Tom Morrissy on KRAKEN – Publisher Perspective on Video Machine Learning

We had the unique opportunity to talk with Tom Morrissy our Board Advisor in Time Square NY.  Below is the transcript between Tom and Chase talking about his perspective on publishers and KRAKEN our Video Machine learning technology.

Chase: Hi I’m Chase McMichael CEO and Co-founder of InfiniGraph and I’m here today with Tom Morrissy our Board Advisor. Hi Tom.

Tom: Hi Chase.

Chase: So Tom tell me a little about your experience working with us obviously you’re a midi ex media guy from SpinMedia, Timing you’re basically better. So tell us a little about your experience working with us and obviously you know more about the media space than we do. Now we really like to have your viewpoint on you know knowledge space, some of the issues. What do we do to really break in and arbitration within you know obviously cracking mobile videos big deal? So tell me what you think?

Tom: One thing that we’re finding as we talk to different publisher was just one of the reasons that I joined the company is all have very similar pain points. There’s a certain buried entry with video. There’s only so much video they can create but getting it seen and consumed by consumers is a huge challenge. So, how can we excite the consumer to be more excited and excited enough about the content that they are viewing to actually click to play and actually watch it and engage more deeply on these websites and as a factor of that we create more inventory for the publishers to monetize their video plays.

Really what it comes down to its combining a better User experience and starting with that and then taking the better User experience and being able to monetize it in a much more accelerated way. That’s the magic on what this company has done in my eyes because it took the publishers view through the User experience. Most Ad Tech starts with how we can make more money off this User with our technology does is empowers the user and that’s a vital difference relative to all the other technologies I’ve seen.

Having been on the publisher’s side and then on the Ad Tech side and then back to the publisher’s side. I was getting email after email, LinkedIN after LinkedIN request saying I’ve got a seamless integration opportunity for you revenue generating and the truth and matter is there is no seamless integration. Anybody who promises that is not telling the full truth because we all know that’s not the case.

So, the question becomes as a publisher what do you want to do and where do you want to spend your time and resources to integrate what kind of impact does it going have in your audience and then what kind of impact ultimately will have on your business. That’s what InfiniGraph is taking into account. We created the most frictionless integration opportunity that I’ve seen from seeing all the viewpoint of a publisher and figuring out a way to excite the reader and therefore grow the business.

Chase: Yeah we are just embedding it on the video player how much lower touch we can get you know we’re not touching the CMS don’t change the workflow that’s a death nail. One of the things you say a lot “its about the content especially premium content”. I like this whole thing where you talking about where we’re really going for this from a content angle. Not the ad angle and how are we going to juice the User and get the much money out of them. There’s some importance there for some of the publisher but reality is that they play an incredible strong content game they are going to lose the audience. So talk a little more about that.

Tom: The truth of the matter is, you don’t have that much time to engage the audience you can have killer content but the audience may not choose to view it for whatever reason. You got to figure out ways to engage them and get into what we call cognitive thinking and making the commitment to watching the content that is you can have the best movie you can have the best video clip if nobody watches it is like a tree falling in the forest right.

Chase: Yeah, that’s a great point because I know on mobile, we’ve been measuring you got 0.25 seconds man and if you’re not “thumbstopping” The viewer is just scrolling and scrolling you’re going to miss them. The video is a linear body of work and there is so much content there but then you’re starting out with just one image. What do you do?

Tom: Right, you have to figure out what’s going to motivate that person because think about it they have chosen to go to that space to watch a video and ninety percent of them on average are not. So why?

Chase: Yes they are bailing.

Tom: How can we make a better experience and make them more motivated to watch the video in the first place that they are there to watch in the first place. That’s the weirdest thing about this market. That’s what keeps me up at night and what keeps you up at night to try to solve for that. That’s what our company I think the promise of what we’re trying to do is really help lead the consumer to the choice they always told themselves they want to make.

Chase: Exactly.

Tom: And, if you can do that then you have a much more robust relationship with your consumer to spend more time on your site, they watch more video which is why they’re there and then you as a publisher make more money. It’s as simple as that.

Chase: Right. Thanks Tom man. It’s great to have you on board and awesome here to be in the big apple. And hey viewers, click up on the (i) up on the right here to see some more information and we will back at you soon. Thank you.

Want to see more? Request a live demo.

What Content Does KRAKEN’s Video Machine Learning Work With?

Want to learn what type of video content KRAKEN’s Video Machine Learning technology works with?  Read on!

So, you already have video on your site and you’re asking yourself, “can KRAKEN help me get more video plays??? I mean, my content is pretty special and unique!”

Let’s get right down to answering that with these 3 easy steps:

Step 1: Come to our blog. Wait… you’re already here. Good job! You’ve done Step 1 successfully, so check it off your list.

KRAKEN Video Machine Learning NY Giants

Optimized thumbnails with 198% lift

Step 2: Ask yourself—what kind of content do I have and how is it monetized? I mean, you could just have video and not make money on it. You could run pre-roll beforehand, or you could get paid with every video play by the producer/creator (this last one is called “premium video”).

Step 3: Scratch Step 2, because KRAKEN can help increase play rates with all three types! Check out the following examples!

 

Examples

Quick recap of what’s happening with each video below:

  1. Video is broken down into a bunch of “best possible” thumbnails (using crazy smart algorithms)
  2. Your audience selects their favorite thumbnails via A/B testing & machine learning (favorite=thumbnail they WANT to click on)
  3. We intelligently rotate through the best (aka favorite) thumbnails to ensure a high click-to-play rate


Click play… notice that the video begins playing? This is premium video where the content is the advertising medium. Notice the video is about a product, so there’s no need to run a pre-roll ahead of time.

What do I do next?

As you can see, KRAKEN increases play rates on just about any type of video content.  You just need to release the KRAKEN!

Want to see some awesome examples where optimized thumbnails performed up to 425% better than the original? Check out this article.

Did you know that 70% of videos see more ‘plays’ with machine learning? Here’s the rundown.

Ryan Shane VP of Sale
To find out how KRAKEN can help YOU, request more information here.

How Hyper Video Machine Learning Boosts 70 Percent of Videos to Higher Engagement


US Digital Video Ad Spending by Device

Publishers are under financial assault and video performance is a white hot topic with brands doubling down on mobile video spend. It’s all about revenue, consumer win and getting the most out of your videos assets.

Boosting video performance on existing content is not simple, however, video machine learning provides a unique and scalable way to accelerate video engagement like never before.

Here we will dive into how one of the top 20 news site uses hyper video machine learning to boost play rates on the lion’s share (70%) of videos published.

David Bowie KRAKEN Video Machine Learning 2

The David Bowie video release achieved an average 92% boost and in the first 3 days hit 104% boost (We’ll miss you David)

We kicked in the hyper drive on The Force Awakens movie trailer delivering 41% boost and describing what’s behind visual learning. News oriented video content has shown tremendous lift rates up to 425%, with many videos achieving 100%+ lift in play rates. Breaking away from old school thinking, in this post you will learn what’s driving higher video revenue beyond image recommendation (selection) technology to full on creating more engaged video watchers via intelligence.

How it’s done

KRAKEN’s video machine learning API connects directly within the publisher’s video player. The video thumbnail images are optimized using real time A/B testing and image recognition algorithms. There is tons of evidence on the immense power of visuals and first impression is everything for viewers.

The key component of KRAKEN is learning algorithms that understand placement and visual elements within the video that resonate with a particular audience. Video Machine Learning KRAKEN Pie Chart of Video LIFT Consumers are guzzling content at a hyper rate and in a world full of distractions, content “images” that can quickly capture attention will achieve a higher share of time.

In the case of video:
more plays =
more share =
more overall engagement = MORE REVENUE.

Another key attribution of KRAKEN is helping videos moving away from an unresponsive static image that retained no intelligence. KRAKEN’s image selection is not random and incorporates the sequencing of images “Image Rotation.”  This translates into showing more visual depth and further stimulates visual cognition. Hey, we patented KRAKEN and here are some solid number to prove it works!

Results

Game Of Thrones Video Machine Learning KRAKEN2

The ability to tell a visual story based on behavioral engagement assures the maximum possible engagement levels, which would otherwise be lost. That’s right—you’re losing money!

That lost video play is lost revenue and in most cases, it’s a great deal of money being left on the table. For the fans of Game of Thrones this video hit an astonishing 169% LIFT proving the right visuals drive higher revenue (lift= performance of dynamic visuals over the original thumbnail).

Video play decay over time. NOTE two days highlight gets a bump. KRAKEN achieve a 90% LIFT on the David Bowie video.

Video play decay over time.  Note that after two days, the video gets a bump. KRAKEN created a 92% avg LIFT on the David Bowie video.

All published videos experience a time to live.  Even viral videos will decay over time. Video play engagement decay on high CTR videos is displayed in the graphs above and below.

Time to live is a function of:

  • Video placement on main sections
  • Placement above or below the fold on published page
  • Mobile feed depth (how many times to scroll to see the video)
  • Mobile in view (how long is the video in view)
  • How long it’s displayed in the editorial pick or trending section
  • Social share magnitude
Video Decay over time KRAKEN Video Machine Learning

This video achieved 141% LIFT demonstrating that human faces don’t generate greater action over visual scenes that depict the video content.

Time to live variables have an impact on how long content can achieve high engagement and for how long. Obviously video performance is a function of site traffic, however, the wrong image causes massive consumer engagement loss due to the speed at which humans can process visuals and determine relevance. This speed is on the order of a blink of an eye. Are you adjusting your visual at a blink of an eye? That coupled with Forrester Research, one minute of video is worth 1.8 million words and there you have a perfect reason to make sure every consumer engagement counts.

 

Ryan Shane VP of Sale

Want to increase your video play rates and increase revenue? Contact our VP of Sales Ryan Shane for a 1:1 demo, access case studies, and see live examples on both mobile and desktop.

 

The Force Awakens Video Machine Learning – Star Wars

Star Wars: The Force Awakens Video Machine Learning Trailer achieves a massive boost (41% gain) using visual sequence story telling. Optimizing video is now a must for publishers looking to maximize their video assets and engage customers with content relevant to them. Embrace the “FORCE”
Force Awakens KRAKEN Video Machine LearningAbove is a live example of KRAKEN’s “Image Rotation” in action powered by video machine learning seen on NYDailyNews.  The image sequencing is created by KRAKEN and is integrated directly inside the video player via the KRAKEN API.

Force-Awakens-KRAKEN-Video-Machine-Learning-Mobile-Star-WarsThe Problem

The impression a video makes on a consumer is everything, especially with mobile. Typically seen is a still image with a large play button overlay in video players. This thumbnail image has been stuck in a static world for over 15 years. The old school static thumbnail on video is dead and auto play is frankly annoying.

There have been recent advancements in image processing using deep neural nets.  Finding quality and clarity is great but can be expensive at scale.

Google Thumbnailer quality selector Neural Networks

Image quality is important but our findings prove that consumers select images and prefer not the best image but the ones that cause the human mind to have intrigue.

However, the static thumbnail selection is still dependent on the person who uploads a video. This process does not scale to thousands of videos over a short period of time. That is why the majority of commercial video platforms auto select from a fixed time slice from the video and hope for the best.

Image Selection in YouTube Note KRAKEN enabled Video Machine Learning

Static thumbnail selection with customized thumbnail upload. All video platform provide this manual feature as well as a auto default is selected.

Humans cannot optimize or adjust creative on the fly to increase video performance. Many attempts to do A/B testing have proven to be helpful, however they produce limited results due to their manual nature.

The Solution

Video machine learning has come of age because it is cost effective and enables publishers to use the FORCE. Image sequencing is not a new ideal and has been used for centuries for depicting visual story telling.
cat218-lge

Video machine learning makes it possible to scale image sequencing over thousands of video placements and millions of plays. Video has gone from a static world to a dynamic and intelligent world. Star Wars: The Force Awakens Trailer benefited tremendously from video machine learning with a lift of 41%.

Force Awakens KRAKEN Video Machine Learning International

Another major bonus of video machine learning is the ability to scale and combat image fatigue (decreasing engagement over time).

Conclusion

Video Machine Learning Star Wars Force AwakensCapturing a consumer’s attention has never been harder than now. Consumers are glued to their smartphones and every millisecond counts. Publishers are reverting to the annoying auto play tactic, however, consumers are pushing back and complaining.  Fox has responded to consumer feedback by offering a feature to turn auto play off. The growth of mobile video will continue to increase massively for publishers optimizing video.  Machine learning will continue to help them benefit and maximize their valuable video assets.

Do you want to learn more about KRAKEN and hear what others are saying about video machine learning?  Check out our testimonials and intro below. Thanks for your input and thoughts on our our journey in video machine learning.

Ryan Shane VP of Sale

Ryan Shane VP of Sales

Want to increase your video play rates and increase revenue? Contact us for a 1:1 demo and access customer use cases and see live examples on both mobile/desktop implementations.

 

Video Machine Learning Success Customer Testimonial

Introducing Baglan Rhymes, Chief Digital Officer at AnchorFree with Chase McMichael, CEO of InfiniGraph, discussing the recent success of video machine learning KRAKEN on AnchorFree video ads page. Video Machine Learning Customer Testimonial – Case Studies discussed in this video are Fifty Shades of Grey, American Sniper and Birdman.

Video Transcription:

Chase: Hi I’m Chase McMichael, CEO and Co- Founder of Infinigraph and I’m here today with Baglan Rhymes, the Chief Digital Officer of AnchorFree. Hi Baglan. Baglan: Hi Chase. Chase: So tell us a little about AnchorFree. Baglan: Of course. AnchorFree is the world’s largest internet freedom platform and our mission is to provide secure and uncensored access to the world’s information for every single person on the planet. To date, we’ve been installed 300 million times. We have 30 million monthly active users and we secure approximately 5 billion page views.

Chase: That’s excellent. Obviously, we got connected with the video machine learning technology—a technology called Kraken. Baglan: Yes. Chase: And you know one of the things was that you are using a monetization page with video on the free sites. Baglan: Correct.Video Machine Learning Kraken American Sniper Graph Chase: Tell us a little more about that.

Baglan: Yes, because we have a free service and subscription-based service and the revenue stream for the free service is our content sponsors—be it movie studios, be it news organizations. And we have our own content discovery platform where we have tiles of video content and also static content where we present the users upon connect. And the videos—we don’t make any revenue off of the videos unless the users click on it. So how do we get the users to click on a video when we have maybe 5 or 10 seconds of their attention right upon connection and that’s when we connected.Video Machine Learning Kraken 50 Shades Video Lift So we partnered with you on click to play videos to increase click to play rate because unless those videos are played we don’t get paid and through your machine learning algorithms we were able to increase the click rate.

Click to view rate grew 20 to 30 times on videos overall, movies, overall movies and we ran a test on Fifty Shades of Grey and American Sniper afterwards we did and we did Birdman where we got 3,000% that ridiculous Video Machine Learning KRAKEN Baglan Customer Testimonial Birdman Play Buttonnumber [increase in click to play rate]. A fight scene in tighty whities. I actually remember I asked you to remove that. We can’t show it there and you kept it and that tighty whities that fight scene.Chase: That was the best one! Baglan: Exactly. 3,000% increase [in click to play rates] and I’m so happy we kept it.

Chase: That’s the one that boost the most revenue. So you know right now, where you seeing you going, especially around the consumer in mobile. Baglan: Yeah, video is the way users consume content now. And then whenever we see a video associated with a brand, we see a 96% increase on purchase intent, 139% increase on brand recall and even our conversations are now in the form of a video with your friends and it is just a video. So the whole communication is changing from voice to audio, visuals and emotions—which is video. Chase: Thank you so much Baglan. So please be sure to click on the (i) above to get more information. Thank you.

Would you like to see more? Request a demo

Quick Intro to Video Machine Learning

Video Machine Learning Skyrockets Mobile Engagement by 16.8X (Case Study)

Video machine learning technology called KRAKEN skyrockets mobile consumer engagement by 16.8X for the Interstellar Trailer (case study).

COMPANYVideo Machine Learning KRAKEN Social Moms Logo

Social networking for influential moms
SocialMoms began in 2008 as a popular community site for moms looking to build their reach and influence through social networking, traditional media opportunities, and brand sponsorships. It now boasts over 45,000 bloggers, reaches more than 55 million people each month, and has a network of influencers with more than 300 million followers on Twitter.

CHALLENGE

Create engaging mobile digital media campaigns for women 25-49
Video Machine Learning KRAKEN Interstellar PosterSocialMoms brings top brands to millions of women each month. They are responsible for ensuring that each campaign not only reaches the intended audience, but also that it be engaging and meaningful. However, it was challenging to get meaningful audience engagement with video campaigns on their smartphones.

SOLUTION

Responsive visuals optimized for mobile

interstellarkraken1KRAKEN replaces a video’s old, static default thumbnail with a responsive set of “Lead Visuals” taken from the video. It treats each endpoint differently, so it can optimize a movie with one set of visuals for a desktop site and another set of visuals for a mobile site—because people respond differently depending on which device they use for viewing.

RESULT

Maximum lift of 16.8X on mobile for the Interstellar CampaignVideo Machine Learning KRAKEN Interstellar Graph
After KRAKEN’s “Lead Visuals” optimization, engagement via mobile skyrocketed. SocialMoms saw over 16.8X increased engagement compared to the original default thumbnail that was chosen for the desktop site. They also reported higher completion rates when running KRAKEN.

 

Video Machine Learning KRAKEN Jim Calhoun“We’re seeing the highest engagement levels for our customers using InfiniGraph’s KRAKEN powered content.”
– Jim Calhoun COO
SocialMoms

 

 

Download InfiniGraph’s Interstellar Case Study (PDF)

Read our Birdman Case Study

Would you like to learn more about video machine learning?  Request a demo!

Video Marketing Powered By Machine Learning: Game Changer

As video consumption increases, the need for a more intelligent, learning and adaptive technology is necessary to remain competitive in video marketing today. Data driven marketing is a digital differentiation. Those that have harnessed video insights to increase video yield will lead the way. SEE Case Study on Birdman (PDF)

In our previous post 5 Ways Machine Learning Accelerates Mobile Video [VIDEO] we describe the behavioral properties of content interactions within the video stream and how to sustain consumer engagement over various video networks. The above video is a quick intro by Chase McMichael, CEO of InfiniGraph and co-inventor of KRAKEN, the world’s first video machining learning technology. In this video, he describe KRAKEN’S value proposition for both video networks and publishers.

Chase McMichael Video SetupVideo machine learning is no longer science fiction. But the technology is only part of the equation; scalability is required to process and display massive numbers of videos. For big publishers, just managing the video distribution process appears daunting; video optimization is an afterthought.  However, thanks to advanced algorithms and fast computing, the ability to learn what “Lead Visuals” are most engaging and then optimize videos can be done real-time and at scale.

Creating great video takes time, and time is money. Maximizing all your video assets requires rethinking a post-and-pray strategy.  Just as algorithms are used to target consumers,  they can now be used to optimize videos and increase engagement.

Request a demo and let us show you how KRAKEN video machine learning will increase your video yield. Access our case studies here for more depth on video machine learning.

5 Ways Machine Learning Accelerates Mobile Video

 In Mobile Video Machine Learning KRAKEN, the “Birdman” Case study demonstrates video lift engagement powered by machine learning. In “5 Ways Machine Learning Accelerates Mobile Video”, we dive into why brands are embracing video as a key marketing and storytelling tool and how machine learning can be used to drive higher engagement.

The hard reality is video is STILL LINEAR.  Even so, some are attempting to make them interactive like Jack White’s Interactive Video that allows viewers to choose their own adventure.

While the majority of brand videos are still stuck in a 15s / 30s pre-roll with a force fed content model, we’re starting to see a clear migration to long form and sponsored content that’s not just an interruption but instead it IS the story. Video machine learning is new and millions of videos can benefit from programmatic visual control. Why machine learning? Marketers don’t care what algorithms you’re using they just want to see:

Mobile Video Machine Learing Birdman post

Case study on the movie trailer “Birdman” Click to play lift achieved 3000% using machine learning technology.

  • Revenue
  • Efficiency
  • Effectiveness

Publishers are looking to achieve high KPI’s in order to increase overall spend while the media buyer is looking to lower CPA, without increasing costs. Publishers are trying to increase inventory and get the most out of their customer’s engagement. Machine learning enables both parties to achieve their goal by impacting revenue, efficiency and effectiveness simultaneously.  With this technology publishers are empowered to keep the user video engagement high over significantly longer periods of time which is proving to be an invaluable tool that will become imperative to all successful video marketing efforts.

What Marketers want to see?

  • Viewability
  • Video watch time
  • Audio on or off
  • When did consumer stop watching
  • Was the video paused
Video Viewablity Across the Web

Google research finds only 53 percent of PC video advertising is viewable.

Gone are the days of simply tracking web page hits. A more sophisticated marketer has emerged where data is king. However, video distribution and analytics are complicated. Machine learning facilitates the systems ability to learn behavior and automatically adjust marketing efforts based on active feedback loops. This virtual neural network driven by human interaction with video content creates a meaningful data set providing the foundation for mobile video intelligence.

Programmatic Explosion

Machine Learning Mobile Video Birdman Split Test KRAKEN

Graph shows real-time A/B testing of static image and KRAKEN image driven by machine learning. Machine learning makes it possible to stabilize and achieve lift.

Programmatic targeting reached an all time high of sophistication with it’s own machine learning and big data approach. Companies like RockFuel, Turn and eXelate have all perfected audience based targeting with advanced machine learning methods of aggregating massive sums of data to ensure that the right content is placed in front of the right people at the right time. The following are examples of machine learning techniques being used to enhance content engagement levels.

1. Algorithmic learning is used to determine what demographic segment responds well with specific content (e.g. videos).

2. Identification of habitual responses to visual objects by region allows for higher confidence of consumer engagement with content.

3. The type of content greatly affects the reaction of a targeted segment. Machine learning can track the visual preference of the video segments to give brands and content creators a new level of understanding as to what an audience will find most appealing.

4. Machine Learning can predict audience consumption. Plotting audience behavior across video types creates a consumption map, which can be used to predict things like video placement and cycle times.

5.  Reduce video fatigue and increase engagement by rotation of static video images (thumbnails). Static starting images face image fatigue due to a lack of visual changes, color and motion alterations. Continuous and dynamic changes in a static video image will increase audience interest and result in higher click to play rates as well as completion rates.

Visual Programmatic

Netflix has the capability to “predict” what you would watch next based on past viewing habits. Information like show/movie title and genre are compiled to help select Netflix’s recommendations. These algorithms are an example of something that pulls from the surface level information vs actual content within the video.

Netflix-Wants-Personalized-Recommendations-Instead-of-Current-Interface-443094-2 Visual content marketing is a very powerful method of attracting and retaining customers. Building a content story arch is key to perpetuating engagement and video is the most effective means to accomplish this. Publishers that leverage their audience to tune the video will achieve higher levels of revenue on their existing assets.

How do you see machine learning impacting video in the further and what video KPIs do you track that aren’t on the list? Let us know in the comments!