will cause higher abandonment rates.
Further advances in deep learning are opening the doors to continuous learning and self improving systems. One are we’re very excited about is visual prediction and recommendation of video. We see a great future of mapping human collective cognitive response to visuals that stimulate and created excitement. Melting the human mind to video intelligence is the next phase for publishers to deliver a better consumer experience.
Chase McMichael, NAB VIDEO Intro – Top Video Platforms and Video Machine Learning made a big splash at NAB 2016.
The event was all about digital video, video production, VR, drones and every other technology you could imagine. Think of NAB as the as the CEO of digital and video broadcasting. Everywhere you looked there was drone technology, robotics and even a full area dedicated to VR. The future of video publishing is bright for sure as new technology simplifies quality capture and distribution. We took the time to connect with some of our video platform partners at NAB. Our one-on-one interviews were with Ooyala, Brightcove, and Kaltura. Each video platform provided a comprehensive walkthrough of their latest development and demos. What stood out the most was the big push in Over The Top (OTT) supporting broadcasters. OTT was a big theme for many video platforms, and all show amazing on-demand video technology. Everyone has seen Netflix and Hulu interfaces and are now becoming serious about OTT. Visuals are everything in OTT interfaces and using the power of intelligence is a key differentiation. Netflix identifies this fact in “Selecting the best artwork for videos through A/B testing”
The consumer has gone mobile in a big way, and digital video is taking on TV. Consumers want access to on-demand video wherever they are and on their terms. User experience was also a big draw, too. There is no question that lines have been drawn with rumblings of opening up the Set Top Box and unbundling the TV. Apple TV and Roku started to look like a yesteryear technology compared with the OTT interfaces and mobile native app interfaces being demoed. Brightcove released an OTT Flow and a very exciting interface for a video library and we got a first-hand view of a super slick mobile interface to digital video consumption. Kaltura also showed off what they did for Vodafone. The video platforms seem well positioned to service a TV Everywhere strategy and feed into the Apple TV and Roku devices.
Another part of the demonstrations on each platform that we experienced was 360 video support. Each player had mouse controls whereas Ooyala demonstrated split screen view supporting Google Cardboard. There is an exciting future in VR content and all are waiting to see what’s going to come out from a content perspective. Beyond linear video, immersive storytelling has a great future and we hope that technology doesn’t encumber the adoption and create friction for the experience. The speed of video player loading, streaming efficiency and low buffer rates have always been major competitive advantages when video publishers evaluate platforms.
A big topic was the relatively new Apple standard HLSjs streaming protocol. DASH by Microsoft was also discussed at various booths. All players support HTML5 with a focus on migrating customers away from the old Adobe Flash technology. Every platform demonstrated to use of HLSjs/HTML5. Kaltura shows a real-time side-by-side with an impressive HTML5 player load speed of 50% improvement. Improving load time and streaming will continue to benefit the mobile web and autoplay world. Video is everywhere and customers are demanding more of it. All video publishing platforms had very well organized video management and publishing capabilities. The big takeaways are that the platforms are focused on simplification in publishing and handling a large volume of video with greater intelligence built-in. Obviously, this is important when serving video and creating a better video viewing experience. Here are the top 4 most mentioned attributions for all the platforms.
Availability - percentage of times video playback starts successfully
Start Up Time - time between the play button click and playback start
Rebuffers - number of times and the duration of interruptions due to re-buffering
Bitrate - average bits per second of video playback. The higher the bitrate, the better the experience
All of our conversation centered around using intelligence within thumbnail selection and the process of integration. KRAKEN video machine learning has a bright future with the onslaught of OTT platforms offering more video carousel and indexes as part of the central interface for video discovery. Next up is video prediction (recommendation) and using data to make smarter decisions on what to watch next. There are some very positive results coming from companies like Iris.tv and JW Player. Look for our next post coming from Stream Media East. Catch more on our last podcast here “Thumbnails are part of a Video Marketing Strategy”
We had the unique opportunity to talk with Tom Morrissy our Board Advisor in Time Square NY. Below is the transcript between Tom and Chase talking about his perspective on publishers and KRAKEN our Video Machine learning technology.
Chase: Hi I’m Chase McMichael CEO and Co-founder of InfiniGraph and I’m here today with Tom Morrissy our Board Advisor. Hi Tom.
Tom: Hi Chase.
Chase: So Tom tell me a little about your experience working with us obviously you’re a midi ex media guy from SpinMedia, Timing you’re basically better. So tell us a little about your experience working with us and obviously you know more about the media space than we do. Now we really like to have your viewpoint on you know knowledge space, some of the issues. What do we do to really break in and arbitration within you know obviously cracking mobile videos big deal? So tell me what you think?
Tom: One thing that we’re finding as we talk to different publisher was just one of the reasons that I joined the company is all have very similar pain points. There’s a certain buried entry with video. There’s only so much video they can create but getting it seen and consumed by consumers is a huge challenge. So, how can we excite the consumer to be more excited and excited enough about the content that they are viewing to actually click to play and actually watch it and engage more deeply on these websites and as a factor of that we create more inventory for the publishers to monetize their video plays.
Really what it comes down to its combining a better User experience and starting with that and then taking the better User experience and being able to monetize it in a much more accelerated way. That’s the magic on what this company has done in my eyes because it took the publishers view through the User experience. Most Ad Tech starts with how we can make more money off this User with our technology does is empowers the user and that’s a vital difference relative to all the other technologies I’ve seen.
Having been on the publisher’s side and then on the Ad Tech side and then back to the publisher’s side. I was getting email after email, LinkedIN after LinkedIN request saying I’ve got a seamless integration opportunity for you revenue generating and the truth and matter is there is no seamless integration. Anybody who promises that is not telling the full truth because we all know that’s not the case.
So, the question becomes as a publisher what do you want to do and where do you want to spend your time and resources to integrate what kind of impact does it going have in your audience and then what kind of impact ultimately will have on your business. That’s what InfiniGraph is taking into account. We created the most frictionless integration opportunity that I’ve seen from seeing all the viewpoint of a publisher and figuring out a way to excite the reader and therefore grow the business.
Chase: Yeah we are just embedding it on the video player how much lower touch we can get you know we’re not touching the CMS don’t change the workflow that’s a death nail. One of the things you say a lot “its about the content especially premium content”. I like this whole thing where you talking about where we’re really going for this from a content angle. Not the ad angle and how are we going to juice the User and get the much money out of them. There’s some importance there for some of the publisher but reality is that they play an incredible strong content game they are going to lose the audience. So talk a little more about that.
Tom: The truth of the matter is, you don’t have that much time to engage the audience you can have killer content but the audience may not choose to view it for whatever reason. You got to figure out ways to engage them and get into what we call cognitive thinking and making the commitment to watching the content that is you can have the best movie you can have the best video clip if nobody watches it is like a tree falling in the forest right.
Chase: Yeah, that’s a great point because I know on mobile, we’ve been measuring you got 0.25 seconds man and if you’re not “thumbstopping” The viewer is just scrolling and scrolling you’re going to miss them. The video is a linear body of work and there is so much content there but then you’re starting out with just one image. What do you do?
Tom: Right, you have to figure out what’s going to motivate that person because think about it they have chosen to go to that space to watch a video and ninety percent of them on average are not. So why?
Chase: Yes they are bailing.
Tom: How can we make a better experience and make them more motivated to watch the video in the first place that they are there to watch in the first place. That’s the weirdest thing about this market. That’s what keeps me up at night and what keeps you up at night to try to solve for that. That’s what our company I think the promise of what we’re trying to do is really help lead the consumer to the choice they always told themselves they want to make.
Tom: And, if you can do that then you have a much more robust relationship with your consumer to spend more time on your site, they watch more video which is why they’re there and then you as a publisher make more money. It’s as simple as that.
Chase: Right. Thanks Tom man. It’s great to have you on board and awesome here to be in the big apple. And hey viewers, click up on the (i) up on the right here to see some more information and we will back at you soon. Thank you.
VIDEO – Better User Experience, Time on Site and Converting Readers into Viewers.
Video Optimization With Machine Learning is now a reality and publishers are intelligently making the most out of their O&O digital assets. The digital video industry is undergoing a transformation and machine learning is advancing the video user experience. Mobile, combined with video, is truly the definitive on-demand platform making it the fastest growing sector in digital content distribution.
Video machine learning is a new field. The ability to crowd source massive human interactions on video content has created a new data-set. We’re tapping into a small part of the human collective conscious for the first time. Publishers and media broadcasters are now going beyond the video view, clicks, and completions to actually obtaining introspection into video objects, orientations and types of movements that induce positive cognitive response. This human cognitive response is the ultimate in measurement of relevance where humans are interacting with video in a much more profound way. In this article, we will dive deep into the four drivers of video machine learning.
Video by its nature is linear, however, there are several companies working to personalize the video experience as well as make it live. We’re now in an age where the peak of hype on Virtual Reality / Augmented Reality will provide the most immersive experience. All of these forms of video have two things in common: moving sights and sound. Humans by nature prefer video because this is how we see the world around us. The bulk of video consumed globally is mostly designed around a liner body of work that tells a story. The fact that the video is just a series of images connected together is not something people think much about. In the days of film, seeing a real film strip from a movie reel made it obvious that each frame was in fact a still image. Now fast forward, digital video has frames but those frames are made up of 1’s and 0’s. “Digital” opens the door to advance mathematics and image / object recognition technologies to process these images into more meaning than just a static picture.
It’s hard to believe how important images really are. For videos placed “above the fold,” you have to wonder why so many videos have such a low play rate to begin with (Video Start CTR). Consumers process objects in images within 13 milliseconds (0.013 seconds). That’s FAST! Capturing cognitive attention has to be achieved extremely fast for a human to commit to watching a video and the first image is important, but not everything. More than one image is sometimes required to assure a positive cognitive response. The reality is people are just flat out dismissive and some decide not to play the video. This is evident when you have a 10% CTR, which means 90% of your audience OPTED OUT OF PLAYING THE VIDEO. What happened? The facts are the first image may have been great but didn’t create a full mental picture of what was possible in the linear body of work. The reality is you’re not going to get 100% play rates, however, providing greater cognitive stimulation that builds relevance will drive greater reasons to commit time to watching a linear form of video.
Machine Learning and Algorithms
In the last 4 years, machine learning / artificial intelligence has exploded with new algorithms and advanced computing power has greatly reduced the cost of complex computations. Machine learning is transforming the way information is being interpreted and used to gain actionable insights. With the recent open sourcing of TensorFlow from Google and advances in Torch from Facebook, these machine learning platforms have truly disrupted the entire artificial intelligence industry.
Feature extraction and classification is key to learning what’s in the image that is achieving positive response.
Major hardware providers, such as NVIDIA, have ushered massive advancements in the machine learning and AI fields that would have otherwise been out of reach. The democratization of machine learning is now opening the doors to many small teams to propel the product development around meaningful algorithmic approaches.
The unique properties of digital video specifically in a consumer’s mobile feed, delivered from a video publishing site, creates a perfect window into how consumers snack on content. If you want to see hyper snacking, ride a train into a city or watch kids on their smartphones. Digital content consumption has never been so interactive than now. All digital publishers and broadcasters have to ask themselves this question, “How is my content going to get traction with this type of behavior?” If your audience is Snapchatters, YouTubers, or Instagramers you’re going to have to provide more value in your content V I S U A L Y or you will lose them in a split second.
Graphs – Video Views (Mobile-KMView / Desktop-KDView) vs. Minutes in a day – 1440 min = 24 hrs. Mobile is dominating the weekend where as work week, during commute and after work, skyrockets in usage. Is your video content adapting to this behavior?
Video Publishing Conundrum
A big conundrum is why people are not playing videos. This required further investigation. We found that the lead image (i.e. the old school “thumbnail”, or “poster image”) had a huge impact on introducing a cognitive response. In the mobile world, video is still a consumer driven response and we hope this will stay a click to play world. We believe consumer choice and control will always win the day. For video publishers, under the revenue gun, consumers will quickly tire of native ad content tricks, in-stream video (auto play), and the bludgeoning and force feeding of video on the desktop. No wonder ad-blocking is at an all time high! There is a whole industry cropping up around blocking ads and it’s an all out war. The sad part is the consumer is stuck in the middle.
Many publishers are using desktop video auto-play to reduce friction, however the FRONT of the page, video carousel, or gallery is a click to launch environment making the images on the published page even more important. Those Fronts are the main traffic driver over possible social share amplification. As for mobile video, it’s still a click to play world for a majority of broadcasters and publishers. Video is the highest consumer engaging vehicle at their disposal and it is why so many publishers are forcing themselves to create more video content. Publishing more video oriented content is great, however, the lack of knowledge of what consumers emotionally respond to has been a major gap. A post and pray or post and measure later system is currently prevalent throughout the publishing industry.
Video Quality matters
Creating a better consumer experience is everything if you want your content to be consumed in the days where auto-play is rampant and force fed content is inducing engagement. More brands demand measured engagement. Video engagement quality is measured by starts, length of time on video, and physical actions taken. Capturing human attention is very hard due to many distractions, especially on a mobile device. We’re in a phase where the majority of connected humans are now digital natives in this digital deluge. ADD is at an all time high (link). With < .25sec to get the consumer to engage before they have formulated the video story line in their mind is a hard task. A quick peak on the video thumbnail fast read of a headline and glance of some keywords could be standing between you and a revenue generating video play. People are pressed with their time and unwillingness to commit to a video play unless it induces a real cognitive response. Translating readers into video viewers is important and keeping them is even more important.
Mobile Video and Machine Learning
Mobile is becoming the prevalent method of on demand video access. This combination of video and mobile is an explosive pair and most likely the most powerful marketing conduit ever created. Here we have investigated how machine learning algorithms on images can provide a real-time level of insight and decision support to catch the consumer’s attention and achieve higher video yield otherwise lost. The big challenge with video is it created in a linear format and then loaded in a CMS put up for publishing and pray it gets traction. Promotion helps and placement matters, however, there is really nothing a publisher can do to adjust the video content once out. Enter video intelligence. The ability to measure in real-time video engagement is a game changer. Enabling intelligence within video seems intuitive, however, the complexity of encoding and decoding video has great a sufficient barrier of entry that this area of video intelligence has been otherwise untapped.
How and Why KRAKEN Works
Here we dive deep into consumers looking to interact with certain visual objects to create a positive response before a video is played. InfiniGraph invented a technology called KRAKEN that actually shows a series of images, but the series of images we call “image rotation” is not really new. What’s new is the actual selection and choice of those images using machine learning algorithms allowing us to adjust those images to achieve highest human response possible.
GRAPH – LIFT by KRAKEN mobile (KMLIFT) vs. desktop (KDLIFT) on same day. NOTE the grouping prior and after lunch had overall higher boost by KRAKEN. We attribute this behavior due to less distraction.
As more images are processed by KRAKEN, the system becomes smarter by selecting better lead images driving higher video efficiency. This entire process of choosing which order to sequence the best is another part of the learning mechanism. Image sequencing is derived from a collection of 1 to 4 images. These images are being selected based upon KRAKEN ranking linked with human actions. Those visual achieved the highest degree of engagement will receive a higher KRAKEN rank. The actual sequence also creates a visual story maximizing the limited time to capture a consumer’s attention.
KRAKEN in Action
KRAKEN determines the best possible thumbnails for any video using machine learning and audience testing. Once it finds the top 1-4 images, it rotates through them to further increase click-to-play rates. It also A/B tests against the original thumbnail to continually show its benefits. Here are 2 real examples:
KRAKEN Thumbnails with 273% lift below. What makes a good video lead image unique? We’re asked this question all the time. Why would someone click on one image versus another? These questions are extremely context and content dependent. The actual number of visual objects in the frame has a great deal to do with humans determining relevance, inducing intrigue or desire. The human brain sees shapes first in black / white. Color is a third response however red has it’s on visual alerting system. The human brain can process vast sums of visual information fast. The digital real estate such as mobile or desktop can be vastly different. A great example is what we call information packaging where a smaller image size on a mobile phone may only support 2 or 3 visual objects that a human would quickly recognize and induce a positive response whereas the desktop could support up to 5. Remember one size doesn’t fit all especially in mobile video. KRAKEN Thumbnails with 217% lift to the left. Trick your brain: black and white photo turns to colour! – Colour: The Spectrum of Science – BBC
4 drivers of video machine learning
Who benefits from video machine learning? The consumer benefits the most because of increased consumer experience due to creating a more visually accurate compilation of what the video content’s best moments are. It’s critical that people get a sense of the video so they commit to playing the video and sticking around. Obviously the publisher or broadcaster benefits financially due to more video consumption yielding to higher social shares.
Color depth: remember bright colors don’t always yield the best results. Visuals that depict action or motion elicit a higher response. Depending on the background can greatly alter color perception, hence images with a complementary background can enable a human eye to pick up colors that will best represent what they are looking at creating greater intrigue.
Image sequencing: Sequencing the wrong or bad images together doesn’t help but turns off. The right collection is everything and could be 1 to 4. Know when to alter or shift is key to obtaining the highest degree of engagement. The goal is to create a visual story that will increase consumer experience.
Visual processing: The human brain can process vast amounts of visual information fast. The digital real estate such as mobile or desktop can differ. A great example is what we call “information packaging” where a smaller image size on mobile phone screen may only support 2 or 3 visual objects in view. Humans can quickly recognize and induce a positive response whereas the desktop could support up to 5. One size doesn’t fit all especially in mobile video.
Object classification: Understanding what’s in an image and classify those images provides a library to top performing images. These images with the right classification create a unique data set for use in recommendation to prediction. Knowing what’s in the image as just as important as knowing it was acted on.
The first impression is everything or maybe the second or third if you are showing a sequence of images. For publishers and digital broadcasters adapting to their customers content consumption preferences and being on platforms that will yield the most will be an ongoing saga. Nurturing your audience and perpetuating their viewing experience will be key as more and more consumer move to mobile. KRAKEN is just the start of using machine learning to create a better user experience in mobile video. We see video intelligence expanding into prediction to VR / AR in the not too distantd future. As this unique dataset expands we look forward to getting your feedback on other exciting use cases and finding ways to increase the overall yield on your existing video assets.
Tell us what you think and where you see mobile video going in your business.
A Series of Forbes Insights Profiles of Thought Leaders Changing the Business Landscape: Chase McMichael, Co-Founder and CEO, InfiniGraph
Optimizing web content to drive higher conversion rates, for a long time, meant only focusing on boosting the number of click-throughs, or figuring out what kinds of static content got shared most often on social media sites.
But what about videos? This key component of many sites went largely overlooked, because there simply wasn’t a good way to determine what actually made viewers want to click on and watch a given video.
Chase McMichael, Co-Founder and CEO, Infinigraph
In an effort to remedy this problem, says entrepreneur Chase McMichael, brand managers may have, at most, tried to simply improve the video’s image quality. Or, in a move like a Hail Mary pass, they might have splashed up even more content, in the hopes that something, anything, would score higher click-to-play rates. Yet even after all that, McMichael says, brands often found that some 90% of viewers still did not watch the videos posted on their sites.
As it turns out, the “thumbnail” image (static visual frame from the video footage) has
everything to do with online video performance. And while several ad tech companies were already out there, using so-called A/B testing to determine how to optimize the user experience, no one had focused on optimizing video thumbnail images. Given video’s sequencing speed with thousands of images flashed up for milliseconds at a time, it meant that measuring the popularity of thumbnails was simply too complex.
Sensing a challenge, McMichael, a mathematician and physicist with an ever-so-slight east Texas drawl, set out to tackle this issue. He’d already started InfiniGraph, an ad tech firm aimed at tracking and measuring people’s engagement on brand content. But as his company grew, he found that customers began asking more and more about how they might best optimize web videos in order to boost viewership.
Viewership, of course, is key: Higher video viewership translates into more shares; more shares means increased engagement. And that all translates into more revenue for the website. Premium publishers are limited in their ability to create more inventory because the price of entry is so high. These new in house studios are producing quality content, but getting scale is a huge challenge.
When he started looking into it, McMichael says, he often found that the thumbnails posted to attract viewers usually fell flat and the process for choosing thumbnails hasn’t changed in 15 years. And the realization that the images gained little to no traction among viewers came as something of a surprise: Most of the time, the publishers and brand managers themselves had selected specific images for posting with no thought at all into optimizing the image.
According to McMichael, the company’s technology (called “Kraken”) solves for two critical areas for publishers: it creates inventory and the corresponding revenue while also increasing engagement and time spent on site.
Timing, it turns out, was everything for McMichael and InfiniGraph. Image- and object-recognition software had been improving to the point where those milliseconds-at-a-time thumbnails could be slowed down and evaluated more cheaply than in the past. Using that technology along with special algorithms, McMichael created Kraken, a program that breaks down videos into “best possible” thumbnails. Using an API, Kraken monitors which part of the video, or which thumbnail, viewers click on the most. Using machine learning, Kraken then rotates through and posts the best thumbnails to increase the chances that new users will also click on the published thumbnail in order to watch an entire video.
This process is essentially crowd-sourced, says McMichael—the images that users click on the most are those that Kraken pushes back to the sites for more clicks. “What’s fascinating is we’ve had news content, hard news, shocking, all the way up to entertainment, music, sports and it’s pretty much universal,” he says, “that no one [person] picks the right answer”—only the program will provide the best image or images that draw in the most clicks. On its first few experimental runs, InfiniGraph engineers discovered something huge: By repeatedly testing and re-posting certain images, InfiniGraph saw rates of click-to-play increase by, in some cases, 200%. Says McMichael: “It was like found money.”
InfiniGraph is a young and small company, even for a start-up: The Silicon Valley firm has eight employees in addition to a network of technicians and specialty consultants he scales on and as-needed basis, and has boot-strapped itself to where it is today. McMichael says he’s built a “very revenue-efficient company” because “everything is running in two data centers and images distributed across a global CDN.” His goal is to be cash-flow positive by this summer. Right now InfiniGraph works exclusively with publishers but the market is ripe for growth, especially in mobile devices, McMichael says.
Recently, Tom Morrissy, a publishing leader with extensive experience in both publishing (Entertainment Weekly, SpinMedia Group) and video ad tech (Synaptic Digital, Selectable Media) joined InfiniGraph as a Board Advisor.
“So many companies claim to bring a ‘revenue generating solutions that is seamlessly integrated.” This product creates inventory for premium publishers and is the lightest tech integration I’ve seen. I was completely impressed with Chase’s vision because he truly thought through the technology from the mindset of a publisher. Improve the consumer experience and the ad dollars always follow” says Tom Morrissy
The son of a military officer father and registered nurse mother, McMichael grew up in the small town of New Boston, Texas, located just outside of the Red River Army Depot. A self-described “Brainiac kid,” McMichael says he was always busying himself with science experiments, with a special interest in superconductors, or materials that conduct electricity with zero resistance. Though he’d been accepted to North Texas, McMichael still took a tour at the University of Houston, mainly because the work of one physics professor who discovered high temperature superconductivity had grabbed his attention. “So I went to Paul Chu’s office and said, ‘hey, I want to work for you.’” It was the craziest thing, but growing up I was always told, ‘If you don’t ask for it, you won’t know.’”
That spawned the beginning of seven-year partnership with Chu during which time the University built a ground-breaking science center. McMichael spent seven years in DARPA funded applied science, but decided to leave for the business world. A friend of McMichael’s worked at Sun Microsystems and encouraged him to leverage his programming knowledge. His first job out of college was creating the ad banner management system for Hearst. “So I got sucked into the whole internet wave and left the hard-core science field,” he says. He also worked at Chase Manhattan Bank in the 90s, building out its online banking business.
As for the future for InfiniGraph?
McMichael says his mission is “to improve the consumer experience on every video across the globe, and it’s an ambitious plan. But we know that there are billions of people holding a phone right now looking at an image. And their thumb is about to click ‘play,’ and we want to help that experience.”
Bruce H. Rogers is the co-author of the recently published book Profitable Brilliance: How Professional Service Firms Become Thought Leaders - Originally posted on Forbes