So, you’ve developed an OTT app and you’ve marketed it to your viewers. Now your focus is on keeping your viewers watching. How can machine learning drive more engagement? Let’s face it—they may have a favorite show or two, but to keep them engaged for the long term, they need to be able to discover new shows. Because OTT is watched on TVs, you have a lot of real estate to engage with your viewers. A video’s thumbnail has more of an impact on OTT than any other platform, so choose your thumbnails carefully!
Discovery is different on different platforms
On desktop, most videos start with either a search (e.g. Google) or via a social share (e.g. Facebook). Headlines and articles provide additional info to get a viewer to cognitively commit to watching a video. Autoplay runs rampant removing the decision to press “play” from the user.
TVs have a lot more real estate than smartphones
On a smartphone, small screen size is an issue. InfiniGraph’s machine learning data shows that more than three objects in a thumbnail will cause a reduction in play rates. Again, social plays a huge role in the discovery of new content, with some publishers reporting that almost half of their mobile traffic originates from Facebook.
OTT Discovery is Unique
The discovery process on OTT is unique because the OTT experience is unique. Most viewers already have something in mind when they turn on their OTT device. In fact, Hulu claims that they can predict with a 70% accuracy the top three shows each of their users is tuning in to see. But what about the other 30%? What about the discovery of new shows?
Netflix AB Test Example
Netflix has said that if a user can’t find something to watch in 30 seconds, they’ll leave the platform. They decided to start A/B testing their thumbnails to see what impact it would have, and discovered that different audiences engage with different images. They were able to increase view rates by 20-30% for some videos by using better images! In the on-demand world of OTT, the right image is the difference between a satisfied viewer and a user who abandons your platform. If you’re interested in increasing engagement on your OTT app, reach out to us at InfiniGraph to learn more about our machine learning technology named KRAKEN that chooses the best images for the right audience, every single time. Also, check out our post about increasing your video ad inventory!
Being a publisher is a tough gig these days. It’s become a complex world for even the most sophisticated companies. And the curve balls keep coming. Consider just a few of the challenges that face your average publisher today:
Decreasing display rates married with audience migration to mobile with even lower CPMs.
Maturing traffic growth on O&O sites.
Pressure to build an audience on social platforms including adding headcount to do so (Snapchat) without any certainty that it will be sufficiently monetizable.
The sad realization that native ads—last year’s savior!–are inefficient to produce, difficult to scale and are not easily renewable with advertising partners.
The list goes on…
Of course, the biggest opportunity—and challenge–for publishers is video. Nothing shows more promise for publishers from both a user engagement and business perspective than (mobile) video. It’s a simple formula. When users watch more video on a publisher’s site, they are, by definition, more engaged. More video engagement drives better “time spent’ numbers and, of course, higher CPMs.
But the barrier to entry is high, particularly for legacy print publishers. They struggle to convert readers to viewers because creating a consistently high volume of quality video content is expensive and not necessarily a part of their core DNA. Don’t get me wrong. They are certainly creating compelling video, but they have not yet been able to produce it at enough scale to satisfy their audiences. At the other end of the spectrum, video-centric publishers like TV networks that live and breathe video run out of inventory on a continuous basis.
The combined result of publishers’ challenge of keeping up with the consumer demand for quality video is a collective dearth of quality video supply in the market. To put it in culinary terms, premium publishers would sell more donuts if they could, but they just can’t bake enough to satisfy the demand.
So how can you make more donuts? Trust and empower the user!
Rise of Artificial Intelligence
The majority of the buzz at CES this year was about Artificial Intelligence and Machine Learning. The potential for Amazon’s Alexa to enhance the home experience was the shining example of this. In speaking with several seasoned media executives about the AI/machine learning phenomenon, however, I heard a common refrain: “The stuff is cool, but I’m not seeing any real applications for my business yet.” Everyone is pining to figure out a way to unlock user preferences through machine learning in practical ways that they can scale and monetize for their businesses. It is truly the new Holy Grail.
That’s why we at InfiniGraph are so excited about our product KRAKEN. KRAKEN has an immediate and profound impact on video publishing. KRAKEN lets users curate the thumbnails publishers serve and optimizes towards user preference through machine learning in real time. The result?: KRAKEN increases click-to-play rates by 30% on average resulting in the corresponding additional inventory and revenues.
It is a revolutionary application of machine learning that, in execution, makes a one-way, dictatorial publishing style an instant relic. With KRAKEN, the users literally collaborate with the publisher on what images they find most engaging. KRAKEN actually helps you, the publisher, become more responsive to your audience. It’s a better experience and outcome for everyone.
In a world of cool gadgets and futuristic musings, KRAKEN works today in tangible and measurable ways to improve your engagement with your audience. Most importantly, KRAKEN accomplishes this with your current video assets. No disruptive change to your publishing flow. No need to add resources to create more video. Just a machine learning tool that maximizes your video footprint.
In essence, you don’t need to make more donuts. You simply get to serve more of them to your audience. And, KRAKEN does that for you!
Video viewability is a top priority for video publishers who are under pressure to verify that their audience is actually watching advertisers’ content. In a previous post How Deep Learning Video Sequence Drives Profits, we demonstrated why image sequences draw consumer attention. Advanced technologies such as Deep Learning are increasing video Viewability through identifying and learning which images make people stick to content. This content intelligence is the foundation for advancing video machine learning and improving overall video performance. In this post, we will explore some challenges in viewability and how deep learning is boosting video watch rates.
Side by Side Default Thumbnail vs. KRAKEN Rotation powered by Deep Learning
In the two examples above, which one do you think would increase viability? The video on the right has images selected by deep learning and automatically adjusted image rotation. It delivered a whopping 120% more plays than the static image on the left, which was chosen by an editor. Higher viewability is validated by the fact that the same video with the same placement at the same time achieved a greater audience take rate with images chosen by machine learning.
This boost in video performance was powered by KRAKEN, a video machine learning technology. KRAKEN is designed to understand what visuals (contained in the video) consumers are more likely to engage with based on learning. More views equals more revenue.
A/B testing is required when looking to verify optimization. For decades, video players have been void of any intelligence. They have been a ‘dumb’ interface for displaying a video stream to consumers. The fact was that without intelligence, the video player was just bit-pipe. Very basic measurements were taken, such as Video Starts, Completes, Views as well as some advanced metrics such as how long a user watched, etc. A new thinking was required to be more responsive to the audience and take advantage of what images people would reacted on. Increasing reaction increase viewability.
So how does KRAKEN do its A/B Testing? The goal was to create the most accurate measurement foundation possible to test for visuals consumers are more likely to engage with and measure the crowds response to one image vs another. KRAKEN implemented 90/10 splitting of traffic whereby 10% of traffic shows the default thumbnail image (the control) and 90% of traffic to the KRAKEN selected images. It is very simple to see why testing video performance through A/B testing is possible. Now that HTML5 is the standard and Adobe Flash has been deprecated, the ability to run A/B testing within video players has been furthered simplified.
Making sure a video is “in view” is one thing, but the experience has a great deal to do with legitimate viewability. A bigger question is: Will a person engage and really want to watch? People have a choice to watch content. It’s not that complex. If the content is bad, why would anyone want to watch it? If the site is known for identifying or creating great content then that box can be checked off.
Understanding what visual(s) makes people tick and get engaged is a key factor to increase viewability. Consumers have affinities to visuals and those affinities are core to them taking action. Tap into the right images and you will enhance the first impression and consumer experience.
What is Visual Cognitive Loading?
How the brain recognizes objects – MIT Neuroscientists find evidence that the brain’s inferotemporal cortex can identify objects. Visual induce human response using the right visuals increase attraction and attention. Photo: MIT
A single image is very hard to convey a video story with a single image. Yes, an image is worth a 1000 words but some people need more information to get excited. Video is a linear body of work that tells a story. Humans are motivated by emotion, intrigue and actions. Senses of sight and motion create a visual story that can be a turn on or turn off. Finding the right turn on images that tells a story is golden. Identifying what will draw them into a video is priceless.
The human visual cortex is connected to your eyes via the optic nerve; it’s like a super computer. Your ability to detect faces and objects at lightning speed is also how fast someone can get turned off to your video. Digital expectations are high in the age of digital natives. For this very reason, the right visual impression is required to get a video to stick, i.e. “sticky videos”. If you’re video isn’t sticky you will loose massive numbers of viewers and be effectively ignored just like “Banner Blindness”. The more visual information shown to a person the higher the probability of inducing an emotional response. Cognitive loading thereby gives them more information about what’s in the video. If you’re going to increase viewability you have to increase cognitive loading. It’s all about whether the content is worthy of their time.
Why Deep Learning
Deep Learning layers of object recognition. Understanding whats in the images is as valuable as the meta data and title. Photo: VICOS
The ability to identify what images and why are a big deal over the previous method of “plug a pray”. Systems now can recognize what’s in the image and linking that information back in real time with consumer behavior creates a very powerful leaning environment for video. Its now possible to create a hierarchical shape vocabulary for multi-class object representation further expanding a meaningful data layer.
Quality video and actuate measurement are paramount when optimizing video. Many ask, Why are KRAKEN images better? The reality is they are because using deep learning to select the right starting images increases the probability of nailing the right images that consumers will want to engage with. Over time, the system gets smarter and optimizes faster. A real time active feedback mechanism is created continuously adjusting and sending information back into the algorithm to improve over time.
Because KRAKEN consists of consumer curated actions, proactive video image selection is made possible. We make the assertion that optimized thumbnails result in more engaged video watchers as proven by the increase in video plays. KRAKEN drives viewability and enable publishers move premium O&O rates as a result.
Viewability or go home
After the Facebook blunder or “miss calculating video plays” and other measurement stumbles major brands have taken notice …. if you want to believe this was just a “mistake.” A 3 second play in AUTO PLAY isn’t a play in a feed environment when audio is off according to Rob Norman of Group M. The big challenge is there really isn’t a clear standard, just advice on handling viewability from the IAB. However, the big media buyers like Group M are demanding more and requiring half the video plays have a click to play to meet their viewability standard. This is wake up call for video publishers to get very serious about viewability and advertiser to create better content. All agree viewability is a top KPI when judging a campaigns effectiveness. 2017 is going to be an exciting year to watch how advertisers and publishers work together to increase video viewability. See The state of video Ad viewability in 5 charts as the conversation heats up.
How and why did Ad Tech become a bad word? Ad tech has become associated with, and blamed for, everything from damaging the user experience (slow load rates) to creating a series of tolls that the advertiser pays for but ultimately at the expense of margins for publishers. Global warming has a better reputation. Even the VC’s are investing more in marketing tech than the ad tech space.
The Lumascape is denser than ever and, even with consolidation, it will take years before there is clarity. And the newest, new threats to the ad ecosystem like visibility, bots, and ad blocking will continue to motivate scores of new “innovative” companies to help solve these issues. This is in spite of the anemic valuations ad tech companies are currently seeing from Wall Street and venture firms. The problem is that the genesis of almost all of these technologies begins with the race for the marketing dollar while the user experience remains an afterthought. A wise man once said, “Improve the user experience and the ad dollars will follow.” So few new companies are born out of this philosophy. The ones that are—Facebook, Google and Netflix (How Netflix does A/B testing) —are massively successful.
One of the initial promises for publishers to engage their readers on the web was to provide an “interactive” experience—a two-way conversation. The user would choose what they wanted to consume, and editors would serve up more of what they wanted resulting in a happier, more highly engaged user. Service and respect the user and you—the publisher—will be rewarded.
This is what my company does. We have been trying to understand why the vast majority of users don’t click on a video when, in fact, they are there to watch one! How can publishers make the experience better? Editors often take great care to select a thumbnail image that they believe their users will click on to start a video and then…nothing. On average, 85% of videos on publishers’ sites do not get started.
We believe that giving the user control and choice is the answer to this dilemma. So we developed a patented machine learning platform that responds to the wisdom of the crowds by serving up thumbnail images from publisher videos that the user—not the editor—determines are best. By respecting the user experience with our technology, users are 30% more likely to click on videos when the thumbnails are user-curated.
What does this mean for publishers? Their users have a better experience because they are actually consuming the most compelling content on the site. Nothing beats the sight, sound and motion of the video experience. Their users spend more time on the site and are more likely to return to the site in the future to consume video. Importantly from a monetization standpoint, InfiniGraph’s technology “KRAKEN” creates 30% more pre-roll revenue for the publisher.
We started our company with the goal of improving the user experience, and as a result, monetization has followed. This, by the way, enables publishers to create even more video for their users. There are no tricks. No additional load times. No videos that follow you down the page to satisfy the viewability requirements for proposals from the big holding companies. Just an incredibly sophisticated machine learning algorithm that helps consumers have a more enjoyable experience on their favorite sites. Our advice? Forget about “ad tech” solutions. Think about “User Tech”. The “ad” part will come.
The live example above demonstrates KRAKEN in action on the movie trailer “Intersteller” achieving 16.8X improvement over the traditional static thumbnail image.
Chase McMichael, NAB VIDEO Intro – Top Video Platforms and Video Machine Learning made a big splash at NAB 2016.
The event was all about digital video, video production, VR, drones and every other technology you could imagine. Think of NAB as the as the CEO of digital and video broadcasting. Everywhere you looked there was drone technology, robotics and even a full area dedicated to VR. The future of video publishing is bright for sure as new technology simplifies quality capture and distribution. We took the time to connect with some of our video platform partners at NAB. Our one-on-one interviews were with Ooyala, Brightcove, and Kaltura. Each video platform provided a comprehensive walkthrough of their latest development and demos. What stood out the most was the big push in Over The Top (OTT) supporting broadcasters. OTT was a big theme for many video platforms, and all show amazing on-demand video technology. Everyone has seen Netflix and Hulu interfaces and are now becoming serious about OTT. Visuals are everything in OTT interfaces and using the power of intelligence is a key differentiation. Netflix identifies this fact in “Selecting the best artwork for videos through A/B testing”
The consumer has gone mobile in a big way, and digital video is taking on TV. Consumers want access to on-demand video wherever they are and on their terms. User experience was also a big draw, too. There is no question that lines have been drawn with rumblings of opening up the Set Top Box and unbundling the TV. Apple TV and Roku started to look like a yesteryear technology compared with the OTT interfaces and mobile native app interfaces being demoed. Brightcove released an OTT Flow and a very exciting interface for a video library and we got a first-hand view of a super slick mobile interface to digital video consumption. Kaltura also showed off what they did for Vodafone. The video platforms seem well positioned to service a TV Everywhere strategy and feed into the Apple TV and Roku devices.
Another part of the demonstrations on each platform that we experienced was 360 video support. Each player had mouse controls whereas Ooyala demonstrated split screen view supporting Google Cardboard. There is an exciting future in VR content and all are waiting to see what’s going to come out from a content perspective. Beyond linear video, immersive storytelling has a great future and we hope that technology doesn’t encumber the adoption and create friction for the experience. The speed of video player loading, streaming efficiency and low buffer rates have always been major competitive advantages when video publishers evaluate platforms.
A big topic was the relatively new Apple standard HLSjs streaming protocol. DASH by Microsoft was also discussed at various booths. All players support HTML5 with a focus on migrating customers away from the old Adobe Flash technology. Every platform demonstrated to use of HLSjs/HTML5. Kaltura shows a real-time side-by-side with an impressive HTML5 player load speed of 50% improvement. Improving load time and streaming will continue to benefit the mobile web and autoplay world. Video is everywhere and customers are demanding more of it. All video publishing platforms had very well organized video management and publishing capabilities. The big takeaways are that the platforms are focused on simplification in publishing and handling a large volume of video with greater intelligence built-in. Obviously, this is important when serving video and creating a better video viewing experience. Here are the top 4 most mentioned attributions for all the platforms.
Availability - percentage of times video playback starts successfully
Start Up Time - time between the play button click and playback start
Rebuffers - number of times and the duration of interruptions due to re-buffering
Bitrate - average bits per second of video playback. The higher the bitrate, the better the experience
All of our conversation centered around using intelligence within thumbnail selection and the process of integration. KRAKEN video machine learning has a bright future with the onslaught of OTT platforms offering more video carousel and indexes as part of the central interface for video discovery. Next up is video prediction (recommendation) and using data to make smarter decisions on what to watch next. There are some very positive results coming from companies like Iris.tv and JW Player. Look for our next post coming from Stream Media East. Catch more on our last podcast here “Thumbnails are part of a Video Marketing Strategy”
We had the unique opportunity to talk with Tom Morrissy our Board Advisor in Time Square NY. Below is the transcript between Tom and Chase talking about his perspective on publishers and KRAKEN our Video Machine learning technology.
Chase: Hi I’m Chase McMichael CEO and Co-founder of InfiniGraph and I’m here today with Tom Morrissy our Board Advisor. Hi Tom.
Tom: Hi Chase.
Chase: So Tom tell me a little about your experience working with us obviously you’re a midi ex media guy from SpinMedia, Timing you’re basically better. So tell us a little about your experience working with us and obviously you know more about the media space than we do. Now we really like to have your viewpoint on you know knowledge space, some of the issues. What do we do to really break in and arbitration within you know obviously cracking mobile videos big deal? So tell me what you think?
Tom: One thing that we’re finding as we talk to different publisher was just one of the reasons that I joined the company is all have very similar pain points. There’s a certain buried entry with video. There’s only so much video they can create but getting it seen and consumed by consumers is a huge challenge. So, how can we excite the consumer to be more excited and excited enough about the content that they are viewing to actually click to play and actually watch it and engage more deeply on these websites and as a factor of that we create more inventory for the publishers to monetize their video plays.
Really what it comes down to its combining a better User experience and starting with that and then taking the better User experience and being able to monetize it in a much more accelerated way. That’s the magic on what this company has done in my eyes because it took the publishers view through the User experience. Most Ad Tech starts with how we can make more money off this User with our technology does is empowers the user and that’s a vital difference relative to all the other technologies I’ve seen.
Having been on the publisher’s side and then on the Ad Tech side and then back to the publisher’s side. I was getting email after email, LinkedIN after LinkedIN request saying I’ve got a seamless integration opportunity for you revenue generating and the truth and matter is there is no seamless integration. Anybody who promises that is not telling the full truth because we all know that’s not the case.
So, the question becomes as a publisher what do you want to do and where do you want to spend your time and resources to integrate what kind of impact does it going have in your audience and then what kind of impact ultimately will have on your business. That’s what InfiniGraph is taking into account. We created the most frictionless integration opportunity that I’ve seen from seeing all the viewpoint of a publisher and figuring out a way to excite the reader and therefore grow the business.
Chase: Yeah we are just embedding it on the video player how much lower touch we can get you know we’re not touching the CMS don’t change the workflow that’s a death nail. One of the things you say a lot “its about the content especially premium content”. I like this whole thing where you talking about where we’re really going for this from a content angle. Not the ad angle and how are we going to juice the User and get the much money out of them. There’s some importance there for some of the publisher but reality is that they play an incredible strong content game they are going to lose the audience. So talk a little more about that.
Tom: The truth of the matter is, you don’t have that much time to engage the audience you can have killer content but the audience may not choose to view it for whatever reason. You got to figure out ways to engage them and get into what we call cognitive thinking and making the commitment to watching the content that is you can have the best movie you can have the best video clip if nobody watches it is like a tree falling in the forest right.
Chase: Yeah, that’s a great point because I know on mobile, we’ve been measuring you got 0.25 seconds man and if you’re not “thumbstopping” The viewer is just scrolling and scrolling you’re going to miss them. The video is a linear body of work and there is so much content there but then you’re starting out with just one image. What do you do?
Tom: Right, you have to figure out what’s going to motivate that person because think about it they have chosen to go to that space to watch a video and ninety percent of them on average are not. So why?
Chase: Yes they are bailing.
Tom: How can we make a better experience and make them more motivated to watch the video in the first place that they are there to watch in the first place. That’s the weirdest thing about this market. That’s what keeps me up at night and what keeps you up at night to try to solve for that. That’s what our company I think the promise of what we’re trying to do is really help lead the consumer to the choice they always told themselves they want to make.
Tom: And, if you can do that then you have a much more robust relationship with your consumer to spend more time on your site, they watch more video which is why they’re there and then you as a publisher make more money. It’s as simple as that.
Chase: Right. Thanks Tom man. It’s great to have you on board and awesome here to be in the big apple. And hey viewers, click up on the (i) up on the right here to see some more information and we will back at you soon. Thank you.
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.
Editors 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.
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?
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.”
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.
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: 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: 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: 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:
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 email@example.com—I like talking with new people.
Video machine learning technology called KRAKEN skyrockets mobile consumer engagement by 16.8X for the Interstellar Trailer (case study).
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 SocialMoms 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 Campaign 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.
“We’re seeing the highest engagement levels for our customers using InfiniGraph’s KRAKEN powered content.” – Jim Calhoun COO SocialMoms
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.
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.
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.
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.
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:
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.
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.
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?
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.
Dollars, Bits and Atoms: A Roadmap to the future of marketing.
Marketing is in a state of transition, driven by changes in technology, demographic and society. This map visualizes trends identified by industry leaders and experts showing relationships between technologies and marketing tactics, and where the industry is headed.
How brands expose audiences to content and imagery to create emotional connection, awareness and demand.
Delivering brands imagery, stories and information to customers through traditional and digital channels.
Brand as Publishers
Content Finds You:
Offering personalized experiences at the precise time, place and format that they are most engaging to consumers.
Location Aware Offers
How brands interact with customers and create differentiated experiences.
Giving Customers a greater voice and provide new ways of users- created content and ideas to come to the fore.
Real time Brands
Stuff Gets Real:
Technologies that allow us to physically interact with the digital world and vice-versa.
Physical / Digital Blend
Consumerization of Just In Time
PERSONALIZATION & MEASUREMENT
Ways to create customer experience and 1-to-1 relationships with customers, then measure the results
By The Numbers:
Information from mobile devices, sensors, social media, public and third parties, combined with enterprise data.
Expertise On Demand
Location Based Analytics
The Big Picture:
Data, media, automation and personalization combined to provide an immersive, quantified customer experience.
Agents and Proxies
360 Degree CRM
Live & Interactive Example of Content Context
This example is designed for travel and travel related posts. Native Advertising with the ability to detect past engagement on content types and updated based on consumer behavior. Click through to see the Content HUB in action.