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.


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.


Responsive visuals optimized for mobile

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


Maximum lift of 16.8X on mobile for the Interstellar 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



Download InfiniGraph’s Interstellar Case Study (PDF)

Read our Birdman Case Study

Video Machine Learning: A Content Marketing Armageddon?

Video marketing is being revolutionized by machine learning, fast data and artificial intelligence. The dawn of data-driven video is upon us. Video takes the lion’s share of marketing spend and fast-growing mobile video is surpassing all other marketing methods. Understanding behavior and content consumption is key in optimizing mobile video. Brands have an insatiable appetite for consumer engagement, as evident in brands’ adoption of video, reported by YouTube, Facebook and InMobi.

Video industry leaders who embrace these advanced technologies will establish a formidable competitive advantage.

The market is moving away from the video interruption ad model and premium video is taking center stage. Battle for middle earth is being waged between video networks, publishers, and content creators. Those who have intelligent data will win the video marketing thunder-dome.

With few exceptions, old school person-to-person media buying is fading fast. Machine learning is being used to ensure the optimal deal is always reached in programmatic video placement. We are seeing a torrent of data coming in from ad platforms, beacons, wearables, IoT, and so forth. This data tsunami is compounding daily, creating what the industry calls “fast data”. Video and human action on video is a big challenge due to consumption volume. The competitive weapons are now speed and agility when building an intelligent video arsenal.

In July, I attended the launch of Miip by InMobi, an intelligent video and ad unit experience. These units are like Facebook’s left and right slider units, but Miip has also implemented discovery. Check out the video to see more of what I’m talking about:

All programmatic networks use fast data composed of human personas, actions, and connected devices. This data explosion is forming big data, and it’s happening at a massive scale. It’s not surprising that programmatic targeting leveraged machine learning and big data management. There is a lot of hype around Real-Time Bidding (RTB) and programmatic targeting.

With all this technology, the one thing that remains true is content still must resonate with the consumer – and machine learning is creating a huge opportunity to match the right content with the right consumer.

We see big players like Tubmoguel seeing massive growth, as described in Mobile Programmatic Buying Is Taking Off. Programmatic spend in mobile now surpasses desktop by 56.2%, eMarketer points out.

Video Creation & Growth
KRAKEN Video Machine Learning - Chase McMichael Video Setup

Video creation tools like Magisto, PowToon, and iMovie are simplifying the process. The decreasing hardware costs have also lowered the barrier of entry. The iPhone 6, Hero4, and video drone technology are great leaps forward in video capture.

Low-cost broadcast-quality video is here with iMovie HD and Camtasia Studio 8. Full commercials are edited on iPhones only. There is an explosion of professional content now. What was once cost-prohibitive is now the industry norm. With all this video technology unleashed, hundreds of YouTube stars were born. The cable cord-cutting acceleration is upon the cable networks now. As more high-quality digital video hits the scene this will fuel grater choice on the consumer’s terms.

Peter Fasano, from Ogilvy, and Allison Stern of Tubular, did a great job presenting The Rise of Multi-Platform Video. Here they reveal the differing advantages of Facebook and YouTube.

In both cases, content engineering is a must-have (see 5 Hypnotic Mobile Native Video Content Marketing Methods).

Facebook Engagement Tubular Labs Ogilvy

Secrets To A Successful Video Strategy from Social@Ogilvy

Data Driven Video Storytelling

This year, Cannes Lions was all about VIDEO storytelling with a big focus on data. Visual and mobile content experiences are personal. I am seeing a massive shift to data-driven journalism. Companies like Google News Lab, Facebook’s Publishing Garage, and Truffle Pig (a content creation agency) are all working with Snapchat, Daily Mail, and WPP – all powering scaled content creation.

“The power of digital allows content, platform, and companies to test and learn in real time before scaling.” -Max Kalehoff

Hear more on this movement from David Leonhard from New York Times’ The Upshot, Mona Chalabi from Facebook Garage, and Ezra Klein and Melissa Bell from Vox:

Video is Not Spandex

Consumers are not one-size-fits-all when it comes to how they consume content. The creation of content is a natural progress for using artificial intelligence (AI) technology. Machine learning has the ability to connect many data elements and test many hypotheses in real-time. Using humans to adjust the algorithms is “supervised learning”. “Unsupervised learning,” a self learning and constantly improving system, is the holy grail in AI.

The exciting part is when machine can create by themselves. We are witnessing this at Google: see Inceptionism: Going Deeper into Neural Networks.

the carnival of the animals and goodness knows what else

Getting the right message to the right person is critical in obtaining a positive response. The delivery process and decision will impact the responsiveness. Each platform requires a different strategy. Companies like Tubemogul, Tremor Video, and Hulu all have programmatic video management.

Now broadcasters are starting to embrace data, which enables advertisers to target a more specific audience. Soon we’ll have AI video distribution based on the actual content inside the video.

Video Machine Learning Kraken Birdman Graph

This graph shows real-time A/B testing from video launch and KRAKEN machine learning optimization in action. Machine learning makes it possible to stabilize and achieve lift.



The following are three examples of machine learning techniques being used to enhance video engagement levels:

  1. Fast data requires advanced algorithmic learning to process: Identify what demographic responds well to which content type (e.g. video). Segment your audience by the type of content consumed. Look at what was shared when most comments were generated. Combine these data points and see what drove most action. These steps will help you learn what logical groupings achieve highest targeting response.
  2. Identify what visual objects induce habitual responses: What visual objects allow for higher consumer engagement? Visual content can then be grouped and that knowledge can be used over and over in later videos.
  3. Machine learning predicts video consumption habits: What people watch tells you a great deal about their preferences. Measuring audience behavior across video types creates a consumption map. Consumption maps predict things like video placement and cycle times.

The type of visual content affects the reaction of a targeted segment. Machine learning can track the visual preference of the video segments. Each brand and content creator structure can achieve a new level of understanding. What does the audience find most appealing? Is there a large-scale pattern you can identify?

Visual Programmatic
KRAKEN Video Machine Learning - Netflix-Wants-Personalized-Recommendations-Instead-of-Current-Interface-443094-2-1

The next frontier of mobile video is intelligence – the ability to predict, as well as adapt, content based on all the data available. We are seeing companies like IRIS.TV indexing video libraries to recommend content. Netflix and Amazon have the capability to “predict” using supervised learning human curators. All this metadata in video is providing a treasure trove of information: now we’re connecting with the social graph changing the game.

Finding content that viewers will enjoy is the ultimate goal and extended deep video engagement is a big opportunity. Achieving this level of nirvana has its challenges: see Why Websites Still Can’t Predict Exactly What You Want. We are just scratching the learning algorithms surface of artificial intelligence.

As technology advances, more intelligent visual content marketing will emerge. Machine learning will soon dominate the data-driven marketing landscape. We are moving toward story creation with technologies like Dramatis. People like Brian O’Neill at Western New England University are leading the way (see With Expanding Roles, Computers Need To Add ‘Storyteller’ To Resume). Video networks, content creators, and publishers have a grand opportunity, but all are going to need to collaborate and incorporate a more sophisticated offering if they plan on competing over Facebook and YouTube. The big question is, will they maintain control of their content destiny?

In the age of intelligent data, audience insight is always a winning strategy. Those who tune their video content with intelligence will achieve higher levels of revenue.

How do you see machine learning impacting video in the future? Share your thoughts in the comments!

Image Credits
Featured Image: Andrey_Popov/
In-post Photo: Image by Ryan Shane. Used with permission.
All screenshots by Chase McMichael. Taken August 2015.

Video Machine Learning Boosts Consumer Engagement by 309% (Case Study)

Video machine learning technology called KRAKEN boosts consumer engagement by 309% for the Fifty Shades of Grey Trailer (case study).

COMPANYVideo Machine Learning Kraken AnchorFree Logo

AnchorFree: The most trusted VPN service in the world!
With a monthly active user base of over 25 million and 350 million installs to date, AnchorFree’s Hotspot Shield VPN is the largest free VPN service in the world. It has an unparalleled ability to protect users’ IP from spammers, snoopers, and hackers, provide Wi-Fi security, and detect and protect against malware.


Increase revenue from limited inventory
Video Machine Learning Kraken Fifty Shades of Grey Movie PosterIn order to keep Hotspot Shield free, AnchorFree relies on advertising. With finite inventory and users, increasing consumer engagement is very important, as this results in a higher yield for each video. They are constantly looking to generate more interest and engagement with each longform video placement to increase advertising revenue.


Responsive visuals at programmatic scale

KRAKEN uses machine learning technology to replace static thumbnails with a programmatically optimized set of “Lead Visuals.” This directly results in higher user engagement. AnchorFree is therefore able to increase yield from a finite user base and inventory.


Consumer engagement increased 309% with the Fifty Shades of Grey CampaignVideo Machine Learning Kraken Fifty Shades of Grey Graph
Over the course of the campaign, KRAKEN was able to increase consumer engagement by 309% when compared to the trailer using a standard default thumbnail. AnchorFree was able to generate additional revenue leveraging existing customers and without having to add inventory.


Video Machine Learning Kraken Baglan“Without KRAKEN running, we would be leaving money on the table. I can’t imagine why anyone would run video without first optimizing it with KRAKEN.”
– Baglan Nurhan Rhymes
Chief Digital Officer, SVP Global Revenue



Download InfiniGraph’s Fifty Shades of Grey Case Study (PDF)

Read our Birdman Case Study

Video Machine Learning Drives 40% Additional Revenue (Case Study)

Video machine learning technology called KRAKEN drives 40% additional revenue for the Birdman Trailer (case study).

COMPANYVideo Machine Learning Kraken AnchorFree Logo

Most trusted VPN
service in the world!
With a monthly active user base of over 25 million and 350 million installs to date, AnchorFree’s Hotspot Shield VPN is the largest free VPN service in the world. It has an unparalleled ability to protect users’ IP from spammers, snoopers, and hackers, provide Wi-Fi security, and detect and protect against malware. 


Increase revenue from video longform placements
Video Machine Learning Kraken Birdman Poster
In order to keep Hotspot Shield free, AnchorFree is constantly looking for ways to increase their customers’ engagement levels and average revenue per user (ARPU). Regardless of premium placement on the AnchorFree launch page, the video ads were producing less than desired click-to-start and completion rates. Before KRAKEN, AnchorFree tested with various forms of static default thumbnails attached to the video promos. 


Responsive visuals at programmatic scale
KRAKEN uses machine learning technology to optimize “Lead Visuals” in a programmatic structure, enabling the highest video engagement possible. KRAKEN became the preferred platform to maximize video revenue yield from their current advertiser base.


40% revenue gain for the Birdman campaignVideo Machine Learning Kraken Birdman Graph
KRAKEN boosted click to play rates for the Birman trailer video campaign by a staggering 3,000%. This increase in click to play rates directly resulted in a 40% gain in revenue. After realizing such profound revenue gains, AnchorFree does not run high value video campaigns without KRAKEN.


Video Machine Learning Kraken Baglan“InfiniGraph’s Kraken technology is the first real breakthrough we have seen in many years. I can see Kraken being implemented by digital broadcast networks, publishers, ad networks and video player platforms in the very near future. Early adopters will turbo charge their video ad revenues on desktop and mobile.”

– Baglan Nurhan Rhymes
Chief Digital Officer, SVP Global Revenue

Download InfiniGraph’s Birdman Case Study (PDF)

Read our American Sniper Case Study

Video Machine Learning Sustains a 378% Lift Over 48 Days (Case Study)

Video machine learning technology called KRAKEN sustains a 378% video play rate lift for the American Sniper Trailer over 48 Days  (case study).

COMPANYVideo Machine Learning Kraken AnchorFree Logo

AnchorFree: The most trusted VPN service in the world!
With a monthly active user base of over 25 million and 350 million installs to date, AnchorFree’s Hotspot Shield VPN is the largest free VPN service in the world. It has an unparalleled ability to protect users’ IP from spammers, snoopers, and hackers, provide Wi-Fi security, and detect and protect against malware.



Maintain engagement over long periods of time with the same media

Video Machine Learning Kraken American Sniper PosterAnchorFree shows movie trailers as part of their advertising campaigns. A single campaign with various video content might last two months. Before KRAKEN, AnchorFree would see engagement peak when videos were launched, but steadily decrease over time. Engagement levels decreased as users saw the same thumbnail over and over, slowly becoming blind to it. This phenomenon is called video fatigue.



Responsive visuals at programmatic scale

KRAKEN replaces a video’s old, static default thumbnail with a responsive set of “Lead Visuals” taken from the video. Since it is powered by machine learning, KRAKEN continually optimizes the set of “Lead Visuals” to ensure a consistently high engagement rate, even over long periods of time.


Average lift of 378% for the forty-eight day American Sniper CampaignVideo Machine Learning Kraken American Sniper Graph
Over forty-eight days, KRAKEN was able to increase engagement by an average of 378% for a single American Sniper video trailer. With a consistently high yield, Anchor- Free was able to run the campaign longer to maximize revenue versus using a standard, static thumbnail.


Video Machine Learning Kraken Baglan“We run trailers for weeks, even months at a time. Only after optimizing with KRAKEN have we been able to see consistent and high levels of engagement from the beginning of a campaign to the end.”
– Baglan Nurhan Rhymes
Chief Digital Officer, SVP Global Revenue


Download InfiniGraph’s American Sniper Case Study (PDF)

Read our Birdman Case Study

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!

Mobile Video Play Rate Boosted by 200X to 3000X – KRAKEN Release – Case Study


Video is the largest and fastest growing segment in online marketing. Unfortunately the first impression to a consumer of those videos is more than likely a static image and there isn’t a simple way to programmatically adjust based on audience intelligence. This problem is leaving billions on the table with un-played videos and lost engagement due to the lack of compelling starting visuals.


Baglan Nurhan Rhymes , SVP of Revenue – AnchorFree

“In a highly competitive Ad Tech space, where videos drive the lion share of revenues, InfiniGraph’s technology, Kraken, is the first real breakthrough we have seen in many years.

I can see Kraken being implemented by digital broadcast networks, publishers, ad networks and video player platforms in the very near future. Early adopters will turbo charge their video ad revenues on desktop and mobile. ”

Initial Results:

Our beta customers: Disney, Paramount, Microsoft and AnchorFree have experienced between 10% to 3000% lifts in click-through and play rates on their video content using InfiniGraph’s patented Kraken technology..Mobile Video Play Rate Boost AnchorFree

Example –  AnchorFree before and after on “50 Shades of Grey”:

  • 200%+ boost in play rate
  • 10X increase in private viewing sales

InfiniGraph’s machine learning technology, achieves scale by producing the highest possible response to mobile video based on audience behavior.

Learning algorithms matter:

Mobile Video Birdman AnchorFreeImproving mobile video play rates is more of a science as seen in the second example on AnchorFree running “Birdman”.  Is your video “Thumb Stopping” when a consumer scrolls through their feed?  In the case of Birdman, the results were amazing.

Overall video play rates grew by:

  • 3000%+ boost in play rate
  • 2.6% peek click through rate


Mobile Video Performance Birdman KRAKEN Machine LearningSolution:

The Kraken machine learning system is continuously analyzing the video and user interactions at every content distribution endpoint, over many sequences. This decision making is done almost in real-time.


Mobile Video Machine LearningInfiniGraph’s mission is to help video content owners, publishers, and agencies deliver the most relevant video experience. This helps boost video starts, video completion rates, and increases page visit depth by eliminating creative burn and waste. This translates into higher revenues for the existing content through higher video starts, higher VCRs, and higher brand engagement.

Our clients, who have implemented proprietary “video click-through / play rate enhancement technology called “Kraken”,  have experienced upwards of 3000% lift in content plays.
As an Advertising,  Content Mobile Video Machine Learning Whats KRAKENProducer,  or  Brand Management Professional you know that video creates the greatest impact to your online marketing.  The challenge and measurement of success continues to be execution of “play-rates” and “video completion rates” .

Want to see more or run your own test? Contact Us


Mobile Native Video Interstellar 14X Hyper Play Rate Jump

Jim Calhoun, COO SocialMoms

Jim Calhoun, COO SocialMoms

We’re seeing the highest engagement levels for our customers using InfiniGraph’s native content

Paramount’s Interstellar uses mobile native video and distributed native advertising units to hyper jump their play rates by 14X. Mobile video is exploding and brands are leveraging advanced methods of distribution outside of the old school static native ads we are seeing today. We are now in the age of intelligence, machine learning, and responsive design.  Why can’t this intelligence be applied to mobile native video? Learn how to hyper drive your own mobile native video – signup, request a demo, and see how intelligence is applied.
Sign UP for Mobile Native Video

Above is an example of such intelligence integrated within the mobile native advertising unit for on and off domain content amplification. The mobile video leverages “deep linking” to launch video on a smartphone creating a seamless consumer experience. The content inside the native unit is dynamically updated based on consumer actions which increases overall engagement rates and exposure.

The Takeaway:

  • 50% play rate boost
  • Maximize content marketing spend
  • Mobile first content rendering
  • Intelligent content distribution
Rich Media CTR example

Doubleclick data showing industry averages of 0.06 to 0.18 vs .84 for mobile native more on benchmark on rich media

Most brands are sitting on tons of great content. This content is usually stuck in silos and doesn’t have a simple way of tracking with the consumer across other media sources, such as a websites/blogs (owned), and other paid mediums. Ad re-targeting is extremely effective; however a major issue is visual ad fatigue. The consumer sees the same media over and over, eventually reducing effectiveness. The Industry benchmarks on rich media content averages around .06% to .10% CTR compared to native ads which perform much higher. With new intelligent data techniques harnessing machine learning, these technologies are pushing engagement rates up to 50% improvement. 


Interstellar Mobile Native Video

Content Marketing has truly taken on a new life and the quality of content within videos has leaped to extraordinary levels.  As more publishers and brands are seeking to control the content on their site, white label native ad platforms will continue to take hold.

Native unit for Interstellar launching the mobile native video

Native unit for Interstellar launching the mobile native video

The example on the left demonstrates what an embedded native unit looks like when deployed over a publisher network or brand website. The code behind these units is designed with a mobile first strategy, assuring optimal rendering on mobile. InfiniGraph sources content from the existing Interstellar movie published content, scores the content and transforms it into intelligent mobile native units. What’s unique about this approach is the consumer actions on content are tracked and the brands content inventory is managed per individual unit to maximize content marketing spend. eye-120803Keeping content fresh is a major factor in reducing image fatigue, repetitiveness, and inactive engagement. The human brain can process images in 13 milliseconds, how fast your visuals resonate with your consumer is key to amplifying brand content on domain as well as enhancing off domain engagement. The same mobile native unit can be deployed over other 3rd party native networks, ad networks, programmatic exchanges, and on the brands domain simultaneously.

Apply some intelligence in your mobile native video – signup, request a demo and see how intelligence is applied.
Sign UP for Mobile Native Video