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AI AND THE FUTURE OF TELEVISION ADVERTISING
INTRODUCTION
CURRENT STATE OF TV ADVERTISING
WHAT YOU NEED TO KNOW ABOUT AI?
CURRENT USE OF AI IN TV ADVERTISING
AI’S IMPACT ON TV ADVERTISING
FUTURE OF AI IN TELEVISION
THE WAY AHEAD
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37
TABLE OF CONTENTS
INTRODUCTION
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The television and TV advertising industries are being radically reshaped by digitization and the emergence of video streaming technologies. A growing number of consumers are shifting to online streaming services while TV broadcasters and TV companies are switching to digital technologies to retain customers.
Artificial intelligence (AI) is still not widely implemented across the TV industry vertical. Nonetheless, AI and machine learning (ML) technologies are witnessing increased use by marketing and advertising companies while TV stations look to deliver personalized experience through AI and ML solutions.
TV advertising is still a huge business, with organizations spending over $183 billion a year, and it continues to grow. Spending stood at around $94 billion in 1999, according to data by Magna Global and is predicted to double by 2022.
Marketers and TV stations looking to optimize marketing strategies as well as advertisers seeking the highest returns on their investment need to tap into this burgeoning market. One way of doing this is wider adoption of AI and ML solutions as well as automated solutions for programmatic ad buying and delivery.
For their part, TV viewers will benefit from more personalized experiences as brands and agencies adopt AI and machine learning technologies.
TV advertising strategies will incorporate interactive online solutions as more consumers search for news and entertainment online. The success of such interactive platforms will depend largely on the capability of machine learning algorithms to glean consumer behavior from user preferences and online habits. Contrary to popular belief, older audiences are also
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searching for news and shows online and should be considered in the growth and expansion strategy of any media and entertainment company.
Another trend that should be examined is the growing use of mobile connected devices to read news, watch shows and browse the Internet. Mobile is surpassing desktop browsing in many regions, and advertisers are adjusting their ad spending and creative video strategies accordingly.
With so many platforms to choose from – digital, radio, television, newspapers, magazines, etc. – spending ad dollars has never been so prevalent. Add to these the rise of social media, corporate and personal blogs, myriads of podcasts and online forums for consumers to discuss brands and products and the opportunities become positively dizzying. Product-review sites are
a bankable industry of their own while video streaming services such as YouTube, Hulu and Netflix are disrupting a TV business model that has been in place for half a century.
Most of these innovative video services rely on AI and ML algorithms to deliver content to viewers and paid subscribers. The same is true of the distribution of ads over these platforms. To remain competitive, the traditional TV model will need to explore solutions that use AI and machine learning to deliver the same level of personalized experience, including highly targeted ads.
Although there are more channels than ever for spending ad dollars advertisers want to target specific groups, a goal to which the traditional one-ad-reach-all model is not well suited. To help reach their intended audiences, companies now have tools to track and assess their ads’ performance using Big Data solutions.
CURRENT STATE OFTV ADVERTISING
“DIGITAL CHANNELS ARE REPLACINGTV ADVERTISING IN TOTAL DOLLARS”
5Source: PwC
Annual global TV advertising is projected to reach $183 billion by 2021-2022. Terrestrial TV advertising still dominates the market, but multichannel advertising and online ads are gaining momentum, according to a report by PwC. Nonetheless, terrestrial advertising revenue will still account for about two-thirds of global TV advertising revenue, or $128 billion, by 2021.
Online TVadvertising
MultichannelTV advertising
Terrestrial TVadvertising
Source: Global entertainment and media outlook 2017-2021, PwC, Ovum
Global TV advertising revenue by source (US$bn), 2012-2021
$200
$150
$100
$50
$02013 2015 2017 2019 2021
TERRESTRIAL TV ADVERTISING STILL DOMINATES THE MARKET
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Bearing in mind that the markets in western Europe and North America already are well developed, the areas with the highest potential growth for TV advertising are the APAC (Asia Pacific) region, EMEA (Europe, Middle East and Africa) and LATAM (Latin America). According to PwC, the fourth largest TV advertising market in 2021 will be Indonesia, growing at CAGR of 10.4 percent by 2021.
Various estimates put global TV advertising at $190 to $210 billion by 2021, with North America having the slowest growth compared to the rest of the world.
Digital channels are gradually replacing TV advertising in total dollars. For the first time, digital advertising exceeded TV ad spending in 2017. A Magna Global report forecasts TV ads to increase only 2.5 percent in 2018, reaching $183 billion, as compared to digital advertising which will grow by 13 percent to almost $237 billion over the same period.
North America is still the largest
source of TV advertising revenue,
accounting for about $70 billion of
the global TV ad market in 2016. The
pace of growth in North America
will be relatively slow, however, with
the market estimated to add only $5
billion by 2021.
7
DIGITAL (FINALLY) KILLED THE TV STARWorldwide digital and TV ad spending (in billion U.S. dollars)
‘99 0
100
200
300
400
‘00 ‘01 ‘02 ‘03 ‘04
TV Digital
‘05 ‘06 ‘07 ‘08 ‘09 ‘10 ‘11 ‘12 ‘13 ‘14 ‘15 ‘16 ‘17 ‘18 ‘19 ‘20 ‘21 ‘22
4.8
347.7
183.4
94.5
Projections for 2018 to 2022Source: Magna Global
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The report from Magna estimates digital ad spending will skyrocket to almost $350 billion by 2022 while TV advertising market will remain flat at just over $180 billion a year.
Video marketing is increasingly popular. Even social networks such as LinkedIn introduce free video embedding features to satisfy the growing demand for marketing space in a video format. According to emarketer, US companies spent $13.23 billion on digital ads and $72.2 billion on TV commercials in 2017.
Research by video marketing company Magisto shows that U.S. companies spent $13.23 billion (emarketer) on digital ads and $71 billion for TV commercials in 2017.
More importantly, marketers plan to increase their budgets for business videos. According to the report, 84 percent of those surveyed say they plan to create more business videos in the coming years. This looks to be a lasting trend, with some 60 percent of U.S. businesses spending over a quarter of their marketing budgets on business videos. We interpret business videos to be any video content produced by a company to market a service or a product.
Trend Spotting: Increasingly businesses are
developing videos and other marketing materials
internally, as opposed to using outside agencies. In 2017,
98 percent of internal creative teams reported they will
grow or retain these capabilities, the annual In-House
Creative Services Industry Report reads. This is a
marked improvement over the 85 percent positive
answers in 2016 and the uncertain future of
in-house agencies in 2010. Though the
trend is growing, big-budget global and
enterprise creative campaigns are still
handled by agencies.
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SPENDING SENTIMENT FOR VIDEO AND TV CONTENT TYPESIn the next 12 months, would you expect the spend on the following to increase, decrease or maintain the same?
Mobile Video 62%
61%
48%
36%
36%
37%
49%
48%
2%
3%
3%
16%
Digital /Online Video
Advance TV
Broadcast Cable TV
Increase Maintain Decrease Net Optimisim (Increase Minus Decrease)
60%
68%
45%
20%
Source: Interactive Advertising Bureau (IAB)
That said, online video (including TV videos and commercials) is estimated to grow by a whopping 32.4 percent in 2017, according to a report by global agency Dentsu Aegis. The report highlights similar trends in programmatic ad buying for which a growth of 25.4 percent year-over-year is predicted. Programmatic ads are using AI and machine learning algorithms to deliver the best possible return on investment (ROI) for advertisers.
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4.83.8 4.3 5
2016
Global North America Western Europe Central andEastern Europe
Asia Pacific Latin America
2017 2018
3.6 4 4 3.5 3.6
7.66.6
64.7 4.3 4.6
11.9
7
8.9
GROWTH IN ADVERTISING EXPENDITURE 2016-2018(% Growth at Current Prices)
Overall the share of total digital ads is forecast to reach 37.6 percent in 2018 (up from 34.8 percent in 2017) vs. 35.9 percent for TV advertising (down from 37.1 percent in 2017).
Take Starbucks for example: as a company they’ve come to realize that their customers like being in a routine. In other words, they have a tendency to stick to their normal order as opposed to trying something new. The task? To find and reach relevant coffee drinkers and encourage them to try something different in order to increase loyalty and market share. Watson Advertising successfully achieved the goal and exceeded the brand’s engagement benchmark by 33%.
Source: Dentsu Aegis
Programmatic will increasingly
intermix with advanced
technologies such as virtual
reality and augmented reality to
deliver exceptional experiences.
Voice activation is another
disruptive technology; its impact
is yet to be assessed.
WHAT YOU NEED TOKNOW ABOUT AI
“THE CREATION OF INTELLIGENT MACHINES THAT WORK & REACT LIKE HUMANS”
Having a computing machine to assist or replace humans in performing tasks is a concept that dates back to the dawn of civilization and the rudimentary calculations of the abacus. Before the 1940s and 1950s, technology was unable to produce a computing device capable of outdoing its creators in anything besides simple computing operations.
Since then, we have witnessed the emergence of powerful microprocessors and data storage devices that enable execution of complex algorithms. Programming languages have evolved to allow creation of sophisticated software systems, which inevitably have resulted in a push for the creation of “smart” devices.
A definition of artificial intelligence by Techopedia reads:
This is a very broad definition that invites speculation and various interpretations. A simple robot able to perform one or two tasks on a production line is hardly an AI machine.The Techopedia article continues:
“Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines
that work and react like humans.”
The core problems of artificial intelligence include programming computers for certain traits such as:
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KNOWLEDGE
REASONING
PROBLEM SOLVING
PERCEPTION
LEARNING
PLANNING
ABILITY TO MANIPULATE AND MOVE OBJECTS
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SMART ASSISTANTS USE NARROW AI AND MACHINE LEARNING
This broader definition is a closer approximation of how scholars and researchers envision AI. Yet even this one is not an all-encompassing accurate definition. Why?
First, there are two basic types or categories of artificial intelligence. One is the so-called “narrow AI”; the other is “general AI.”
Narrow AI enables machines and their core algorithms to perform specific tasks and accomplish particular goals. For example, a highly sophisticated device such as a Mars rover uses narrow AI to find its path on the Martian surface, take samples and recognize promising spots and routes while exploring the planet.
68.1%
90.6% 81.9%
62.2% 87.0%
56.5%
21.7% 20.7%
How smart are “smart” assistants?Percentage of general knowledge questions (correctly) answered by smart assistants:
Answered
There of 100% correct
Source: Statista
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Most people mistakenly call a device “smart” even when it is capable only of automating certain tasks and performing jobs faster and more accurately than a human. Voice assistants such as Siri and Cortana or home automation devices like Alexa or Google Home are perfect examples of narrow AI that many consider intelligent when in fact they are not.
Mathematician Alan Turing developed a test in the 1950s with the goal of determining whether a machine can convince a jury of humans that it has human intelligence in thoughts, words and actions.
Much more recently, in 2014, a computer chatbot called Eugene Goostman impersonating a 13-year-old boy passed the Turing test by convincing 33 percent of a panel of judges that “he” was human. Many AI experts contend this was no more than a brilliant demonstration of narrow AI in which an algorithm is capable of deceiving people about being human. Actually, we have no clear definition of intelligence
and thinking, and this further complicates the successful passing of a Turing test. IBM pitted its AI supercomputer Watson against human geniuses on the game show Jeopardy!, and it swiftly defeated them. Watson is now being utilized behind the scenes at IBM to solve issues for major brands with a focus on research and development projects in the pharmaceutical industry, publishing and biotechnology.
Scientific disputes aside, we are now in an age where narrow AI is finding a place in fields as varied as manufacturing, marketing and advertising, and space exploration.
The next step will be the development of general AI that will pass the Turing test by comprehending its environment as a human would. Such a machine or device would think abstractly while planning for and solving problems at a general level. More critically, general AI would innovate and create items and concepts that have no precedents, just as human inventors have done over the centuries.
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PROCESSES INVOLVED IN CURRENT AI SYSTEMS
Deep Learning
Machine Learning (ML)
Natural LanguageProcessing (NLP)
Expert System
Vision
Speech
Planning
Robotics
Supervised
Unsupervised
Content Extraction
Classification
Machine Translation
Question Answering
Text Generation
Image Recognition
Machine Vision
Speech to Text
Text to Speech
ARTIFICIALINTELLIGENCE
(AI)
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In theory, artificial super intelligence is much smarter than human beings and possesses far greater scientific creativity, general wisdom and skills. Researchers disagree how super-intelligent AI will evolve. Some believe it will happen instantly and then expand its knowledge at the speed of light.
On the other hand, current AI systems need to be supplied with basic information and require algorithms to operate. A feasible AI-powered system also features machine learning capabilities enabling it to learn from experience or through intentional information input.
Machine learning is as compromised as it is powerful because AI systems accumulate experience based on actions taken by humans. Thus, there is a danger of creating a biased system. As any intelligent or computing system relies on data to make decisions or produce outputs, entering biased primary data into an AI algorithm can produce undesired and even harmful results.
AI and machine learning technologies are still in the early stages of development, with more advanced solutions such as neural networks and quantum computing emerging and developing alongside AI and ML. Several issues need to be resolved before we have trustworthy and intelligent systems that produce unbiased and ethical outputs.
Take, for example, the “black-box phenomena.” This relates to a fundamental problem of a computing system’s creators knowing its input and output but not how the machine determines the results that lead to the output. Acting on a decision made by an AI system in this kind of “black box” is problematic because it ignores the reasoning capabilities of the machine and therefore erodes trustworthiness. Researchers are formulating solutions to this problem, but we have more complicated issues to solve concerning AI.
CURRENT USE OF AIIN TV ADVERTISING
“ADVANCED TECHNOLOGY TO DELIVER THE RIGHT ADS TO THE RIGHT VIEWERS”
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Artificial intelligence is witnessing wider use across all creative and entertainment industries where the retention of consumers’ attention is of critical business importance.
Research by TV Technology shows that about 35 percent of broadcast TV networks, 30 percent of cable TV networks and 17 percent of corporate government educational TV networks in the U.S. use some sort of AI technology. The main fields of application include advertising, transcribing and enriching content in real time.
Most TV networks and stations strive for AI solutions that help them identify successful shows and programs and deliver customized content in real time. Networks such as NBCU have adopted AI solutions which enable them to scan TV show transcripts and deliver relevant ads to the viewer. Such technologies increase ROI for advertisers and reduce total advertising times by 10 percent. They also decrease the total number of commercials per show by as
much as 20 percent, further boosting ROI and increasing the likelihood of an ad’s being viewed by consumers.
Consumers have little patience for commercials bombarding them on every communication channel. As a result, TV stations need advanced technology to deliver the right ads to the right viewers to retain their consumer base.
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A SURVEY BY TV TECHNOLOGY SHOWS THAT TV NETWORKS ARE
INVESTING IN THE FOLLOWING AI-POWERED ACTIONS:
AUTOMATED METADATA CREATION: 47%
AUTOMATED CLIP GENERATION/DISTRIBUTION: 36%
CONTENT QUALITY ASSURANCE/MEASUREMENT: 36%
AUTOMATED CAPTIONING: 33%
OTHER (INCLUDING IMAGE RECOGNITION): 14%
TESTED AI BUT DO NOT USE: 33%
Based on the above data, one can conclude that TV stations invest primarily in AI technology that enables them to generate and distribute clips automatically.
These applications of AI mostly concern end-user experience. AI and machine learning already see wide implementations in marketing TV products and video search.
Video streaming services such as Netflix and Hulu do not limit their recommendations of films and shows to only those enjoying the highest popularity. Their machine learning algorithms take into account indicators such as multiple times viewing, rewound and fast-forwarded scenes and other elements to determine which content is best performing. The same algorithms can be applied to promotional videos and commercials. For example, an AI algorithm can determine which viewers fast-forward through a particular commercial and then deliver a variation that they won’t skip. And this benefit was explicitly graphed in a 2015 paper by former-Netflix VP Carlos Gomez Uribe and still-Netflix CPO Neil Hunt:
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Catalog Size Video Score Ranks
1
0.75
0.5
0.25
0
PERSONALIZED
PERSONALIZED
POPULAR
POPULAR
Effec
tive
Cat
alog
Siz
e
Take
-Rat
e
(Left) The black line is the effective catalog size (ECS) plotted as a function of the number of most popular videos considered in the catalog, ranging from 1 through N (the number of videos in the catalog) on the x-axis. The red line is the effective catalog size for the first k PVR-ranked videos for each member. At a PVR rank corresponding to the median rank across all plays, the ECS in red is roughly 4 times that in black. The values in the x and y axis are not shown for competitive reasons. For more details, see Appendix A. (Right) The take-rate from the first k ranks, as a function of the video popularity rank in black, and as a function of the PVR rank in red. The y-values were normalized through division by a constant so that the maximum value shown equaled 1. – From The Netflix Recommender System: Algorithms, Business Value and Innovation
The upshot: Recommendation unearths video content for users that they would never discover without the help of a little artificial intelligence. This allows video and TV services to greatly increase the reach of the content they already have, without the need for acquisition of massive amounts of additional content.
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PRIORITIES OF PURCHASERS OF BROADCASTING TECHNOLOGY
AI is also used to enhance the technology that delivers videos directly to viewers, both in compression and encoding. Usually, online video is compressed uniformly for a particular connection speed. This results in better compression for simple video content like cartoons, but bigger file-sizes for videos that are more complex, like live-action dramas. Netflix’s Dynamic Optimizer uses the fact that less-complex video content allows for higher compression to decide on the amount of compression shot-by-shot. As even the most visually complex TV-shows features quieter scenes, this allows greatly increased compression without a perceptible loss in quality. With no perceivable loss in quality, Netflix’s Dynamic Optimizer can reduce a 555kbps stream to 227kbps. Cloud-based video delivery pipelines offer advantages over traditional
infrastructure: Elasticity and scalability that simply isn’t economically feasible when you’re working with physical hardware. Even though most cloud-services offer an impressive amount of flexibility, there’s often still a small delay as resources are increased. Today, AI is being used to predict these increases in resource requirements, to reduce delays to zero.
Unsurprisingly, broadcast technology buyers point to multiplatform content delivery as a priority in their media technology purchasing strategy (see below), according to a survey by the International Association of Broadcasting and Media. By comparison, big data analytics and AI as well as programmatic advertising rank low on the list of priorities, at below 5 percent.
Source: Statista
22
However, the survey figures reflect the early-adoption stage of AI and machine learning across the media industry vertical. Many C-level executives still need to grasp that top priorities such as media asset management, cloud-based services and social TV will require implementation of machine learning and AI tools in one form or another.
It’s no mystery why TV broadcasting and TV advertising executives have placed AI on the lower end of their strategic priorities: a substantial number remain unaware of the potential uses of AI and ML. Many existing automations, for example, use narrow AI without top industry managers realizing that their revenue-optimizing solutions are powered by machine learning.
As mentioned, people tend to view AI systems as either automated spreadsheets or a sort of “brain in a box.” This is not the case. Every TV software that automatically distributes content among viewers or finds patterns in viewers’ behavior rests on AI algorithms and uses machine learning methods to make more accurate recommendations over time.
That said, AI and ML can be utilized across a variety of use cases ranging from content creation to idea generation to personalization of user experience (UX) to search optimization.
Companies such as IBM and 20th Century-Fox have created movies and video using AI and ML. In 2016, these two companies partnered to create a “cognitive movie trailer” for the horror film Morgan. The basic idea behind this AI application involves feeding a machine learning algorithm with thousands of scenes and settings from horror films so the software can produce a suspenseful trailer that converts.
The same method can be used to create an AI-generated video plot. Visuals that touch the viewer and scenes guaranteed to evoke emotional reactions also can be engineered in this manner.
Creative and marketing agencies experiment with AI differently and with varying degrees of success. Whether algorithms can possess creativity is debatable. AI-generated content is created using past experiences of people and concepts developed
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by human beings. Nonetheless, an increasing volume of video and other content created or aided by AI and ML is sure to come.
In TV advertising, AI is applied most intensely in content distribution and recommendation (i.e., content marketing). Video streaming services such as Netflix invest in applications utilizing machine learning for scheduling and workflow management. UX personalization requires complex algorithms that collect data on consumer behavior and preferences, identify patterns and trends, and then validate actionable insights concerning TV programs’ scheduling and distribution of ads.
Recommending the best content and delivering the most appropriate ads to individual viewers is quite different from old-school mass marketing. The latter delivers the same experience to the largest possible audience simultaneously while the former requires personalized experience at the level of content timing as well as brand message customization in the form of
personalized offers and content. An example of this application of AI and ML learning is a recommendation chatbot used by Sky TV. The bot utilizes sentiment analysis by IBM and recommends TV shows based on a combination of group chats and an archive of viewer data. The algorithm is able to process and understand natural language and take into account viewers’ preferred times for watching shows and other data that accumulates as the group-chat conversation progresses.
This is a passive example of cross-platform use of AI to recommend TV shows. The viewer needs to install and activate the bot before s/he gets any recommendations. Furthermore, the trend toward programmatic ads is more effective on digital channels than traditional linear TV.
Although a worthy goal, integrated screen planning has a long way to go before it becomes viable for TV advertising despite personalization gaining momentum across all channels.
AI’S IMPACT ONTV ADVERTISING
“CONSUMERS EXPECT A TAILORED EXPERIENCE AND PREFER IT IN REAL-TIME”
25Source: Statista
How AI will impact TV advertising and the delivery of tailored consumer experiences is a field in itself. Although the shift toward customized experience is impacting all major industry verticals, media and entertainment businesses experience greater pressure. Digitization has already reshuffled the media and publishing industries, redirecting advertising-money flows in ways that have caused the entire market segment to go extinct, including sectors that seemed about to flourish.
Money Follows Eyeballs - Mobile Ad Boom Continues
Estimated Change in Annual Worldwide Advertising Spending Between 2017 and 2020
ADVERTISERS REDIRECTBUDGETS TO MOBILE AND TV
Mobile Internet $72.6b
$6.9b
$3.0b
$2.1b
$1.1b
Desktop Internet
Magazines
Newspapers
Television
Outdoor
Cinema
Radio
-$2.4b
-$4.6b
-$7.0b
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Digital online mediums and interactive content platforms will grow in importance as mobile access becomes the new normal for service providers. This cross-platform and tailored mobile approach poses challenges, however.
On the one hand, advertisers and mediums have access to an unprecedented amount of data on their target audiences and viewers. On the other, this massive data needs to be analyzed to the smallest detail in order to yield valuable insights. Only powerful computing devices and feasible algorithms can do the job of analyzing billions or even trillions of records about viewers’ past behavior and preferences while assessing consumer behavior in real time.
Consumers expect a tailored experience on every channel and prefer it in real time. This experience cannot be delivered without the help of machine learning and AI algorithms that in turn require access to Big Data to produce the desired results.
We are moving toward an interactive TV experience where algorithms will take care of multiple details concerning scheduling, content format and even video scenes delivered to an individual consumer.
Technology that delivers variations of a TV commercial to different audiences or individual viewers is already is available. An AI system can tailor a branding message and video content depending on factors such as age, education, TV viewing preferences and habits. AI can even tweak properties such as the color scheme of a video commercial to get the estimated best results depending on gender, location, time of day or brand. This is otherwise known as Advanced TV Targeting. Achieving this on the level of programming code is not difficult. All that is necessary is access to large amounts of historical data as well as real-time data about who is online and watching in any given moment. The harder task is to identify changing patterns and emerging
27
trends in consumer behavior and have AI respond accordingly. A tailored general AI system is unnecessary as an advanced narrow AI algorithm is capable of delivering a tailored content experience.
Another area that will experience noticeable change is the removal of language barriers in real time. We are closer to a working Babel fish than you might imagine. Predictive algorithms are becoming more effective in processing natural language expressions while others are growing more advanced in understanding natural language (i.e., more effective in translating in real time).
Although AI capable of understanding complex content such as scientific articles is still a few years away, the average comedy or horror movie will be a no-brainer for an AI-powered translation tool. Companies are getting access to both TV content sources and TV audiences that would have been impossible only a couple of years ago. Thinking within the framework, or
limits, of the English-speaking market is quite narrow, as two-thirds of the world speaks a language other than English. Machine learning tools that translate in real time open a whole new world of video-content possibilities and opportunities for TV advertising.
The future of TV advertising depends on other factors as well. AI and machine learning can improve video experience, but how effectively and how long viewers engage with ads on different channels and mediums still need to be considered.
According to a survey by Kantar Millward Brown, premium mediums such as magazines and TV networks are still perceived as the most appropriate and trustworthy to distribute ads. Ads in magazines and on TV are “well-received” by 53 percent and 52 percent of respondents, respectively. Ads on desktops and laptops, tablets and phones are embraced by only 30 to 33 percent of consumers.
28
Source: Kantar Millward Brown
Money Follows Eyeballs - Mobile Ad Boom Continues
Estimated Change in Annual Worldwide Advertising Spending Between 2017 and 2020
ADVERTISERS REDIRECTBUDGETS TO MOBILE AND TV
53%
60%
50%
40%
30%
20%
10%
0%
Magazin
e
Outdoor
TV
Newspaper
CinemaRadio
Online disp
lay (P
C or Lapto
p)
Online Searc
h
Video (PC or L
aptop)
Online D
isplay
(Phone or T
ablet)
Video (Phone or T
ablet)
53% 52% 51% 51% 44%34% 34% 33% 30%
30%
29
Obviously, any ad-optimizing AI software should take into account the above consumer preferences in the context of a multichannel experience. Machine learning algorithms are used to reduce the number of commercials a TV station is delivering to individual viewers. The overall number of ads can still be increased, but they need to be delivered in a targeted way, thus effectively reducing the number of ads any single viewer receives. A well-designed ML algorithm will increase commercials’ ROI by delivering mostly targeted ads and reducing unwanted ads.
Brands have been collecting information about their customers for hundreds of years. The age of Big Data, however, opens the gates to gathering information on a scale limited only by privacy regulations and users’ willingness to exchange private data for free services or other perks.
Big Data will continue to be vital as it already greatly impacts every aspect of marketing. With OTT and Addressable TV, networks will have more information about the demographics of customers as well as more precise data about their viewing history and TV-watching
habits. This will facilitate the delivery of more effectively tailored content and ads.
As previously pointed out, the industry cannot track and assess all this data using spreadsheets. Modern TV technology and smart sets allow collection of unstructured data about details such as how often a household switches a TV on and off, when they start recording a show for later viewing, which ads are unwanted based on channel switching, and so on.
AI-powered systems can currently can provide immediate analysis of vast datasets while machine learning technology enables TV stations to optimize schedules and delivery of spots based on consumer sentiment and behavioral patterns. Furthermore, although it can take months to discern a trend in viewers’ sentiment, AI-enabled software can produce insights in mere minutes based on the slightest deviations from past consumer behavior. As noted in the article: How AI is Driving a New Era of TV Advertising from Advanced TV News: “With Artificial Intelligence (AI) in their corner, marketers will be able to optimize target sets in a matter of milliseconds based on both online and offline behaviors…”
FUTURE OF AIIN TELEVISION
“START THINKING IN A NON-LINEAR CONTEXT”
31
MARKETERS USING ADDRESSABLE TV IN THE UNITED STATES
The future comes down to customized content and personalized experience. Certainly, there will be automation of certain processes and increasing use of algorithms that collect information with the goal of profiling consumers. But AI and ML will play a major role in the customization of advertising content and the interactive experience provided by all varieties of video delivery. Although the number of non-addressable commercials may gradually decrease due to the adoption of AI and ML algorithms that determine where each commercial should end, the number of viewers and outlets will not.
How knowledgeable are you about addressable TV?
Source: Forrester
Not at all aware
6%
15%
35%28%
18%
Regularly includeaddressable in TV plans
Aware of, but don’t knowenough to use it
Knowledgeable but haven’tbought addressable ads yet
Experimented,but need to learn more
32
About half of marketers and members of the Association of National Advertisers (ANA) use or experiment with addressable ads on TV. This intuitive technology is gaining traction. Forrester analyst Jim Nail admits there are business-model limitations before the adoption of addressable TV ads. Not all ads can be delivered to very narrow target groups; nonetheless, demand for addressable commercials is growing.
AI has helped deliver addressable ads from large global advertisers in big-budget industries such as automotive, travel and financial services. Other industries will inevitably follow this path once the technology matures and proves its efficiency and ROI.
Only 6 percent of the advertisers surveyed are totally unaware of the addressable TV technology, so we can say with a high degree of certainty that most advertisers are at least aware of or are planning for personalized ads in their long-term marketing strategies.
The pressure is on TV networks to adapt to the new realities of tailored content as video-streaming services such as Netflix already deliver nearly 100 percent personalized landing pages for their customers. TV stations may need to start thinking in a nonlinear context if they are to be successful in delivering the same level of personalized experience to their viewers.
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PERCENT OF AMERICANS ACCESSINGTV CONTENT VIA THE INTERNETThe Rise of Video Streaming Across All Age Groups% of Americans Who Access TV Content Via the Internet by Age Group
7782
8776
86 90
53
6578
49 48
63
2015
18-24 25-34 35-49 50-59
2016 2017
While some age groups access TV content over the Internet less regularly than others do, more than half of the current living population is watching TV online. More importantly, a growing number of consumers of all ages switch to online to watch TV from time to time. Nearly 100 percent of Americans aged 18 to 34 use online to view TV content while many others admit to watching TV with a second screen in hand (mobile device, tablet or laptop).
Now, more than ever, TV networks and TV advertisers have access to accurate consumer data. Cross-platform, cross-device information is connected to timings and habits.
Collecting and analyzing consumer data is no longer a problem. These AI algorithms are already in place. The real issue concerns real-time customization of TV experiences in which there is a mix of linear and nonlinear video experience. The trend toward digital, connected and mobile is challenging the linear experience, and inevitably AI and ML tools will control most of the content delivered to viewers.
34
20100
10
20
30
Num
ber o
f sub
scrib
ers
in m
illio
ns 40
5044.5 43.5 42.2 41
39.25 37.834.34
2011 2012 2013 2014 2015 2016
FALLING NUMBER OF CABLE TV SUBSCRIBERSNumber of Cable TV Subscribers in the United States From 2010 to 2016 (in millions)
Source: Statista
The number of TV subscribers has been steadily decreasing over the past few years. Cable TV networks have lost just over 10 million subscribers in the U.S. in only five years.
The rate of decline in cable TV subscriptions is at a record high, according to The Hollywood Reporter. In the second quarter of 2017 the pace of decline in U.S. cable TV reached 2.7 percent quarter-on-quarter.
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The figures speak for themselves: the future of video and TV content lies with interactive channels and nonlinear technologies. They are another way to provide customized experiences and make commercials noticeable to increasingly unaware or distracted consumers who have grown used to ignoring thousands of ads per month.
According to a survey by MediaPost, Fox News TV network broadcasts an average of 16.52 minutes of commercials per hour. More than 40 commercials every hour is overwhelming for the average consumer, especially when one takes into account that with linear TV the viewer cannot skip ads or fast-forward. Even utilizing an AI algorithm to deliver the right ads to the right consumers results in a large number of ads that most viewers ignore. AI algorithms need to deliver the correct number of TV commercials to boost ROI for advertisers and enhance the TV experience of viewers. This is where the future of AI in TV lies.
With AI systems already understanding natural language expressions, interactions between machines and humans are entering a new age. Speaking to a device is no longer a sci-fi scenario, and TV will have to adapt to this kind of interactivity.
“Satellite operators can’t offer a competitive
lineup of VOD, and their one-way-only
programming guides are increasingly
disadvantaged in an age of two-way
interactivity. Making matters worse,
cable’s ability to bundle broadband
means that satellite ends up
being more expensive than
cable,” Craig Moffett of
Moffett Nathanson tells
The Hollywood Reporter.
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Interactivity will incorporate such early-stage technologies as conversational AI, which allows users to control and tweak a service through natural language. A few TV networks already have services that make use of devices such as Alexa and Google Home, and we can experience real conversational AI in a very short time.
Additionally, AI algorithms are capable of predicting viewers’ engagement with video content and MIT researchers have conducted successful tests predicting how many comments a certain movie will generate on social video platforms. In other words, working AI algorithms for delivery of engaging video content are already available.
Depending on specific and regional data privacy regulations, AI is becoming more effective in reading and analyzing data from different sources. An AI algorithm can easily extract the signal from the noise when connected to multiple data sources, not just a particular video-streaming channel.
THE WAY AHEAD
“WITH MORE AND MORE DEVICES BECOMING INTERCONNECTED, THE POSSIBILITIES ARE ENDLESS.”
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As the industry builds on these technologies, video streaming services, interactive TV and addressable TV ads will become more interactive. Users will get recommendations by AI-powered software while machine learning algorithms will customize the video-watching experience to the utmost.
The maturing of these technologies and the growing number of Internet of Things (IoT) devices will affect new methods of delivering brand messages to consumers. With more and more devices becoming interconnected and linked to the Internet, the possibilities are limitless. IoT devices now in use range from smart bulbs to fridges and ovens to connected mirrors. According to Statista, connected home technologies sales in the US have increased from 98 million USD to 1485 million USD between 2012 to 2017.
Marketers can deliver messages via content channels to a wide array of connected devices and customize those messages while creating
consumer profiles based on the use of one or more devices. Of course, the issue of data privacy and personal data protection are factors that need to be considered when crafting strategy.
New content is emerging continuously in a market dominated by video and in a global connected ecosystem in which new online content services emerge daily. More than half of consumers, or 55 percent, are looking for new TV shows or movies to watch, a survey by PwC reveals. Using AI and machine learning to deliver smooth video experiences to this large segment is inevitable.
Investment in ML and AI tech also will be aided by growing demand for video content and consumers’ willingness to pay more for custom video content. At the same time, 62 percent of respondents struggle to find something to watch amid increasing options.
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I AM CONSUMING MORE... THAN I WAS A YEAR AGO
I AM PAYING MORE... THAN I WAS A YEAR AGO
DESIRE TO CONSUME AND PAY FOR MORE CONTENTConsuming More Content; Willing to Pay More for it
Nearly two thirds of those surveyed by PwC consume more video content and nearly half don’t object to paying more for video content. However, most prefer nonlinear on-demand video where they choose what and how to watch, including the increasing use of mobile devices to stream video. Ninety percent of consumers under age 30 state that streaming video services play a huge role in their discovery of new video content.
People can stream video on virtually any device that has a screen. Searching for video content on many of these is a struggle, though. Search services and TV content creators will invest in technologies such as conversational AI to promote content and deliver more ads to mobile users.
This investment will boost innovative methods such as streaming analytics, which aims at analyzing data streams in real time to customize an online service. AI-powered technology will grow as increasing number of devices connect to streaming services.
Source: Dentsu Aegis
The recent Workforce of the Future
report finds that only 18 percent of
people in China, Germany, India,
the United Kingdom and the U.S. are
worried about a future dominated by AI
and automation. About 36 percent of
respondents are confident they will be
successful in the age of smart machines,
and 37 percent see a world full of
opportunities.
VIDEO
VIDEO
72%
46%
MUSIC
MUSIC
64%
33%
READING
READING
57%
39%
40
Source: Statista
Human beings are uncomfortable with fully trusting machines, and so adoption of AI in television and video streaming will be hindered until researchers develop more advanced AI algorithms. A survey by the British Science Association reveals that 32 percent of Britons are skeptical about AI and 26 percent distrust AI and ML tech.
Artificial Intelligence: Blessing or Curse?
% of Adults in Great Britain who Feel the Following Ways About Artificial Intelligence
VIEWS ON ARTIFICIALINTELLIGENCE
MISTRUSTFUL26%
POWERLESS13%
OPTIMISTIC22%
ANXIOUS19%
ACCEPTING18%
EXCITED20%
DISBE-LIEVING
6%
FRIGHTENED11%
SPEPTICAL32%
INDIFFERENT14%
INSPIRED13%
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1. Arm your brand and your team with the proper asset management strategy be-fore you need it. You may only have a small set of brand relevant assets right now, but that number will grow. It’s critical for multiple teams in different geographies to be able to access, edit, and engage with those assets for multi-channel distribution. Make sure to assess your team structure and decide if you need to put software or a full-time employee in place to help manage this process. Buttoned up team and asset management workflows equate to campaign success.
2. Align your distribution – is your social strategy a bi-product of your TV strategy or is it leading the charge. In many cases, brands will lead with the channel where they have the most direct connection with the audience. Well, AI and machine learning, as shown here will change all of that. Targeted advertising by household through TV and in some cases by individual through mobile channels is al-ready possible today. As AI becomes more
and more infused into ad tech, you will need to have a clear understanding of your audience and make sure that the message is consistent across all channels. Versioning and version control can help with this effort. By having mul-tiple versions of the same content for different channels, teams can better align a message to the intended audience member(s).
3. Analytics – This is critical. Aligning dis-tribution and audience targeting is only possi-ble with the help of good analytics. Where did your ad run? What time? How many times? Is your Digital Asset Management system track-ing this for you and your team in one central location? For ROI and targeting, these ques-tions should be easily and quickly answered. As AI becomes common place the analytics will only get more robust and more actionable. It’s key to establish this process now and onboard team members who know how to interpret and utilize that date. Analytics drive strategy and that equals currency in a digital economy.
THE WAY FORWARD FOR AI LIES IN THE PREPARATION. Because AI and machine learning are still emerging technologies in the TV space it’s important to lay the ground work for success. What does that mean? Here are five ways you can prepare teams for success as AI becomes a regular part of the technology stack:
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4. Metadata – We cannot emphasize this enough here at Adstream. Proper file tagging and metadata use is our #1 request to all who use our asset management and delivery products. Sim-ilar to search technology, the AI and machine learning are only as good as the data you feed into them. Machines cannot start from scratch when it comes to learning and there is still a human element inherent within data integrity. Adding proper identification to assets such as campaign names, general search terms, categories, years etc. will not only serve your team now, but will serve well when implementing AI technologies. This effort becomes even more essential when blockchain is introduced into the mix, but that is for another ebook. ;) Stay tuned!
5. Thoroughly understand your target audience and how they see your product and consume TV and video programming. As I am typing this, Inc. magazine online just published an article entitled “AI Assistants Want to Raise (and Market to) Your Children” as we eluded to earlier in this ebook we are not far from the reality of voice- activated AI devices remarketing to and rearing our children. In the age of smart interconnected homes the refrigerator will eventually tell Alexa to or-der more milk two days before the expiration date. Understanding your audience’s specific needs, TV viewing and listening habits will become critical to the advertising and marketing processes. As the article notes, “For those totally willing to let the robot overlords parent their children, there’s a skill called ‘Tell My Kids,’ which randomly chooses household chores to designate to children” So as a team, determine how your audience is consuming information first. If its via audio device that is an important distinction for a product that may rely on highly visual content. For accessibil-ity consider having your visual ads also transcribed into an audio format so that you are ready air across devices.
As Adstream continues to innovate on our current DAM and delivery products, we are infusing ma-chine learning and AI strategy built around advertiser needs. We invite you to let us know how, as a leading ad tech company, we can better serve our brands and agencies by helping them deliver ads that win every time. Send Comments to [email protected] ~
