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

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.

 

Think You’ve Picked the Best Video Thumbnail? Think Again — 52 Videos that Prove Video Machine Learning can Double Play Rates

I’ve never met anyone who intentionally picked a bad video thumbnail—but they’re everywhere.

To be clear, bad ≠ ugly. Bad thumbnails are sometimes beautiful. Bad means that people don’t WANT to click on them. After all, the point of a thumbnail is to get people to click “play” or “stop scrolling” long enough for the video to start to playing.

KRAKEN Video Machine Learning Rikers Original ThumbnailEditors and content creators with years of experience spend a lot of time picking “best” thumbnails.  And publishers posting hundreds of videos daily rely on content management systems (CMS) that suggest or auto-pick thumbnails.

Guess what? They’re usually wrong.

Almost always, there is a better thumbnail for any given video or set of thumbnails.

How? Why?

Because “best” is defined by your audience, not you. You bring your experience and baggage with you every time you pick a thumbnail—and you are different from your audience.  Why not take the guess work out of the equation and use data, not opinion, to choose the right thumbnails every time?

Real Example

KRAKEN Video Machine Learning Teacher Original Thumbnail Let’s say you’re an editor in LA and pick a thumbnail for a video about the latest breaking news topic. You might choose this image to the right:

Now what if your viewer is from Texas? What if that image doesn’t speak to them at all? That doesn’t mean they’re not interested in the topic or wouldn’t want to see the video content, it means that the thumbnail doesn’t make them WANT to click “play.”

KRAKEN Video Machine Learning Teacher
If you had asked your viewers, they would have told you that they preferred seeing the images on the left—all taken from the very same video.

 

 

Our recent post “The Force Awakens” shows another great example and the science behind data-chosen thumbnails.

Your audience isn’t one-size-fits-all. Your thumbnails shouldn’t be either.

Here are 52 videos from last month that prove intelligent selection of images can greatly improve video play rates.  Each has an optimized set of thumbnails that performed 101%–425% BETTER than the original thumbnail.

KRAKEN Video Machine Learning Rikers

Quickly though—what is an optimized thumbnail?

Optimized thumbnails are dynamic and rely on machine learning and audience feedback. Our product called KRAKEN does this all in real-time

So, what the heck does that mean in english???

It means that our computers examine a video and pick a bunch of ‘best possible’ thumbnails, then A/B tests them to determine what ones people actually click on. It will serve different images to different people depending on a variety of factors, including device and placement. Hey, it’s a patented process!

Said another way, we crowdsource what thumbnails people actually engage with, then show them to future visitors.

Results – Before & After

Think sports fans will click on any video related to their team? Think again. Optimized thumbnails performed 198% better than the original:
KRAKEN Video Machine Learning NY Giants Original Thumbnail KRAKEN Video Machine Learning NY Giants
                    Original Thumbnail                                 KRAKEN Optimized Visuals

Optimized thumbnails work for ‘hard’ news videos, too. This video about Enrique Marquez’s ties to the San Bernardino gunmen had a 205% lift:
KRAKEN Video Machine Learning Enrique Original Thumbnail KRAKEN Video Machine Learning Enrique
                    Original Thumbnail                                 KRAKEN Optimized Visuals

Kardashians—love them or hate them, right? It turns out that optimized thumbnails can produce a 128% lift in video play rates:
KRAKEN Video Machine Learning Kardashian Original Thumbnail KRAKEN Video Machine Learning Kardashian
                    Original Thumbnail                                 KRAKEN Optimized Visuals

From earlier in the article: the Rikers Island Guard video saw a 157% lift, while the video of a Teacher under fire for her lesson on Islam saw a 127% lift.

Our top performing video of December saw a 425% lift.  Here’s an overview of all 52:
KRAKEN Video Machine Learning Graph Videos with 100 lift Dec 2015

 

What could you do with double the video plays (or 3X or 4X)?

Would it double your video revenue? Satisfy your audience because more of them are seeing your awesome video content (after all, that’s why they’re on your site in the first place)?

The good news is your “best” thumbnails already exist and are buried in your existing videos. You just need to release the KRAKEN and get them to the surface.

Leave a comment below and tell us your thoughts. If you are interested in links to all 52 top performing videos, send me an email at ryan.shane@infinigraph.com—I like talking with new people.

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 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: 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.

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

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

Image Credits
Featured Image: Andrey_Popov/Shutterstock.com
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.

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