It’s hard to predict what will happen next in the streaming wars. Just when the world was beginning to wonder if the market and consumers were approaching “peak subscription,” the pandemic hit and the streaming wars were given a new lease of life. 

Netflix — arguably the biggest name in streaming — announced that it acquired 15.7 million new subscribers in the first three months of 2020, almost double the number it gained during the same period in 2019. More recently, its competitor Disney Plus reached 100 million subscribers just 16 months after its launch: a number that Netflix took 10 years to reach, albeit while pioneering the burgeoning streaming market.

It was an unanticipated but welcome boost. But it will be difficult to sustain this level of growth.

A Pain in the CPAs 

Streaming services are caught in a marketing arms race. They are under immense pressure to grow their subscriber base and keep shareholders happy — but the wider they cast their net searching for prospective new users, the higher the CPA (cost per acquisition) typically becomes. This is clearly an unsustainable model for long-term growth. Like in any arms race, the consequences for those involved are often MAD, or mutually assured destruction. 

Instead of attritional marketing warfare, streaming brands need to identify clever ways to decrease churn and increase engagement. And the best way to maximize efficiency is to craft experiences that leverage data-driven personalization.  

Here are three things to consider when using personalization to make scaling subscribers more palatable and effective.  

Leverage optimized landing pages and sign-up flows: Personalization within the streaming platforms is an effective way to reduce churn and it should define the experience from the very first touchpoint. 

Landing pages should be at the forefront of this strategy. They should be customized and personalized to reflect a user’s unique journey. If someone arrives on a landing page after clicking on an advert for a specific show, then this show should be the thematic template for the page and define the copy and hero images used — with other personalized content suggestions orbiting dynamically around it.  

The landing page should also have a close relationship with the sign-up process. Poorly signposted sign-up processes quickly spark friction and frustrate users. 

The UX of the signup process is crucial. Brands should remember that:

• Visual progress indicators are more intuitive than text-based progress indicators; 

• It must be clear what subscription tier users are signing up for;  

• Keeping a consistent message across the platform for users with no active subscription is the best way to re-focus these users to sign up

Beyond this, streaming services should recognize that for customers, signing up doesn’t end with the payment form. New trial sign-ups should be optimize through personalized experiences, customer data mining and predictive modeling in order to execute more efficient media across platforms.

Personalizing recommendations: From Spotify, to YouTube or Amazon, consumers expect to be met with intelligent recommendations based on their preferences. Research from Accenture found that 91% of consumers are more likely to engage with brands that provide offers personalized to them. 

Streaming services are broadly wise to the powers of personalization. In 2019, Netflix experimented with an AI tool which would edit trailers based on user data, so different users would see different trailers depending on their likes and preferences. 

While personalized ads and landing pages may get users into your ecosystem, providing reliable recommendations is a strong way to build trust, and consequently longevity, between the streaming brand and the user. The services that can crack when and what content to present to each user will do better at retention.

Shifting the focus to LTV and predictive modeling of customer data: Streaming services also need to have a holistic view of their customers and their habits. Understandably each service sits on a treasure trove of data: what shows are being watched, did they complete or drop off, and what did they watch next?

The main point here, though, is not how much fancy data services have, but how that data is activated to make smarter decisions within their customer acquisition strategies. This should naturally lead brands to be more inquisitive and open to a lifetime value (LTV) based marketing model.

At this moment in time, every advertiser should be considering moving to an LTV model. It will help take a brand’s marketing strategy from short-term metrics to a long-term advanced approach to acquisition.

There are five steps to take here:

1. Unifying customer data 
2. Data preparation and modeling
3. Understanding the drivers of LTV
4. Understanding customer clusters and audiences
5. Activation in audience and bidding

With LTV, brands can better target high-value audiences more likely to subscribe to their service. Campaigns, ads and keywords are measured by their LTV performance using a data-driven conversion attribution model, and the brand can optimize activity accordingly. The LTV metric can also be used for automated bidding, to get the right messages to the right customers at the right time.

This is a hyper-competitive moment in the streaming wars. To continue to grow, streaming brands will need to embrace marketing tactics that combat churn and engage new users — all without getting caught up in a marketing arms race. This means investing in understanding what customers are doing on their services — and crafting experiences that reflect these needs from the very first moment. 

Kris Tait is managing director of the U.S. business at global digital marketing agency Croud. 

Personalized ads can forge stronger connections with consumers and ultimately boost purchase intent, a new study from media buyer Magna and IPG Media Lab.

The study, Pursuit of Relevance: All About Ad Personalization, found that most forms of ad personalization had value, but moving some metrics took more work. 

Personalizing ads based on major life events struck an emotional chord with people, leaving them 16% more interested in hearing more from the brand and 12% more likely to purchase the brand, compared to the same video ad without a personalized message.

Testing was conducted among brands’ demographic target and with high-value audiences, based on people-based ID created by Kinesso. Six data sources were tested for personalization: demos, past purchases, location, life events, search terms and persona-based messages (only used on the high-value audiences).

“This is an important study that reinforces our belief that personalized messaging, when coupled with precision targeting is a key to driving the results that brands demand today of their advertising,” said Brendan Gaul, global chief content officer, head of UM Studios, an IPG media agency. “It also uncovers the importance of maintaining best practices and creative excellence in the development of the work. A good ad that is personalized works very hard. A bad ad that is personalized is still a bad ad.”

The study was conducted with 6,600 participants and looked at three advertiser categories, auto, retail and finance. 

“Relevance is a critical part of brand marketing strategy because people today are inundated with content on a daily basis and it can be overwhelming,” said Kara Manatt, senior VP, group director, intelligence solutions at Magna Global. “Personalized messaging can not only help create more relevant ads, but also help amplify brand stories, forging stronger, more solid personal connections.”

The study also found that personalization doesn’t guarantee branding. Performance of personalized ads is amplified when branding is incorporated into the messaging. With branding in the message, personalized ads are 8% more memorable than standards ads. Creative best practices still apply, so the process of planning for personalization shouldn’t distract from the need to have strong branding, according to the study.

Advertisers should couple personalization with precision targeting, the report said. Using people-based IDs for targeting HVAs is effective, but these audiences are often critical of mass market messaging. In response to personalized ads, however, HVAs are much more open, with 5% saying its something they’re open to right now.

Brands should also consider crafting persona-based messaging for even greater impact. The report found a 20% stronger response to ads personalized based on brand preference compared to 8% using standard personalization.

Zone TV said it is introducing a software suite under the Zone·TV Studio brand that enables content owners and video distributors to create self-branded, linear TV channels that can be personalized and customized by and for each viewer in the household.

Zone·tv Studio is an artificial intelligence-powered service for content discovery, channel curation and video delivery, the Santa Monica, Calif.-based firm said. The software enables a “Spotify-like” experience based on what the individual viewer wants to see. 

Related: Zone TV Uses A.I. to Program 14 Linear-Style OTT Channels

“Traditional video distribution is in a state of disruption as hundreds of direct-to-consumer streaming services are emerging, making it difficult to maintain customer loyalty,” Zone·TV CEO Jeff Weber said in the release. “Our technology means that any video distributor or content owner can build new, precisely targeted, compelling channels which enable a more relevant content consumption experience for their customers. Zone·tv studio enables the simplicity of traditional linear channel programming combined with the intelligence power of A.I. to bring new user experiences to TV services.”

Zone TV said using the software "learns what viewers love to watch and presents similar content, automatically and in real time. This unique capability empowers viewers to create their own channels with the click of the TV remote control and 'zone·ify' their own channel." A Foodies channel one viewer watches could be more about grilling than a neighbor's desserts-focused channel, based on their viewing habits.

"Thanks to technological advances and greater collaboration between industries, it is now possible to, for instance, safely and anonymously pair geolocation data with viewership and online shopping behavior."—Tyler Winton, TiVo 

In today’s fragmented video content space, it’s increasingly clear that marketers, content providers and creators are having trouble identifying and honing in on their target audiences — to no fault of their own. Consumers are now jumping across platforms, screens and content at a frenetic pace.

Because of that, tracking viewing behavior and calculating attribution is more complex than ever, and newer and smarter ways of utilizing data are emerging as an intelligent response for defining consumer tastes and behavior. At the same time, we’re seeing a growing consumer demand for increased personalization when engaging with content and brands.

In fact, according to a recent Epsilon survey, 80% of consumers are more likely to conduct business with a company or brand that provides a personalized consumer experience.

How can marketers and pay TV content providers work together to deliver the promise of greater personalization for the ever harder-to-find consumer? The answer is, unsurprisingly, through better data integration — not only across content platforms and devices, but also across different industries. We need to integrate pay TV data with external, third-party data.

The advertising industry is just now starting to rediscover the potential that TV data holds as the foundational piece for cross-channel attribution. Linear and pay TV data is the glue that binds together data from OTT and connected devices, social media and online shopping to slowly help the industry move toward a fully viable model of cross-channel attribution. However, we are still missing the omnichannel part of the puzzle. What does a consumer do or purchase when not online or consuming content, and how does that impact our assessment of that individual? How does it impact our assessment of the content, even? And the advertising that might have caused a consumer reaction?

These might seem like impossible queries to solve, but, in a world where the line between the physical and the digital is quickly fading, we can’t have true personalization without providing an answer to them.

Related: Personalized TV Ads — Less Is More

To fully realize the potential of personalization we also need to move from household-level to true individual identification. Clearly our culture is one in which we place a high value on our individuality, both in our outward self-expression as well as in the way we respond to personalized messages from leading brands. In TiVo’s Q4 2017 Online Video and Pay TV Trends Report, the ability to create personal profiles was more popular than any other element of a Netflix subscription, even scoring higher than price. Technologies like biometrics and far field microphones are being rapidly adopted and in conjunction with machine learning ease the barriers to developing truly individualized profiles.

Thanks to technological advances and greater collaboration between industries, it is now possible to, for instance, safely and anonymously pair geolocation data with viewership and online shopping behavior. This allows us to know where a fan of XYZ show dines on Saturday night. We’re also able to correlate that information, for example, with what shows other patrons at the same establishment tend to watch and ultimately what kind of messages they will be most receptive towards on any given device at any given time. That alone can take recommendation carousels and content discovery engines to the next level, but the possibilities are simply endless. And, as long as we ensure the data is anonymized, we ensure consumers’ privacy rights are respected.

Personalization’s Great Potential

The tremendous potential for granularity in this approach can and will benefit consumers, marketers and content providers alike. Consumers will be offered video content recommendations and ads that align much better with who they are and what they care about, allowing them to enjoy a more personal experience on an ongoing basis. In turn, marketers will be able to better identify and target their potential customers while optimizing campaign investment. As for content providers, the increased granularity and understanding of the potential audience for their inventory will only lead to more marketable inventory and increased ad revenue.

However, this is only the beginning of a new era of data portability and integration. We are seeing more brands willing to look beyond the walled gardens that traditionally protected their proprietary data in exchange for game-winning insights. We can only hope that this trend continues to grow so that we, as an industry, can keep working toward the goal of the ultimate customer experience: a highly personalized model of attribution that is able to satisfy the needs and concerns of all parties involved.

Tyler Winton is vice president of business development, Consumer Market HQ, for TiVo.

Over-the-top video is now on par with cable and satellite, if the general consensus coming out of this year’s CES is to be believed.

Once its own separate bucket for video content, OTT has now become a hallmark of any successful video option. Netflix and Hulu may be the largest names in the game, with the first-to-market advantage. However, offerings from Apple, Facebook, YouTube, Amazon and countless others provide numerous options to catch up.

Beyond those services, there are also the standalone streaming options from publishers like Sports Illustrated, CBS, Billboard and more — services that can’t branch outside of their ecosystem from a content perspective, but have ample opportunity to own their audiences in today’s environment. While the early OTT trends were geared toward housing video content where it could potentially find the widest audience, that dynamic has now changed somewhat.

Targeted, personalized experiences are the preferred way consumers interact with brands. And one way to do that well with video is the methodology Netflix has long put into play. Its algorithms prioritize its original content, clearly. But based on your preferences and watch habits, there’s also plenty more to explore around the service, brought to you by artificial intelligence (AI).

Pathways to Monetization
Netflix isn’t monetized from an ad perspective, however. And that’s something many publishers need in order to survive this current video- focused landscape.

One potential avenue is audience ownership, and as a result, an augmented user experience with AI.

Related: Three Predictions for AI in 2018

What’s been missing from the OTT streaming equation for plenty of publishers, though, is how to take ownership of audiences and make business decisions from that data.

Shopping content to third parties can allow for a larger base audience, but it also dilutes the value you can glean from those viewers. The content isn’t targeted, and there are no adjustments to be made in real time. Users get what they get, and can be directed away from your content by the third party’s own video server as well.

Related: AI, Machine Learning to Change the Customer Experience, Comcast’s Watson Says

With owned-and-operated video hosted directly on publisher sites, audiences opt into viewing only your content. That in and of itself makes for a more tailored scope (especially for niche sites). And it also allows for AI to provide customized recommendations for what’s next on each individual user’s personal playlist. It also enables you to collect valuable first party data that can be further utilized for programming strategy.

Then, there are subscription-based video services, which also lend themselves to high quality audience intelligence.

Related: Standing Out in the Crowded SVOD Sector

Netflix and Amazon have put pressure on the consumer’s willingness to pay. With more players in the field competing for share of wallet, content, programming and marketing strategies require a higher level of precision. You need to maximize engagement from trial in order to convert.

Making Marketing Personal
AI-based personalized recommendations and data-driven decision making don’t just inform content acquisition strategies, but also marketing tactics for targeting new, returning and loyal viewers to maximize subscriber yield. Your service cannot just show Moneyball, it must also be playing it.

Effectively utilizing this sort of data is where OTT can thrive, optimizing every aspect of the video ecosystem down to when or why eyeballs tune out of certain programming. The more granular the information available, the more content can be adjusted accordingly to make video more relevant to viewing preferences.

This is where OTT can thrive. With streaming video seen as just as valid an option as cable and satellite pay TV services, one would imagine rises in advertising and subscription rates could come next. With a greater ability for AI to tailor content and brand experiences to individual audiences and, as a byproduct, provide prescriptive insight. There’s incredible upside for OTT that traditional video content can’t necessarily match.

Rohan Castelino is vice president, marketing, for Iris.TV. Image downloaded from Flckr and used under Creative Commons CC0 1.0 Universal Public Domain Dedication.

The advent of connected TV (television programming delivered via over-the-top devices) has sent shockwaves through the television industry and the lives of consumers.

Disrupters like Netflix and Hulu have moved from streaming videos in browser-only environments to making content available on virtually any device and from strictly licensing content to developing original shows, ushering in what some have called “the golden age of television.”

It’s changed how consumers watch TV, transforming the medium from appointment-based watching and recording to one where viewers watch what they want, when they want, wherever they want from any of hundreds of different OTT apps and services.

These changes aren’t just good news for the streaming services or television fans; connected TV (CTV) also presents advertisers with fresh avenues for reaching new customers, serving ads to ideal audiences, and generating revenue.

But how, exactly, do advertisers understand and measure the impact of their CTV investments? Unfortunately, not very well. Because CTV advertising falls somewhere between digital and traditional TV, the industry can’t agree on how to measure it. The environment doesn’t support cookies or traditional viewability measurements, and CTV isn’t tied to linear TV’s conventional demographic-based audiences.

As the industry confronts a series of crossroads around CTV, let’s explore some of the challenges, solutions and metrics that are emerging.

Sticking With Tradition: GRP
CTV’s similarities with traditional TV mean that many advertisers have turned to the traditional television metric of Gross Rating Points (GRPs) to measure ads on connected devices. By paying attention to reach and frequency, these advertisers are simply applying a metric they already know and use.

Beyond convenience, GRP enables marketers and agencies to evaluate their CTV investments in line with their traditional TV buys. That means advertisers can include CTV in the same high-level media plan they use for all their TV activities.

Despite these upsides, GRP will prove too broad and flawed a metric for CTV. Whether an advertiser reaches one individual a million times or a million individuals just once, the GRP will be the same.

Advertisers that rely on GRP alone will be unable to answer these and other questions. More significantly, this focus on traditional TV metrics is beginning to shape — and obstruct — the path forward for CTV. By relying solely on GRP, advertisers may fail to consider richer ways to measure CTV, particularly those that give their digital counterparts deep insights into their advertising efforts.

Today, digital advertisers have access to the technology they need to target specific groups of consumers with personalized messages all along the customer journey. CTV advertisers, too, can draw on advanced data analytics to gain a deeper understanding of individual consumer behavior and improved attribution. By combining rich data with a robust cross-device graph (think of a massive table with tens of billions of entries representing links or relationships between cookies, mobile device IDs, households, smart TVs or connected TV devices), brands can link CTV data with data from other devices belonging to the same household to create complex and comprehensive consumer profiles. They can then use this information to serve customized ads targeted to just the consumers or households that match their desired audience and measure the results.

Advertisers with e-commerce-enabled websites, on the other hand, can advertise to consumers on CTV, then measure subsequent online visits across devices from households that were exposed to the CTV ad. They can even leverage real-time machine learning to optimize the campaign to find CTV audiences similar to those that are visiting their sites in real-time.

However, adopting metrics from digital advertising won’t solve all the challenges surrounding CTV — it may even bring some of its own. For instance, not every publisher has fully embraced the tech platforms that enable this advanced targeting and measuring in CTV environments. Concerned about data leakage, some publishers are reluctant to give buyers all of the signals they need to target or measure in these environments.

The good news, though, is that many publishers are already allowing advertisers to target and measure on their own platforms. And now these publishers are benefiting from the incremental increase in demand from the “stickiness” that comes from advertisers who continue to increase their investments in OTT because they can reach just the audiences they want and measure the impact of those investments against their bottom line.

Moving the Industry Forward
What can advertisers do to make sure they’re making the most of their CTV investments right now?

Using GRP is a useful step to making it easier for some advertisers to direct more TV budgets into CTV, but the industry shouldn’t stop there. The advantage of CTV over linear TV is that it can be measured against other behaviors across devices.

And we finally have the technology and capabilities to do it. Still, it may take some time and additional work across the publisher ecosystem before advanced analytics is fully supported on CTV. Advertisers shouldn’t stay complacent; they must continue to push and support the evolution of GRP measurements on CTV and insist on the ability to target and measure in rich detail across environments.

At the same time, as publishers continue to support GRP measurement, they must lean forward and embrace the potential for rich targeting, analytics and attribution on their inventory. The “golden age of television” has certainly arrived for consumers. Now it’s up to all of us in the industry to ensure advertisers realize the full potential of this golden age as well.

Kevin Fennelly is director of TV products and solutions at dataxu, a Boston-based software provider for marketing professionals.

We are living in an on-the-go, on-demand world. Many television critics claim we are experiencing a golden age of programming, which in part has grown from a better understanding of emerging viewing habits and needs. Content is still where the true value lies, and there are a growing number of streaming services — both large and small — that stand out from the crowd by offering a carefully customized selection of content aimed at specific audiences.

Gone are the days of the living room TV being the only place to watch television. Today, consumers desire personalized and curated content that can be viewed anywhere, anytime, on any platform — even potentially through new channels like autonomous vehicles. Viewers are no longer tied to a specific type of content or program; if the content does not match viewers' expectations, they now have the means to find something better.

Unique Offerings Are Crucial
In order for broadcasters and media companies to stay ahead during this rapid transformation, offering niche content or delivering something unique that enhances the viewer experience is key. Over-the-top networks have dramatically lowered the barriers to entry, so that while big players like Netflix may be leading the way, smaller streaming networks are also now in with a shot at success based on the wide variety of platforms and data that are available.

Television sets and movie theaters have previously dominated the way viewers consume content, but people can now watch videos on mobile devices almost anywhere they like, including the car. With the rise of autonomous vehicles, video content for cars can even soon develop as its own unique business.

A recent study from Intel and Strategy Analytics predicts that a “passenger economy” will emerge and will be worth $7 trillion by 2050 as autonomous vehicles become mainstream. This type of economy will capitalize on the time that will be freed up as drivers become passengers, allowing them more time to be consuming content.

The report warns that it’s important to start thinking about digital strategy now as the early opportunities offered by the autonomous cars of the future are still significant. It also predicts that media producers will develop custom content formats to match short and long travel times. One example of this could be content tailored to a journey based on what location the passenger is in — like a local community events recap curated for a Friday afternoon commuter ahead of the weekend, or some information on interesting pit stops for a long road trip.

With so many new platforms and offerings, there’s a great opportunity for other industries to produce their own tailored content to meet the needs of a more niche audience. The ability to offer curated content is particularly useful in the world of sports, enabling teams and leagues to complement match coverage with additional content, such as athlete interviews, postgame analysis and behind-the-scenes reports. In-depth analysis and behind-the-scenes player footage can be used to enhance the fan experience, which is especially useful for fans based overseas who wouldn’t otherwise have such direct access to their favorite teams.

Traditional television networks have previously used ratings to inform which shows to renew and which shows to remove. Now, in order to offer personalized content, artificial intelligence (AI) software is increasingly used for more in-depth analysis. For example, Netflix is using this type of technology to track viewer consumption trends and make personalized suggestions based on their historical preferences. This kind of technology was previously used mostly by large companies, but it is becoming increasingly accessible to smaller companies, giving them the same quality data to work with as the larger corporations.

Ripple Effects
As viewing habits evolve, there are even opportunities for other types of businesses to use digital content to reach their loyal customers, including news distributors, retailers and technology companies. Through the use of OTT platforms, these companies can develop new digital video content quickly and at scale.

With greater personalization through the use of data, the ability to reach a more niche audience and the availability of content through new platforms, we are entering an exciting new age for television and a complete revolution in the way content is commissioned in today’s industry.

Brian Morris is vice president and general manager of Global Media & Entertainment Services at Tata Communications. Image by ThinkStock Photos.

Truly conversational devices are becoming more and more prevalent in our daily lives. Through smart home devices such as Amazon Echo or Google Home, interactive “assistants” allow consumers to activate shortcuts to save time and help automate routine tasks — all using the sound of their own voice.

This sea change in home automation is starting to take place in the living room as well. No longer the domain of just adjusting the thermostat or turning on music, people are using their voice to search for — and discover — content on TV. The industry is seeing not only an increase in the number of households that use voice to discover content, but also the complexity of that discovery. The prevalence of home assistants is teaching people not only that they can speak to a device, but how to speak naturally with that device.

Act Naturally
A transformation from robotic dictation to natural conversation is underway and will play out over the coming months and years. What started out as simple searches for titles (“Find me Game of Thrones”) or channels ( “Tune to HBO”) has now broadened to much subtler, and more complex, searches such as “What’s on TV?” or “Find me some good comedy movies,” followed by “only the ones rated five stars.” A voice-supported interface that isn’t smart enough to handle these complex searches, or maintain context for follow-up questions, is like the first iteration of Siri: a fun toy, but for many of us, it’s not providing an overly valuable experience to improve our day-to-day lives.

The implications of this shift are significant. A voice interface is a like a wormhole through a user interface. Instead of navigating through endless menu paths that only very few viewers succeed at mastering, a cluttered screen can be replaced by a clean and intuitive user interface that allows voice interaction. The challenge then becomes not only to provide a set of results for a given search, but also to interpret the intent of the viewer, and act upon that intent in a meaningful way so that only the most salient results are seen, personalized to the viewer's interests.

Adding the extra dimension of personalization to voice functionality not only offers viewers the most cutting-edge user experience, but also a more accurate, high-quality set of results. The data is available to marketers, thus it’s important to deliver the right promotion at the right time to the right viewer.

Talk, Then Target
For example, when a viewer expresses a very well-defined intent, like “Find me my game,” the system knows to tune to the New York Yankees based on previous viewing habits, but also provides the opportunity to deliver targeted marketing and upsell paid content such as MLB Extra Innings. For networks and studios, the opportunity to deliver a target promotion is priceless. If a viewer asks, “Find me a comedy movie” and a broadcast network or studio has a new satirical comedy, that program can be promoted to only the viewers who have previously enjoyed such shows.

The ability to dynamically generate valuable screen real estate, as opposed to hard-coded areas of the screen, will deliver a much greater monetization opportunity. Combining this with the ability to target certain portions of a viewing audience enables not only the right content at a time that is “in the moment,” but also delivers it to the right viewer. Tracking the performance of these efforts will also allow marketers to continuously optimize not only the content, but also the ad message.

The living room is on a journey of transformation. Gone are the days of endless sifting through channels, or debating over which content is best. Voice functionality provides not only the ability to provide a clean, simple and easy-to-use interface, but also the next generation of monetization platforms.

Jon Heim is director of product management, conversation services, and Chris Ambrozic is senior director of analytics and product at TiVo.

We’re at an exciting time in society where revolutionary technology like artificial intelligence is beginning to find its way into nearly every industry. There is both a buzz about the positive benefits as well as voices of concern, but the truth is, the future of AI is a long road that we’ll all continue to walk.

As we begin to wrap up 2017, what does the near future look like for consumers and companies in the world of media and advertising? Here are three predictions:

1) The previous education period will now lead to action: The past few years have been an opportunity for businesses and consumers to begin learning more about artificial intelligence, but we’re shifting to a time of investment.

A recent report by market research firm Vanson Bourne and data analytics provider Teradata found that 80% of surveyed organizations (280 large enterprises) are now investing in AI.

This investment means that in 2018, more companies have the power to unlock previously unattainable business intelligence. Unfortunately, businesses that neglected to spend time educating themselves on the benefits are going to begin seeing the rise of a digital divide.

The same research study notes some of the reasons why companies have not yet invested: lack of IT infrastructure (40%); lack of access to talent (34%); lack of budget for implementation (30%); complications around policies, regulations, and rights (28%); and impact on customer expectations (23%).

2) On-demand media as we know it will turn into organic, real-time libraries created by AI: With consumers opting out of advertising by flocking to companies like Netflix and Hulu, AI advertising is poised to thrive in a market where customization and user preferences are paramount. Exastax says that one of the most beneficial outcomes of AI for customer service is personalization. Essentially, this means that AI algorithms will be able to track, analyze and visualize customer data (behavioral, demographic, location, etc.) in order for organizations to determine the customer identity and, in addition, to recommend the best service.

We predict that behavioral targeting will be critical as ad blocking becomes the norm. In addition, in-content advertising will become more prominent as AI can help with “pull” content. This is because all media, in all formats, will be very quickly indexed down to the second and frame, equating to truly packaged, on-demand content. As media is boiled down to topic level and word-level, or “atomized,” consumers will build specific and personalized media based on topic preference. In that context, AI will help index and create content like a playlist.

A unique scenario will be how AI can produce shows and programs for consumers via dynamic compilation development. This will be ubiquitous and crossplatform. Think of it as a collaboration between YouTube, Spotify, AM/FM radio and more.

3) If the product is changing, the workplace will, too: We’re already seeing AI become more prominently used in the media industry. For example, iHeartMedia, the leading audio company with the largest reach of any radio or television outlet in America, uses AI to seamlessly and automatically process, transform and review audio data in near-real time.

While some people are hopeful for the future, there have been others who voice concern at the prospect of losing their job. Thankfully, this is a misconception. Mass unemployment is obviously a sensitive topic, not to mention a future that no one wants, but the fear isn’t backed by the data. AI will soon be a key component of many positions throughout a company, regardless of industry, but to cite an example, Gartner predicts that by 2021, 40% of IT staff will be “versatilists” holding multiple roles, most of which will be business- rather than technology-related.

This point emphasizes that an important position like IT — one that enterprises rely on to ensure their company and product runs smoothly — won’t be eliminated, but augmented with AI.

Like any emerging technology, AI is facing tough criticism and there is a wealth of misinformation. Thankfully, we can cut through the noise in order to see the purpose-driven benefits for society with leading data and insight from top-tier plays in the industry.

Drew Hilles is senior vice president of Veritone, a New York-based artificial intelligence software firm. Image by CHRISTIAN LAGEREK/SCIENCE PHOTO LIBRARY/Getty Images.

Netflix, with its ad-free content, has profoundly changed the way consumers watch TV. Some estimates claim Netflix saves consumers more than six days of their life, or about 157.87 hours, per year in commercials not watched compared with a traditional cable provider.

To put this in context, the traditional hour of TV has almost 16 minutes of commercials.

This leaves broadcast channels and the service providers that carry them in quite the predicament. Consumers are getting used to a commercial-free experience, which is certain to affect future programming. This is because ads play a critical role in funding the development of creative video programs, and if they disappear altogether, then the ecosystem will not be nearly as vibrant.

I believe we’ll soon see a future in which there are far fewer advertisements, but more personalized content within those advertisements when they do play.

I’m not alone in this thinking. According to analyst firm Ovum, data will transform the business of visual entertainment and drive the biggest shift in the balance of power in TV in the 2020s. We’ll see increasingly granular, personalized TV services, as the main unit of TV consumption continues to shift from households to individuals.

To make this personalized advertising approach successful, service providers working with broadcasters need first to identify the target viewer persona across multiple devices. This can easily be done using Netflix/Hulu-style profile selection linked to a consumer’s Facebook or Google account. However, with privacy being front and center in the U.S. right now, service providers will need to manage consent through an opt-in before information is collected. Once access is granted, the service provider can use various data sources like social media and viewing habits to create a “360” profile that is unique to the viewer.

They have this data – they just need to know how to leverage it.

The main question here is: Will consumers be more willing to share private information if it means getting less, but more relevant, commercials during their favorite shows? While privacy is important, I’m willing to bet consumers would be willing to participate, at least at some level, if it means less friction and fewer ads.

If done right, personalized ads can generate much more revenue per view than general targeted ads today, which means a consumer could see a fraction of the commercials they do now, but the same revenue is obtained.

While it may seem like fewer commercials mean less overall revenue for broadcast and service providers, more targeted ads would increase interaction and potential purchases. According to a survey conducted by ad insertion company Yospace, 78% of audiences are more likely to take notice and 55% are likely or very likely to look online for a product if they’re personally targeted by an ad.

Broadcast and TV content providers can look to offset initial losses with options like ad-funded free content or better incentives to purchase digital content.

We are seeing some very big telecom carrier moves in the media space, such as AT&T’s purchase of DirecTV, and this creates advertising inventory space to monetize from any screen the consumer is using. With this in mind, it’s clear the advertising business is a key revenue stream to consider and perhaps revamp entirely.

While advertisements aren’t going completely away, more personalized commercials are a clear next step as service providers look to further monetize their offerings by using customer data. This is the only true way to battle the idea that content should be commercial-free due to the likes of Netflix.

Besides, wouldn’t you want some of your 158 hours per year back?

Gary Miles is chief marketing officer of Amdocs.