3 Ways Video Recommendation Drives Video Lifetime Value

Video recommendation and discovery are very hot topics across video publishers looking to drive higher returns on their video lifetime value. Attracting a consumer to watch more videos isn’t simple in this attention deficit society we live. Gone in 90 Seconds according to Netflix. Your audience is one swipe away from being on another experience. Fluid media shifting is just life. However, video publishers are finding ways to keep consumers engaged using higher video intelligence. Want to make an impact in your consumer experience? Then make it simple to discover and surface relevant video content they find interesting. In this post we’ll explore the intelligence behind visual recommendation and what’s being leveraged to increase video lifetime value. VIDEO: Chase McMichael gives talk at Intel on how to process massive amounts of video on a budget and why visual computing attracts more attention to video.

Don’t be fooled

Google Video Intelligence Demo At Google Next 17

Google Video Intelligence API demo of video search finding baseball clips within video segments.

Enough with the buzz words around Artificial Intelligence, Machine Learning and Deep Learning. What problem are you solving?  Is there a learning system and automated method to create a better solution? Last year we posted on Search Engine Journal How Deep Learning Powers Video SEO describing the advantages of video image labeling. Since then, Google announced at Next17 a full video annotation platform call Video Intelligence . (InfiniGraph was honored to be selected as a Google Video Intelligence Beta Tester) Beyond Google having a huge cloud systems running on chips design for deep learning (TPU) to pull this, this massive video processing capability comes with a cost. We’re still in the very early days of video analysis. The MAJOR challenge with Google cloud offering is pushing all your video over to Google Cloud and 2nd is letting your DATA be used as part of their training set. This is very problematic on many levels due to content rights and Google becoming smarter on your video than you are. How do you achieve similar results without all this overhead?

Not all data is created equal

Trending Content - Lacks Image Based Video Machine Learning

Trending Content is based on popularity vs content context and the consumer content consumption.

All video publishers have the standard meta data attached to their videos when loading a video in their CMS.  Behavior tracking is very powerful if you have the consumers consent. Many consumers don’t want to be tracked, if they are not logged into your property. Complicating matters, there are communal devices in many homes. As for the mobile device, (iPhone etc.) this is VERY PERSONAL and tracking is possible BUT Apple and Google have taken steps to block 3rd parties tracking. Single party tracking will be in place, however; a standard has yet to be fully adopted. Gone are the good old day of “dropping a cookie”. Creating a truly personalized experience is ideal, however; depends on the consumer authorizing and receiving value for giving up their privacy. OTT apps provides the best path to robust personalization. We have learned a great deal from innovative companies like: Netflix, HULU, YouTube and Amazon who have all come a long way in their approach to advanced video discovery.  So how do you leverage these innovations on a budget?

See how “Netflix knows it has just 90 seconds to convince the user it has something for them to watch before they abandon the service and move on to something else”.

Video recommendation platforms

Video Recommendation Mantis Powers by KRAKEN Video Machine Learning

Image based video recommendation “MANTIS”. Going beyond simple meta data and trending content to full intelligent context. Powered by KRAKEN.

Not all video recommendation platforms are created equal. The main challenge is every mouse trap is virtually using the same META data and behavior tracking does not create meaningful discovery to new content. The heavy reliance on what people have played hence popular video must be what everyone wants to watch. Right? Popularity is not a barometer of relevance and vast majority of you’re video content isn’t seen by the majority of your audience. Hence good video content that lacks engagement will not be surfaced at all. This is your most expensive content like what’s the most expensive table in a restaurant? The empty table.

Video Machine Learning, Going beyond meta data is key to a better consumer experience. Trending only goes so far. Visual recommendation looks at all the content based on consumer actions.

Going beyond meta data is key to a better consumer experience. Trending only goes so far. Visual recommendation looks at all the content based on consumer actions.

To exacerbate the problem, trending videos are a self fulfilling prophecy because trending is being artificially amplified and doesn’t indicate relevance. Surfacing the right video at the right time can make all the difference in people staying or going.  What videos got play, time on video and completion indicates watchablity and captured interest. There is so much more to a videos than raw insights. Did someone watch a video is important but understanding the why in context of other like videos with similar content is intelligence. YouTube has been recommending videos for a long time but until recently started leveraging AI to build intelligent personalized video play lists. So has Netflix, HULU and Amazon to some extent. There are a few 3rd party platforms in the space when it comes to video recommendation. However, very few have tapped into visual insights to achieve higher intelligence. Companies like Iris.tv being an early entry in video recommendation and latter like Prizma and BABATOR all have unique Meta data tracking algorithms designed to entice more people to stay longer mostly on desktop auto play video. Now with the Viewablity demand increase and requirement to verify viewablity more advance methods of assuring people are watching the content is required. Hence, a new thinking on video recommendation was mandated.

An Intelligent Visual Approach

A definitive differentiation is using the images and video segments within the video to build relevance. Consumer know what they like when they see it. Understand this visual ignition process was key to unlocking the potential of visual recommendation.  A visual psychographic map now can be created based on video consumption. How do you really know what people would like to play if you really don’t know much about the video content? Understanding the video content and context is the next stage in intelligent video recommendation and personalized discovery. Dissecting the video content and context now opens up a new DATA set that was other wise trapped behind a play button.

3 Ways Visual Video Recommendation Drives Video Lifetime Value

1. Visual recommendation – Visual information within video creates higher visual affinity to amplify discovery. Content likeness beyond just meta data opens up more video content to select from. Mapping what people watch is based on past observation, predicting what people will watch requires understand video context.

2.  Video scoring – A much deeper approach to video had to be invented where the video is scored based on visual attribution inside the video and human behavior on those visuals. This scoring lets the content SPEAK FOR ITSELF and enables ordering play list relative to what was watched.

3. Personalized selection - Enhancing discover requires getter intelligence and context to what content is being consumed. Depending on the video publishers environment like OTT or a mobile app can enable high levels of personalization. For consumers using the web a more general approach and clustering consumers into content preferences powers better results while honoring privacy.

The Future is Amazing for Video Discovery

Google, Amazon, Facebook and Apple are going head to head with deep video analysis in the cloud. Large scale video publishers have a grand opportunity to embrace this new technology wave and be relevant while creating a visually inducive consumer experience.  Video annotation has a very bright future using a technology called Deep Learning. We have come a very long way from just doing single image labeling via ImageNet. A major challenge going forward is the speed of change video publishers must adapt if they wish to stay competitive. With advanced  technologies designed for video publishers there is hope. Take advantage of this movement and increase your video lifetime value.

Top image from post melding real life with recommendations.

Video Publishers Ready for Video Autoplay Shutdown.

deer-in-headlights Publishers need intelligence via Machine Learning KRAKEN video artificial intelligence Video publishers have been caught off guard with the recent announcement of Apple blocking video autoplay. Even Google is pushing back on bad web ads. The backlash against video autoplay has been festering for some time. If losing video ad revenue and turning consumers off with declining traffic isn’t a wake-up call then what will be? Headlines like this from CNN “Apple’s plan to kill autoplay feature could leave publishers in the dust” should get video publisher’s attention. This clamp down isn’t a joke and Google and Apple are taking a hardline to clean up the web experience when it comes to video. Here we dive deep into how to get ahead of these changes by Apple/Google and increase your video lifetime value.

Facebook started the conversation

Since Facebook started force-feeding video autoplay on us, other publishers followed suit knowing their video volume would go up. However, some major agencies flat out said they would only pay half of the CPMs due to the viewability issues with autoplay. A major advertiser (Heineken) is publicly having challenges getting a 6 sec clip to stick. Publishers say the video relationship with Facebook is “complicated”. This is a topic of constant discussions and other players are outright opting out of video autoplay altogether in favor of a better consumer experience. Apple Autoplay Blocking iOS11 KRAKEN Video Machine Learning is the SolutionThe major catch 22 here is that publishers driving their O&O strategy can’t think of autoplay is a video strategy—it’s a tactic that, in most cases, turns consumers off. If you want to see some of the consumer backlash, just search on Google “how to turn off autoplay” and you will see that this is most definitely a real consumer pain point. With Apple’s latest release of iOS 11 specifically blocking video autoplay, a more thoughtful and intelligent approach is required.

Video Strategy?

Autoplay on off Publisher handling UI KRAKEN Video Machine Learning Drives Higher Play Rates

Publishers are responding to consumer demand by giving the options to turn OFF autoplay video.

A video strategy involves deciding to dominate a content category vertically and be the go-to source for the highest value content in that space. Yes, video is content marketing. People watch video for information, enlightenment, entertainment, etc. Video is a very effective communication tool. Video is mobile and on demand. And being a tool, the publisher has a responsibility to harness and wield that tool surgically vs. a blunt object that pushes video views without consumer consent or value add to paid advertisers. Some publishers understand this, such as LittleThings Inc. They are disabling video autoplay completely and focusing on consumer experience. This has resulted In higher play rates (CTR), and higher CPMs that can be verified and justified to their customers. The other major benefit was consumers engaged more.

“We wanted video views to be on the consumer’s terms.  By running autoplay, you might [reach your desired] fill rate, but the user is not engaged with the brand the way they would if they raised their hands to watch the video” said Justin Festa, chief digital officer for Little Things, at JW Player’s JW Insights event in New York

Higher Intelligence

The digital publisher today is going to have to use higher intelligence with consumers. A surgical approach to utilizing data and then presenting it is now a must have. So what is the benefit of artificial intelligence in video? It is better to start with the question: What is digital video? If we break it down, digital video is just a series of images and sequences spliced together. Humans are visual and have emotional responses to images and context. The story is a major draw in creating greater emotional response over simply the affinity one may have to the people. Now a computer that translates all the above and puts it into context would have to be truly intelligent. This is not something new; Netflix proved you get higher take rates by having the right images, which results in higher consumer engagement.

In the Making

KRAKEN AMP example powers by Video Machine LearningThree years ago, a technology was introduced called KRAKEN.  It utilizes video machine learning to select images to replace the static non-intelligent thumbnail with interactive dynamic thumbnails which are the best set of images to drive the highest play rates possible. The rotation of images provides more visual information when compared to a single image. Video clipping (GIF) was next, however, it is most effective in action shots. A new way of looking at video thumbnails was required. The solution was creating a real time responsive, dynamic intelligence and scoring images based on relevance. Finding the best images is one thing, however, powering video recommendation was a natural fit for finding great images.  Learning what collective visuals work together to extend longer time on site is a major deal for all publishers. We’re living in exciting times with advances in machine learning and computer chip design having achieved amazing levels of image processing capability. We have experienced a big leap forward in the code foundation (like Deep Learning) now powering platforms to segment out objects, images, places and facial recognition. We’re in an artificial intelligence renaissance.

Show me the money

Video Recommendation Powered by KRAKEN Video Machine Learning

Video Recommendation powered by KRAKEN video machine learning. Going beyond meta data and plays to now visuals within the Video.

It’s no secret ads still drive the bulk of digital video revenue. For that very reason, each video play, and increased time on site, translates into cold hard cash. Making the site sticky and getting more repeat visits requires video intelligence. Google and Apple are very serious about protecting the mobile web. It is clear that Google AMP (accelerated mobile pages) has won out with the publishers while Facebook instant articles has fallen short and most have abandoned it due to lack of making money vs AMP. The perfect trifecta of real-time video analytics, intelligence image selection, and video recommendation are now a reality. We have the data and processing power to predict what images make you excited and what video is most relevant to watch. Video discovery is key for increasing video life time value.

Conclusion

Are you ready for the do not track and the non-autoplay world?  Like it or not, Google and Apple are disabling video autoplay and intrusive ads. The digital broadcasting publisher has a grand opportunity to leverage machine learning in video. Tapping into visually relevant actions and drawing out behavior is a competitive advantage. Machine learning linked with digital video that maximizes your video assets is a strategic advantage and increases video lifetime value. The above video recommendation example was not possible before machine learning based video processing made it a reality. What possibilities can you imagine? .

How To Increase Video Lifetime Value via Machine Learning

Videos Found For You Recommendation KRAKEN Video Machine Learning Deep Learning

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.

Reeses two great tasts put togehter Video Machine Learning Deep Learning Artificial IntelligenceThis 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.

 

For OTT, Machine Learning Image is Worth More Than a Thousand Words

So, you’ve developed an OTT app and you’ve marketed it to your viewers.  Now your focus is on keeping your viewers watching.  How can machine learning drive more engagement? Let’s face it—they may have a favorite show or two, but to keep them engaged for the long term, they need to be able to discover new shows. Discovery InfiniGraph KRAKEN Video Machine LearningBecause OTT is watched on TVs, you have a lot of real estate to engage with your viewers.  A video’s thumbnail has more of an impact on OTT than any other platform, so choose your thumbnails carefully!

Discovery is different on different platforms

On desktop, most videos start with either a search (e.g. Google) or via a social share (e.g. Facebook).  Headlines and articles provide additional info to get a viewer to cognitively commit to watching a video.  Autoplay runs rampant removing the decision to press “play” from the user.

TVs have a lot more real estate than smartphones

TVs have a lot more real estate than smartphones

On a smartphone, small screen size is an issue.  InfiniGraph’s machine learning data shows that more than three objects in a thumbnail will cause a reduction in play rates.  Again, social plays a huge role in the discovery of new content, with some publishers reporting that almost half of their mobile traffic originates from Facebook.

OTT Discovery is Unique

The discovery process on OTT is unique because the OTT experience is unique.  Most viewers already have something in mind when they turn on their OTT device.  In fact, Hulu claims that they can predict with a 70% accuracy the top three shows each of their users is tuning in to see.  But what about the other 30%?  What about the discovery of new shows?

Netflix AB Test Example

Netflix AB Test Example

Netflix has said that if a user can’t find something to watch in 30 seconds, they’ll leave the platform.  They decided to start A/B testing their thumbnails to see what impact it would have, and discovered that different audiences engage with different images.  They were able to increase view rates by 20-30% for some videos by using better images!  In the on-demand world of OTT, the right image is the difference between a satisfied viewer and a user who abandons your platform. If you’re interested in increasing engagement on your OTT app, reach out to us at InfiniGraph to learn more about our machine learning technology named KRAKEN that chooses the best images for the right audience, every single time.  Also, check out our post about increasing your video ad inventory!

More on machine learning powered image selection and driving more video views.

Making More Donuts

Being a publisher is a tough gig these days.   It’s become a complex world for even the most sophisticated companies.  And the curve balls keep coming.  Consider just a few of the challenges that face your average publisher today:

  • Ad blocking.
  • Viewability and measurement.
  • Decreasing display rates married with audience migration to mobile with even lower CPMs.
  • Maturing traffic growth on O&O sites.
  • Pressure to build an audience on social platforms including adding headcount to do so (Snapchat) without any certainty that it will be sufficiently monetizable.
  • The sad realization that native ads—last year’s savior!–are inefficient to produce, difficult to scale and are not easily renewable with advertising partners.  

The list goes on…

The Challenge

Of course, the biggest opportunity—and challenge–for publishers is video.  Nothing shows more promise for publishers from both a user engagement and business perspective than (mobile) video. It’s a simple formula.  When users watch more video on a publisher’s site, they are, by definition, more engaged.  More video engagement drives better “time spent’ numbers and, of course,  higher CPMs.    

But the barrier to entry is high, particularly for legacy print publishers. They struggle to convert readers to viewers because creating a consistently high volume of quality video content is expensive and not necessarily a part of their core DNA.  Don’t get me InfiniGraph Video Machine Learning Challenge Opportunitywrong.  They are certainly creating compelling video, but they have not yet been able to produce it at enough scale to satisfy their audiences.  At the other end of the spectrum, video-centric publishers like TV networks that live and breathe video run out of inventory on a continuous basis.   

The combined result of publishers’ challenge of keeping up with the consumer demand for quality video is a collective dearth of quality video supply in the market.  To put it in culinary terms, premium publishers would sell more donuts if they could, but they just can’t bake enough to satisfy the demand.  

So how can you make more donuts?
Trust and empower the user! 

InfiniGraph Video Machine Learning Donuts

Rise of  Artificial Intelligence

The majority of the buzz at CES this year was about Artificial Intelligence and Machine Learning.  The potential for Amazon’s Alexa to enhance the home experience was the shining example of this.  In speaking with several seasoned media executives about the AI/machine learning phenomenon, however, I heard a common refrain:  “The stuff is cool, but I’m not seeing any real applications for my business yet.”  Everyone is pining to figure out a way to unlock user preferences through machine learning in practical ways that they can scale and monetize for their businesses.  It is truly the new Holy Grail.

The Solution

That’s why we at InfiniGraph are so excited about our product KRAKEN.  KRAKEN has an immediate and profound impact on video publishing.  KRAKEN lets users curate the thumbnails publishers serve and optimizes towards user preference through machine learning in real time. The result?:  KRAKEN increases click-to-play rates by 30% on average resulting in the corresponding additional inventory and revenues.     

It is a revolutionary application of machine learning that, in execution, makes a one-InfiniGraph Video Machine Learning Brain Machineway, dictatorial publishing style an instant relic. With KRAKEN, the users literally collaborate with the publisher on what images they find most engaging.  KRAKEN actually helps you, the publisher, become more responsive to your audience. It’s a better experience and outcome for everyone.  

The Future…Now!

In a world of cool gadgets and futuristic musings, KRAKEN works today in tangible and measurable ways to improve your engagement with your audience.  Most importantly, KRAKEN accomplishes this with your current video assets. No disruptive change to your publishing flow. No need to add resources to create more video. Just a machine learning tool that maximizes your video footprint.  

In essence, you don’t need to make more donuts.  You simply get to serve more of them to your audience.  And, KRAKEN does that for you!

 

For more information about InfiniGraph, you can contact me at tom.morrissy@infinigraph.com or read my last blog post  AdTech? Think “User Tech” For a Better Video Experience

 

How Deep Learning Increases Video Viewability

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.

Measurement

Video Deep Learning Machine Learning A_B Testing KRAKEN InfiniGraphA/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.

User experience

Mobile Video Sponsor Content In FeedMaking 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?

MIT-Object-Rec_0-Visual Congnition 2

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.

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.

In our previous post How Deep Learning Powers Video SEO we describe the elements behind deep learning in video and the power of object recognition. This same power can be applied to video selection and managing visual in real time. Both image rotation and full animation (clips) provides maximum visual cognitive loading.

The KRAKEN Hypothesis

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.

How Deep Learning Video Sequence Drives Profits

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.

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.

  • PROS
    • Consumer created clipping points within video
    • Creates more visual information vs. a static image
    • Highlights action scenes
    • Great for mobile and OTT preview
  • CONS:
    • 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:

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%.

  • PROS:
    • 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)
  • CONS:
    • Similar to animated clips, publishers should limit multiple placements on a single page

Apex Image:

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%.

  • PROS:
    • Audience-chosen top image for each placement
    • Can be placed everywhere (including social media)
    • Ideal for desktop
    • Good with mobile and OTT
  • CONS:
    • 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

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.

How Deep Learning Powers Visual Search

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.

Originally posted on SEJ

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 Deep Learning  KRAKEN wonder-woman-trailer

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.

Big Numbers

KRAKEN video deep learning Images for high video engagement

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.

Visual Search

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.

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.

Big Deep Learning Opportunity

 

Chase mcMichael Deep Learning Talk to ACM Reinforced Deep Learning Vidoe

Chase McMichael gives talk on video hacking to ACM Aug 29th Photo: Sophia Viklund used with permission

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.

Big Start

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 .

Hacking Digital Video Via Deep Learning, A Video Machine Learning Solution


Chase McMichael spoke at the ACM Bay Area Chapter Event on September 29th.

Intro to the Video Deep Learning Talk

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.

kraken-gif-example-sportsphelps-kraken

Chase McMichael talk at ACM on Hacking Video Via Deep Learning

Chase McMichael talk at ACM on Hacking Video Via Deep Learning Photo: Sophia Viklund

AdTech? Think “User Tech” For a Better Video Experience

How and why did Ad Tech become a bad word?  Ad tech has become associated with, and blamed for, everything from damaging the user experience (slow load rates) to creating a series of tolls that the advertiser pays for but ultimately at the expense of margins for publishers.  Global warming has a better reputation. Even the VC’s are investing more in marketing tech than the ad tech space.

The Lumascape is denser than ever and, even with consolidation, it will take years before there is clarity.  And the newest, new threats to the ad ecosystem like visibility, bots, and ad blocking will continue to motivate scores of new “innovative” companies to help solve these issues. This is in spite of the anemic valuations ad tech companies are currently seeing from Wall Street and venture firms. The problem is that the genesis of almost all of these technologies begins with the race for the marketing dollar while the user experience remains an afterthought. A wise man once said, “Improve the user experience and the ad dollars will follow.” So few new companies are born out of this philosophy. The ones that are—Facebook, Google and Netflix (How Netflix does A/B testing) —are massively successful.

One of the initial promises for publishers to engage their readers on the web was to Panthers_Video_Machine_Learning_iPhoneKRAKEN (1)provide an “interactive” experience—a two-way conversation. The user would choose what they wanted to consume, and editors would serve up more of what they wanted resulting in a happier, more highly engaged user.   Service and respect the user and you—the publisher—will be rewarded.

This is what my company does.  We have been trying to understand why the vast majority of users don’t click on a video when, in fact, they are there to watch one!  How can publishers make the experience better?   Editors often take great care to select a thumbnail image that they believe their users will click on to start a video and then…nothing.  On average, 85% of videos on publishers’ sites do not get started.

We believe that giving the user control and choice is the answer to this dilemma.  So we developed a patented machine learning platform that responds to the wisdom of the crowds by serving up thumbnail images from publisher videos that the user—not the editor—determines are best. By respecting the user experience with our technology, users are 30% more likely to click on videos when the thumbnails are user-curated.

What does this mean for publishers?   Their users have a better experience because they are actually consuming the most compelling content on the site.  Nothing beats the sight, sound and motion of the video experience. Their users spend more time on the site and are more likely to return to the site in the future to consume video. Importantly from a monetization standpoint, InfiniGraph’s technology “KRAKEN” creates 30% more pre-roll revenue for the publisher.

We started our company with the goal of improving the user experience, and as a result, monetization has followed. This, by the way, enables publishers to create even more video for their users. There are no tricks.  No additional load times.  No videos that follow you down the page to satisfy the viewability requirements for proposals from the big holding companies. Just an incredibly sophisticated machine learning algorithm that helps consumers have a more enjoyable experience on their favorite sites. Our advice?   Forget about “ad tech” solutions.  Think about “User Tech”.   The “ad” part will come.

The live example above demonstrates KRAKEN in action on the movie trailer “Intersteller” achieving 16.8X improvement over the traditional static thumbnail image.