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 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.

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.

Deep Learning Methods Within Video An End Game Application

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.

Deep Learning Methods Within Video – An End Game Application

Big moves

Adobe Stats from Report on Mobile VideoWe’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 KRAKEN video deep learning Images for high video engagementand 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.

Deep Learning

KRAKEN Video Deep Learning AB Test VIDEO mobile video liftBroadcasters 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.

Motivation

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.

Conclusion

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.

Top Video Platforms and Video Machine Learning at NAB 2016

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. Drone Plane Hybrid NAB 2016sm 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.

Tom Morrissy sporting the laste in VR ware at NAB 2Another 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.

  1. Availability - percentage of times video playback starts successfully
  2. Start Up Time - time between the play button click and playback start
  3. Rebuffers - number of times and the duration of interruptions due to re-buffering
  4. 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”