FORBES: InfiniGraph Invents Video Thumbnail Optimization

Bruce Rogers ForbesBruce Rogers, FORBES STAFF
I’m Forbes’ Chief Insights Officer & write about thought leadership.
Originally posted on Forbes

A Series of Forbes Insights Profiles of Thought Leaders Changing the Business Landscape: Chase McMichael, Co-Founder and CEO, InfiniGraph

Optimizing web content to drive higher conversion rates, for a long time, meant only focusing on boosting the number of click-throughs, or figuring out what kinds of static content got shared most often on social media sites.

But what about videos? This key component of many sites went largely overlooked, because there simply wasn’t a good way to determine what actually made viewers want to click on and watch a given video.

Chase_McMichael_Video_Machine_Learning_Headshot_2_2016

Chase McMichael, Co-Founder and CEO, Infinigraph

In an effort to remedy this problem, says entrepreneur Chase McMichael, brand managers may have, at most, tried to simply improve the video’s image quality. Or, in a move like a Hail Mary pass, they might have splashed up even more content, in the hopes that something, anything, would score higher click-to-play rates. Yet even after all that, McMichael says, brands often found that some 90% of viewers still did not watch the videos posted on their sites.

As it turns out, the “thumbnail” image (static visual frame from the video footage) has
everything to do with online video performance. And while several ad tech companies were already out there, using so-called A/B testing to determine how to optimize the user experience, no one had focused on optimizing video thumbnail images. Given video’s sequencing speed with thousands of images flashed up for milliseconds at a time, it meant that measuring the popularity of thumbnails was simply too complex.

Sensing a challenge, McMichael, a mathematician and physicist with an ever-so-slight east Texas drawl, set out to tackle this issue. He’d already started InfiniGraph, an ad tech firm aimed at tracking and measuring people’s engagement on brand content. But as his company grew, he found that customers began asking more and more about how they might best optimize web videos in order to boost viewership.Panthers_Video_Machine_Learning_iPhoneKRAKEN (1)

Viewership, of course, is key: Higher video viewership translates into more shares; more shares means increased engagement. And that all translates into more revenue for the website. Premium publishers are limited in their ability to create more inventory because the price of entry is so high. These new in house studios are producing quality content, but getting scale is a huge challenge.

When he started looking into it, McMichael says, he often found that the thumbnails posted to attract viewers usually fell flat and the process for choosing thumbnails hasn’t changed in 15 years. And the realization that the images gained little to no traction among viewers came as something of a surprise: Most of the time, the publishers and brand managers themselves had selected specific images for posting with no thought at all into optimizing the image.

According to McMichael, the company’s technology (called “Kraken”) solves for two critical areas for publishers: it creates inventory and the corresponding revenue while also increasing engagement and time spent on site.

Timing, it turns out, was everything for McMichael and InfiniGraph. Image- and object-recognition software had been improving to the point where those milliseconds-at-a-time thumbnails could be slowed down and evaluated more cheaply than in the past. Using that technology along with special algorithms, McMichael created Kraken, a program that breaks down videos into “best possible” thumbnails. Using an API, Kraken monitors which part of the video, or which thumbnail, viewers click on the most. Using machine learning, Kraken then rotates through and posts the best thumbnails to increase the chances that new users will also click on the published thumbnail in order to watch an entire video.

This process is essentially crowd-sourced, says McMichael—the images that users click on the most are those that Kraken pushes back to the sites for more clicks. “What’s fascinating is we’ve had news content, hard news, shocking, all the way up to entertainment, music, sports and it’s pretty much universal,” he says, “that no one [person] picks the right answer”—only the program will provide the best image or images that draw in the most clicks. On its first few experimental runs, InfiniGraph engineers discovered something huge: By repeatedly testing and re-posting certain images, InfiniGraph saw rates of click-to-play increase by, in some cases, 200%. Says McMichael: “It was like found money.”

InfiniGraph is a young and small company, even for a start-up: The Silicon Valley firm has eight employees in addition to a network of technicians and specialty consultants he scales on and as-needed basis, and has boot-strapped itself to where it is today. McMichael says he’s built a “very revenue-efficient company” because “everything is running in two data centers and images distributed across a global CDN.” His goal is to be cash-flow positive by this summer. Right now InfiniGraph works exclusively with publishers but the market is ripe for growth, especially in mobile devices, McMichael says.

Recently, Tom Morrissy, a publishing leader with extensive experience in both publishing (Entertainment Weekly, SpinMedia Group) and video ad tech (Synaptic Digital, Selectable Media) joined InfiniGraph as a Board Advisor.

“So many companies claim to bring a ‘revenue generating solutions that is seamlessly integrated.” This product creates inventory for premium publishers and is the lightest tech integration I’ve seen. I was completely impressed with Chase’s vision because he truly thought through the technology from the mindset of a publisher. Improve the consumer experience and the ad dollars always follow” says Tom Morrissy

The son of a military officer father and registered nurse mother, McMichael grew up in the small town of New Boston, Texas, located just outside of the Red River Army Depot. A self-described “Brainiac kid,” McMichael says he was always busying himself with science experiments, with a special interest in superconductors, or materials that conduct electricity with zero resistance. Though he’d been accepted to North Texas, McMichael still took a tour at the University of Houston, mainly because the work of one physics professor who discovered high temperature superconductivity had grabbed his attention. “So I went to Paul Chu’s office and said, ‘hey, I want to work for you.’” It was the craziest thing, but growing up I was always told, ‘If you don’t ask for it, you won’t know.’”

That spawned the beginning of seven-year partnership with Chu during which time the University built a ground-breaking science center. McMichael spent seven years in DARPA funded applied science, but decided to leave for the business world. A friend of McMichael’s worked at Sun Microsystems and encouraged him to leverage his programming knowledge. His first job out of college was creating the ad banner management system for Hearst. “So I got sucked into the whole internet wave and left the hard-core science field,” he says. He also worked at Chase Manhattan Bank in the 90s, building out its online banking business.

As for the future for InfiniGraph?

McMichael says his mission is “to improve the consumer experience on every video across the globe, and it’s an ambitious plan. But we know that there are billions of people holding a phone right now looking at an image. And their thumb is about to click ‘play,’ and we want to help that experience.”

Bruce H. Rogers is the co-author of the recently published book Profitable Brilliance: How Professional Service Firms Become Thought Leaders - Originally posted on Forbes

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 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.

CHALLENGE

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.

SOLUTION

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.

RESULT

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
AnchorFree

 

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

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. 

CHALLENGE

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. 

SOLUTION

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.

RESULT

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
AnchorFree 

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

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.

 

CHALLENGE

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.

 

SOLUTION

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.

RESULT

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
AnchorFree

 

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

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!