In conversation with Digital Content Next’s Michelle Manafy, Flipboard founder and CEO Mike McCue and Washington Post managing editor Kat Downs Mulder explore the evolution of digital media, serving the audience “where they are,” and leveraging emerging technologies to better meet their needs. Their talk, which was part of Collision Conference 2021, covers the challenges and opportunities of social media news distribution and consumption and the rise of Substack. They also talk about the challenges facing local news in particular. Their discussion explores AI and other technologies that increasingly impact news creation, delivery, consumption, and user experiences.
If you have worked in the digital space in the past few years, you’ve likely encountered the term Machine Learning (ML) in some context. For those of us who aren’t tech experts, this buzzword may be a little daunting. However, these technologies are actually widely used across industries including digital advertising.
So, what exactly is Machine Learning? To explain that, let’s take a step back and talk about Artificial Intelligence (AI). AI describes a computer program’s ability to conduct a task that would normally require human intelligence, such as drawing conclusions based on data or recognizing patterns in a dataset. Machine learning is a step above this. A subset of AI, ML is the ability of programs to “learn” and self-improve as they get more data. This allows them to make more accurate decisions and predictions over time. This greatly reduces the human input required in performing crucial tasks.
How is machine learning positively impacting header bidding
Many publishers across the world rely on header bidding as the most efficient way to boost programmatic revenue. However, the technique in itself has undergone significant advancements. Initially, the majority of publishers used client-side header bidding to maximize yield through multiple demand partners. But it had its own challenges such as latency.
Then, server-side header bidding arrived. It helped publishers combat latency issues but had its own shortcomings. Publishers now use a combination of both, which is generally known as hybrid header bidding.
Among all of these advancements, one thing has remained constant: the use of machine learning in improving header bidding for all publishers. Therefore, the future of header bidding relies on how well technology companies can incorporate ML into improving header bidding tech. This will ultimately yield a robust solution that does not require any workarounds for its peripheral issues.
Impact of machine learning on header bidding dynamics
Automatic selection of demand partners
One of the key challenges that publishers dealt with before heading bidding was a lack of control over demand partners. That is key to optimizing revenue. Without knowledge of demand partners, the entire system would lack critical transparency.
Through the use of ML in header bidding, publishers get access to multiple demand partners. In fact, the selection process ois automatic, while simultaneously providing maximum yield. This massively benefits publishers as it saves them time for other activities while ensuring a consistent increase in revenue.
For every publisher, managing timeouts is a tricky process. Timeouts can often result in lost revenue and publishers need to spend some time troubleshooting. However, ML has completely altered the game. Publishers can now use managed heading bidding solutions that utilize machine learning to fine-tune timeout settings in a way that maximizes yield but reduces latency.
Server-side header bidding
Server-side header bidding was introduced to reduce the latency issues associated with client-side header bidding. Instead of a user’s browser, the auction takes place on a server. Many publishers have gravitated towards a combination of client-side header bidding and this to get the maximum out of their investment.
The credit however, goes to ML for helping the ad tech industry in advancing this technology. Machine learning helps in tracking deal performances through automated systems. All this is done in real-time with multiple demand partners in place.
Price floor optimization
Another added advantage with using ML in header bidding is optimizations of price floors. As mentioned above, ML is what makes algorithms smart. With a consistent tracking of data, patterns, trends, demand partners, ad requests, and related factors, ML can help in automated price floor optimization. Over time, this can considerably improve header bidding performance.
Why machine learning is here to stay and scale
ML capacity to learn and improve is uncapped. This means that with time, as the programs amass and go through more data, their predictions will only become sharper, quicker, and more accurate. While ML may not replace humans anytime soon, these tools can definitely help marketing teams free up time to focus on high-level strategy, instead of repetitive data analytics.
Advertisers across the world are already witnessing increasing returns on their ad spends, thanks to ML-based optimization of everything from creatives, targeting, and bid values. However, advertisers are not the only ones who stand to benefit from the adoption of ML tools. Publishers typically receive a percentage of the revenue (through CPM, CPC or other models) and more effective advertising campaigns mean a chunk of a larger pie for publishers.
However, publishers can by no means afford to be merely passive bystanders in the ML revolution. Thanks to the data at their disposal, advertisers, today, have a clearer picture of how, where and when their ads work best. In contextual advertising, for instance, publishers need to continually create content. However, they must also evolve based on their audience’s changing consumption patterns. This requires knowing your target audience deeply, and often working with advertisers to create a synergistic experience for the audience. Publishers, too, can leverage ML solutions for layout optimization to create greater value for advertisers, and by extension for their own platforms.
Overall, AI and ML tools offer an opportunity for all parties involved in advertising technology. Early adopters of these solutions will likely see the best outcomes and returns, and given that these tools are based on the premise of self-improvement over time, it is likely that those who adopt a wait-and-watch approach will face an uphill task of catching up.
About the author
Ankit is the CEO and Co-Founder of AdPushup, a leading technology company which helps web publishers and e-commerce companies accelerate their revenue growth. At AdPushup, he has led the product development, market strategy, and business development efforts, which has allowed the company to scale to 4 billion+ monthly impressions for over 300 publishers worldwide.
Building trust in the news media is a careful balance in accuracy, bias, and transparency. Unfortunately, news brands today don’t live in isolation. Adding social media to the mix alters this balance with its potential amplification of inaccurate, out-right fake, and politically manipulated news stories. With a surge in digital news consumption, it is important to examine new strategies to renew consumer trust.
Newly honored research from the University of Florida Consortium on Trust in Media and Technology offers insight into the value of adding algorithmic reporting to human bylines to increase positive perception of news coverage. T. Franklin Waddell research, Can an Algorithm Reduce the Perceived Bias of News? Testing the Effect of Machine Attribution on News Readers’ Evaluations of Bias, Anthropomorphism, and Credibility, examines the premise that machine-based news stories are objective and “free from bias.”
Adding machines to the process
Waddell conducted an online experiment to test this premise. He tested author attribution: journalist vs. algorithm vs. combined authorship from two news outlets (MSNBC vs. Fox News) on two story topics (Khan Conflict vs. Paris Accord). In all, there were 612 participants who were asked to read a news article that was written by either a journalist, an algorithm, or by a journalist and algorithm together.
After reading the article, the respondents were asked questions on article credibility, perceived bias, and the personification of human characteristics. Waddell’s research also explored whether news attributed to an automated author is perceived as less biased and more credible than news attributed to a human author.
The results of the study showed that algorithms were perceived as less biased than human authors. Machine attribution decreased perceptions of bias, which then related to message credibility. In other words, the research found that news automation aids credibility and helps to reduce the perception of bias.
More importantly, machine and human sources combined offered a more favorable credibility outcome and the perceived bias was less than a human or machine source alone.
The research also discusses the importance of messaging this combined journalistic approach to news consumers. Waddell found a positive impact on message credibility when using a combined byline of journalist and machine. Additional research needs to be conducted on specific labeling and consistency to accurately describe the degree of automation used as well as to heighten the credibility of the news products.
Waddell’s findings that bias perceptions lessen when news is attributed to a combined partnership of human journalist and machine automation is significant. While there are concerns about biases built in to AI itself, these research insights offer a promising step that could have a positive impact on consumer trust in media news. Automation alone does not appear to be the answer. But, along with historical context, background information and fact checking, it offers a starting point. And it is critical for the news media to explore ways in which it can reinforce the credibility of quality journalism.
Consumer interaction on digital platforms is a key driver of revenue for entertainment and media companies. With increasing affordability and availability of broadband, mobile continues to be a strong contributor to the growth of this segment. However, according to the new PwC’s Global Entertainment & Media Outlook 2019–2023 Report, further innovation and personalization will significantly change how we access and use the Internet.
PwC predicts that creative new offerings and business models will increasingly revolve around people’s personal preferences. New applications will involve artificial intelligence in combination with digital assistants. Media companies will strive to build products that empower consumers to set their individual preferences and curate their own context.
The PwC Outlook Report cites personalization as a central theme in overall entertainment and media revenue growth. Global spending is expected to rise 4.3% over the next five years, with revenues hitting $2.6 trillion in 2023. The report provides a strong and notable resource for revenue estimates in the both the US and global markets.I
Additional forecasts from PwC’s Outlook Report include:
- Subscription TV revenue in the U.S. will experience a 2.9% CAGR (compound annual growth rate) decline to from $94.6 billion in 2018 to $81.8 billion in 2023. Much of the loss comes from cord-cutting and SVOD competition. Interestingly, the US remains the biggest Pay-TV market accounting for 46% of the total global revenue in 2018.
- SVOD’s continues its popularity as more streaming services are introduced and unbundling continues to grow. Newcomers to the market will need to differentiate themselves to attract subscribers.
- The OTT market is also dominated by the U.S., contributing to more than half (55.6%) of global OTT revenue in 2018. OTT video revenue in the US reached $14.5 billion in 2018 and is set to double by 2023.
- The U.S. virtual reality (VR) market registered $934 million in revenue in 2018 and is expected to grow at a 16.6% CAGR to reach S$2 billion by 2023. Gaming remains the primary application of VR, accounting for 57.4% of total VR revenue in the US in 2018. VR video, however, will see the most growth in the forecast period, climbing at a CAGR of 22.4% to reach $861 million in 2023.
There’s an important effort in today’s entertainment and media marketplace to meet consumers where they spend their time and to deliver what they need wherever they are. These sorts of personalization efforts cut across OTT, SVOD, and VR. While evolving business models around customer behavior is far from new, the renewed focus amplifies the importance of placing consumers at the center of the media experience.
C-3PO as a nightly news anchor? Alexa winning a Pulitzer Prize? These silly scenarios sound like the stuff of science-fiction. But the reality is that automation, which often takes the form of artificial intelligence and machine learning, is increasingly infiltrating the fourth estate and impacting how media companies gather, report, deliver, and even monetize the news.
From transcribing to fact-checking and polling to tweet parsing, artificial intelligence has been hard at work in newsrooms for years. However, the number of organizations large and small—including giants like The Washington Post, Forbes, AP and Reuters—using AI and machine learning to compose content is on the rise. And that’s got the industry and consumers sitting up and taking notice.
Naturally—along with those in a number of fields—there are journalists worried about being replaced by automation. However, there are many who embrace these technological advancements, seeing them as useful assistants that help process and distribute the news.
“AI can help journalists cover and deliver the news more efficiently by freeing them from routine tasks, identifying patterns in data, and helping surface misinformation,” said Lisa Gibbs, the Associated Press’ director of news partnerships.
Chris Collins, senior executive editor of breaking news and markets at Bloomberg, agreed. “Technology is good at repetitive tasks and newsrooms tend to be overloaded with those. If you leverage technology to help with them, journalists can spend more time doing journalism—interviewing sources, breaking news, writing analysis and so on,” said Collins.
Bloomberg built Cyborg, a program that extracts key info from corporate earnings reports and press releases. Bloomberg also has AI-assisted monitoring tools that rely on machine learning to filter out spam, recognize key names, and classify topics to cut through this noise and capture specific events relevant to Bloomberg’s financial audience.
“By doing that, we’re able to be more competitive when it comes to identifying news events,” said Collins.
AP uses a similar AI resource to automate corporate earnings articles. It also employs video transcription services that create transcripts for its broadcast customers, saving AP’s video operations personnel precious time.
Additionally, the AP’s newsroom is beginning to focus more on how AI can help the news-gathering process itself. “We recently completed a test of event detection tools, such as from SAM, which uses algorithms to scan social media platforms and alert editors when it has identified likely news events,” said Gibbs. “What we found is that using SAM, in fact, does help our journalists around the world discover breaking news before we otherwise would have known.”
Reg Chua, COO of Reuters Editorial, said his organization has been using AI for several years. “A lot of it is your basic automation stuff like scraping websites and pulling stuff off feeds and then turning them into headlines published automatically or else presenting this information to humans for checking before we publish. We also employ quasi automation and technology that scans and extracts important information from documents,” said Chua.
One of Reuters’ newest AI tools is News Tracer, which filters noise from social media to help discern fact from fiction and newsworthy angles from countless tweets and posts. “News Tracer’s core function is to tell journalists about things they didn’t know they were looking for—to quickly find news that can be reported on,” said Chua, who added that the tool provides a newsworthiness score and a confidence score to help reporters determine what to focus on.
Big and small papers benefit, too
RADAR (Reporters and Data and Robots), a London-based news service, has been a trailblazer in the realm of AI-reported local news.
“We operate as a news agency with a subscriber base of UK local news publishers,” said Gary Rogers, RADAR’s editor-in-chief. “We employ six data journalists. Our reporters work largely with UK open data, seeking out stories that will be relevant and informative for local audiences. They work as any data journalist might in finding the stories, but they use software as their writing tool in order to produce many localized versions. These are distributed to local news operations all over the UK.”
Rogers noted that AI allows RADAR to achieve a scale of story production that would not be possible by human effort alone.
“We tackle about 40 data projects each month. Each project will yield an average of 200 to 250 localized versions of the story,” said Rogers. “Since last autumn, we have been producing between 8,000 to 10,000 stories per month.”
Smaller community newspapers are investing in big machine learning capabilities, as well. Case in Point: Richland Source, a Mansfield, Ohio daily, uses a program called Lede AI to automate local sports reporting.
“Lede Ai writes and publishes game recaps for every high school sporting event in Ohio immediately after it finishes,” said Larry Phillips, managing editor of Richland Source. “If it’s a big game, we will send a reporter and Lede Ai writes and publishes the first draft; our journalist adds color, flavor, and flare that can only be done by being at the game. With Lede Ai, we’ve never received a complaint about inaccurate reporting, and we’ve published over 20,000 articles.”
Education and transparency
News media professionals worry about human obsolescence in the face of such quickly accelerating automation. Yet many believe those concerns are premature or misguided.
“While this has been true in most industries and may happen in media, there is a broader picture of AI’s enabling rather than employment-destroying qualities,” Rogers said. “AI can take over repetitive and boring tasks, which frees journalists to do more important work. It can help journalists find stories by sifting large amounts of information. In our case, it allows our reporters to amplify their work, write a story in the form of a template, and produce hundreds of versions of the story for local newspapers across the UK who lack the resources to do it themselves.”
Consider, too, said Phillips, that “AI still can’t ask follow-up questions, can’t knock on the doors of multiple sources, work a beat, make a follow-up call, do the shoe-leather grunt work, garner an off-the-record comment which leads to a story angle, and certainly can’t replicate the human element, the nuance, that encompasses the very best work in the profession.”
Even if their human resources are relatively safe for now, news organizations have to navigate carefully through uncharted waters when it comes to ethics around and disclosure of AI practices.
“As these technologies evolve, having standards around transparency and best practices – such as how do we prevent bias in data from impacting our news coverage – will be critical for the entire industry,” added Gibbs.
Bloomberg’s Collins echoes that sentiment. “It’s essential to understand what technology can and can’t handle. Clearly, as with all journalism, you need judgement, best practices and processes in place to ensure what you are writing is accurate, fast and worthwhile,” said Collins. “You need to be transparent about how a story was produced, if it was assisted or published using AI. In our experience, the combination of years of human journalistic experience with technology such as AI is powerful. Obviously, the technology isn’t left to run the newsroom. It is trained and overseen by journalists, who are learning new skills in the process.”
Reading the tea leaves
Looking ahead, artificial intelligence will create exciting new capabilities as well as troubling obstacles, say the pros.
“As newsrooms increasingly embrace AI, it will help with everything from spotting breaking-news events, to finding scoops in data to audience personalization,” said Collins.
But prepare for even more fake news fiascos.
“Distribution of so-called deepfakes, assisted by AI, is a troubling trend,” Collins cautioned. “How technology evolves to both spread and combat misinformation will be a major challenge for the industry.”
Yet Richland Source publisher Jay Allred and others remain optimistic. “In the near-term at the local level, I think AI will largely be used for two things. First, it will fill the gaps on informational journalism tasks that simply are not done anymore due to shrinking payrolls,” said Allred. “Second, it will surface insights from public databases—finding out, for instance, how a particular city floods and where, how many speeding tickets were issued and where throughout a state, where do the most citations for drunk and disorderly conduct occur within a city. This will spur and support investigative journalism that wouldn’t otherwise happen.”
There isn’t a company leader out there today who doesn’t realize that their ability to harness and interpret data will make or break their business. When it comes to data analytics, the bar is constantly being raised. What was a theoretical concept just one year ago is now mainstream practice in major tech companies. Leveraging data to become more AI-driven is (or should be) on every CEO’s mind.
In fact, research suggests that by 2020, 30% of digital commerce revenue growth will be attributable to Artificial Intelligence (AI) and analytics.
As a content marketer, imagine if you were able to microsegment your readers in the moment and understand which content to best show them to drive subscriptions (or continued subscription). If that sounds appealing to you, read on.
First Party Data
Just look at the renewed importance of first-party data (data you own). In the past, third-party data, or data collected from aggregators, was considered the best way to market to customers. However, privacy concerns (it includes things like purchase and browsing history) have made it less viable.
First-party data, on the other hand, includes website traffic statistics, owned email marketing data, e-commerce data, and the content you publish and promote on your site. The best part is that it’s completely within your control. Also, it builds on itself over the years, which gives you deeper insights than you could have obtained through third-party data alone.
Today, content companies that leverage first-party data are better-positioned to make content recommendations and provide better customer experiences. But to give your customers, users, and readers what they want, you need to be able to process and react to data in less than one second.
And assembling, analyzing, and interpreting the data is no small feat.
From Data to Intelligence
According to 2018 data from Nielsen, Americans spend more than 11 hours consuming various forms of media. And adults between the ages of 18-34 spend as much as 43% of their time reading and viewing media on various digital platforms—a number that’s almost certainly higher today.
All of which is to say: There’s more first-party data up for grabs than ever before.
However, today’s users also expect that content to be relevant. That’s why the most effective content providers not only need to provide content that readers care about, but also content that’s highly tailored to their experiences in that moment.
And in the era of digital transformation, where AI-driven businesses are poised to take in close to $3 trillion, you need a data strategy that performs efficiently.
AI, combined with traditional analytics, can help your company comb through huge amounts of data in real time. And every day you put it off, the data set grows even larger and harder to manage.
Here’s how to harness the power of the data you already have and deliver the content your users want:
Data scientists are often too bogged down with grunt work to focus on what matters.
According to a Deloitte survey, 52% of respondents said they couldn’t make information-backed decisions because the data they needed is trapped in siloed departments. When data is isolated, it’s hard to wrangle and figure out how to analyze it. So data scientists find themselves spending 80% of their time on mundane work like figuring out which data to use, managing data movement on and off the Cloud, and understanding what attributes make the most sense to use in your models.
And after all that grunt work, there just isn’t time to do the high-value analysis.
So when you ask your data science team to tackle a portion of data, you may not understand there are many different moving parts that have to come together besides the analytics. And just because your data scientists have PhDs in mathematics or statistics doesn’t mean they have the expertise in data engineering and management to get it all done.
All CEOs need to make sure there’s a programmatic and structured way to enable data science to be ingrained in the data culture—not an afterthought. This means prioritizing building and updating the infrastructure so that there’s time for the most important analysis.
A strong data science platform makes room for truly valuable analysis.
A lot of the businesses we work with at my customer engagement company, LiftIgnigter, say it’s the operationalizing or the engineering part of data analysis that’s the most time-consuming.
It is easy to become overwhelmed by the challenge of turning data into an integral component of business operations—companies often don’t know where or how to begin.
To access the value of your business’s data, your data science team needs to develop not only a clear data strategy, but also find a programmatic way to make this second nature. Typically, this requires either building a platform or leveraging a third-party platform. A third-party platform helps data science teams aggregate the data, ultimately making data analysis a much more democratic process.
But it’s equally critical that the platform is built and fine-tuned for your company’s specific needs. A generic platform may give you lots of different options to run model testing, but at the end of the day, you need to focus your effort on modeling that is the most likely to yield meaningful insights.
However, with the right platform, which typically combines data science and machine learning, a team of five people can be effective as a team of 50. Imagine how much more efficient your team would be if they could focus their time on higher-level analysis.
AI augments the work your data science team can do, in real time.
With AI becoming increasingly ubiquitous, people are worried it will take their jobs. But AI isn’t replacing the data scientist. It’s actually making them that much more effective.
Here’s why: Training user targeting models is one thing. But then your team has to figure out a way to operationalize the continuous updating of the data.
For example, a large publisher company with 40 data scientists can still take several days to do all of the analytics that their editorial or product teams are demanding in real-time. They can hire 1,000 people—but that’s not cost-effective. The data science team is often called on to find compelling nuggets in your data in real-time. They’re constantly bombarded by your revenue team and marketing teams to come up with the perfect segment that will help drive greater revenue.
Large media companies like Facebook have a keen advantage because they have armies of data scientists to dig through their mounting data and figure out what’s relevant. And this is where AI comes in.
In order to compete, your business needs to make that process as standardized, real-time, and with zero wasted effort.
Third-party platforms enable businesses to create, tune, and refresh high performing models efficiently. Whether it’s generating demographic data for audience creation, or live streaming user behavioral signals to train a targeting system, a third-party platform can do a lot of the heavy lifting for you.
If you can create a unified strategy across your organization, optimize the data you have, and work only with trustworthy third-party partners—you’ll be prepared to navigate this new landscape.
To answer that question, we need to look to the past—1440 A.D., to be exact. When Gutenberg created the printing press, it was suddenly possible to print and distribute thousands of copies of written material, all typeset so that the letters lined up perfectly every time.
Since then, the way we read text—including online—has changed very little. But that doesn’t mean it hasn’t evolved at all.
When I was living in Japan in the 90s, for example, everyone read the newspaper on the train. Japanese newspapers, unlike the papers you find in the U.S., are designed to be folded in half as you read because the trains are so crowded. You couldn’t open a Wall Street Journal arm-to-arm on the train in Tokyo because there simply wasn’t enough physical space.
But today, the way we consume content is not restricted in that way. We’re much more likely to read the news on our smartphones and laptops.
Instead of holding the physical paper in our hands, we scroll away on phones or tablets. But we’re still attached to some old-school tactics that make less sense today. Things like above-the-fold, which is a vestige of newspapers that were folded once they hit the stands so that only the top half of the paper was visible to the passers-by. This basic format remains the same for digital content, but it no longer serves much of a purpose.
Since the advent of the printing press, technology has enabled us to deliver more content more quickly to more people than ever before. As digital technology continues to evolve at an increasingly rapid pace, it’s time authors and publishers rethink how we present digital content. Just as a Japanese newspaper was designed to create the optimum experience for reading on the train, we have to evolve the way we present online content to give users the best experience.
Your users live in a digital world. Here’s how to meet them there:
It’s time to ditch trees, categories, menus, and the like.
Navigation is among the most important elements of web design.
The original navigation favorite was the tree structure, which was popularized with the explosion of Yahoo and its original navigation. It presents a hierarchical view of information, in which each item (or “branch”) leading to a number of subitems. Any branch can be expanded to reveal subitems, or collapsed to hide subitems.
However, with the rise of mobile devices, the once common tree structure is falling out of favor. Designers are creating new and improved ways of navigating websites. They have stopped. forcing users to navigate a tree, a giant list of categories, or even think of search terms. Instead, they are thinking of ways to create a digital user experience that is responsive in real-time.
Just as Google’s search engine rendered Yahoo’s original navigation approach obsolete, AI is pushing out traditional web navigation. An AI-driven site design that can pick up on and infer user signals and direct them accordingly is much more user-friendly. Before long, all websites will be creating menus, landing pages, and content generated dynamically in response to user understanding.
Ideally, prebuilt navigation should be as streamlined and minimalist as possible. When done right, it allows users to open your website or app and find what they’re looking for instantly.
Otherwise, users will become frustrated and sign off—and that’s the last thing you want.
User engagement is at stake.
Today’s consumers are less patient than ever.
According to a 2015 study from Microsoft, the modern American brain loses concentration after eight seconds, a result of our increasingly dependent relationship to technology. “Heavy multi-screeners find it difficult to filter out irrelevant stimuli,” the report read. And they’re “more easily distracted by multiple streams of media.”
Today’s customers expect instant gratification. They want to be met where they are, with an article or product recommendation that’s relevant for them at that exact moment, regardless of the channel or device in use. Real-time customer engagement requires knowledge of past purchases, specific preferences, and situational context to make instant recommendations.
Without it, your customers will go elsewhere.
Data shows that modern real-time companies that embrace this type of real-time responsiveness see double or triple level of user engagement. If it’s a content site, that means up to three times more people watching videos or reading articles, or becoming paid subscribers. On a product site, it means tripling the number of people buying your stuff.
If you’re not leveraging AI to give users a better experience already, you’re leaving money on the table and users in the dark.
Soon, it won’t be enough to just show users a few options based on past purchases. The next iteration of digital experiences is content presented based on behavioral and external cues.
Rappi, or what I like to call “the Uber Eats of Latin America,” is a great example of this next generation UX and a customer of my company, Liftigniter. The app determines what you’re interested in eating and delivers it to your door—whether it’s a four-course meal prepared by a restaurant, fast food takeout, or ingredients delivered from a grocery store for you to cook on your own.
They use our AI engine to make individual determinations based on such signals as users’ behavior in their app. It understands what devices they’re using, where they are, and what time of day it is. Using this information, they can make behavioral and environmental inferences. This allows them to determine whether you’re interested in something healthy or comforting, quick or slow, fancy or casual. When it’s cold out, they’ll suggest even suggest a warm holiday soup.
If you can create a website or some sort of digital experience that can determine the user interests without them telling you, why wouldn’t you? The only conceivable reason is habit. It’s natural—and easy—to cling to the status quo.
However, the printing press no longer reigns supreme. And it”s time online publishers shake off the vestiges of an archaic user experience.
UPDAY has embraced this view. The mobile news app owned by digital publishing house Axel Springer pairs machine learning with human judgment to deliver users personalized news and information aligned with their explicit preferences and implicit requirements. Operating in 16 countries across Europe, UPDAY has established eight editorial “hubs” where teams of local journalists review content from the top news sources to pick top stories and news consumers will genuinely appreciate.
Peggy Anne Salz – mobile analyst and content marketing strategist at MobileGroove – catches up with UPDAY CEO Peter Würtenberger to discuss how the company’s approach to news curation and aggregation has allowed it to build partnerships with publishers, deepen engagement with users, and optimize content delivery to a plethora of devices and platforms.
PAS: AI and algorithms have a legitimate role to play in matching audiences with information they will likely appreciate. But we also see what happens when judgment is left to the machines. How do you maintain a balance?
PW: Fake news happened because companies relied 100% on technology, and this is what we have avoided at UPDAY from the start. Part of it is because, unlike the Apple, Facebook, and Google, we come from the newspaper business where journalists are the most valuable resource, not an overhead. Axel Springer is one of the leading publishers in the western world, selling over 1.5 million copies of the Bild newspaper daily. This was possible because we relied on journalists. At UPDAY, rather than leave news decisions to algorithms, we combine the intelligence of machines with human judgment to deliver personalized news that doesn’t trap audiences in a filter bubble.
The algorithm aggregates news—what you want to know—by understanding your personal interests and preferences out of approximately 300 hand-picked sources per country. There’s a dedicated team of Content Engineers that carefully checks each source in each country before integrating it into our source set. The human—in our case, eight editorial hubs in Europe where editorial teams on the ground curate news and information – judges and delivers what audiences need to know. This is the news of the day that matters, and we rely on a team of trained journalists in their fields to make this call. It’s about serving up the best of both worlds, the best of what technology and humans can offer when they work together.
PAS: You are aggregating content from original sources and packaging it with the help of personalization for your users. Tell me about your audience and their usage.
PW: The feedback we have from our users since day one is that they feel safe and confident that they are reading what really matters to them. This tells me that a user-centric approach to deliver the perfect and personalized mix of stories is working out very well. Users are engaging with UPDAY and highly appreciate the variety of our media brands. Our sources include the top 100 publishers in each of the markets where we are operating in.
We launched UPDAY in March 2016 and last year we counted around 10 million users. Today we have more than 20 million users spread across 16 countries, which makes us the fastest growing news app in Europe. A user session is around 5 minutes. We have more than 3 billion page impressions per month. And we aggregate more than 3,500 sources.
By the way, the publisher also gets a massive amount of traffic from UPDAY, and in some cases 10% even 15% of their mobile traffic comes from Upday. We aggregate their content—snippets with headlines and some body text from the publisher which they provide as part of the RSS feed—and, when the user clicks on the story, we send them directly to the property of that publisher. This is what publishers appreciate most. Unlike Facebook, which keeps all interaction and news consumption in its ecosystem, we drive traffic to publishers.
PAS: You deliver personalized news across over a dozen countries and languages. Do you rely on translations and localization to keep it relevant, or is there something else at play?
PW: We don’t translate any of the content, because that wouldn’t serve the user’s interest. Instead, we serve the users with their local sources in the local language. Our local team of journalists—the quality control, so to speak—is responsible for selecting the 30 to 40 most important top news stories per day and curating them, so they appear in the top news section we show to the user.
Clearly, this isn’t the way all media companies approach localization. Some agencies prefer to translate content from English to Spanish, for example, in order to serve it to large audiences. But we don’t believe this is the right way. In our view, it’s a better experience to source the local sources and media brands in the local language. And that’s the beauty of UPDAY—and now we see that other products and offers are changing to do it this way, too. I can only say we have been doing it like this for over two years and it’s great to see how others are understanding why this is the better way to deliver news and now come up with similar offerings.
PAS: You have engineered the algorithm that you pair with the human intelligence of your editorial hubs to deliver personalized news. How does this combination work to ensure the delivery of more relevant advertising alongside this news content?
PW: Our editorial competence and the understanding of the user behavior enabled us developing an offering that addresses various needs of the advertisers and brands. UPDAY offers a premium user experience with a flow of content and integrated native advertising which does not disturb the flow. It’s not a layer ad that users see and click it away. It’s in the natural stream of the news stream, showing every sixth card on average. It’s also the approach that kicked our monetization forth.
We started with an offering for so called direct sales – premium formats adjusted to the needs of our clients. We talked to clients and agencies and they booked display ads and video ads. At all times, our priority was to develop an advertising offering that enhances, not interrupts the user experience, where we could be the platform that brings advertisers closer to users. Our understanding of the users’ interests plays a crucial role here.
We also integrated a programmatic technology into UPDAY. It became the second phase of UPDAY’s advertising. But we established our capabilities as an SSP. We did this together with AppNexus and with Google. We started with programmatic native advertising that was perfectly aligned with our content. On UPDAY every news card has a photo, a headline, and text— native advertising looks similar to that and attracts the users with a great strength. Together with the data, it boosts our capabilities to deliver the right advertising in the right moment to the right user.
PAS: So, what are you seeing –and what are the lessons for other media companies that seek to monetize their assets and audiences?
PW: Our click rates are beyond expectation because our experience pairs human sense with data intelligence. We are seeing between 0.5% and 0.8% for display and more than 1% for native ads – regarding formats which are non-intrusive and integrated in the flow of UPDAY news. We’ve also seen native campaigns where we get between 5% and 6% click-through thank to our optimization measures. Overall, this is far above the industry standard, which hovers at around 0.1%.
It’s a combination of our human salespeople with our ad tech that leads to a success. We have teams that go to the companies and brands and say, “Hey, we have a high-quality, Europe-wide platform which is a perfect place for your marketing communication.” Once the brands realize there is more than Facebook, they are on board. We are running direct campaigns for SEAT in five major markets as a result. This is premium advertising with higher CPMs on a high-quality platform. We rely on human teams to understand what brands want to communicate and work with them—and really show them–what is possible on our platform. It’s a dual play between people and a constantly developed advertising product, and that is the play that gets advertising right.
If I look out at many media companies out there, they are still filling their pages with what they have been showing for last 20 years. It’s nothing more than a copy of the first page in their newspaper. Advertising is similar. It overwhelms the user with too many blinking parts, banners and annoying interruptions. This is a mistake, and so many content companies are still stuck in this rut. I would recommend content companies change this by introducing more user-friendliness and personalization into what they offer and how they deliver it. For the companies that make the algorithms—the Facebooks and Googles that read the user signals to personalize the content and advertising—they should look for ways to introduce a human touch and ‘humanize’ their tech because that is what they are lacking. It’s got to be user first —and the advertising needs to be highly personal and highly relevant.
PAS: You are also aligned with technology and your partnership Samsung is evolving to take you beyond the smartphone. What are the new opportunities on the horizon?
PW: It started out as a strategic partnership between Axel Springer, an expert in journalism, news, and creating a digital information brand, and Samsung, an expert in engineering and building devices. We pair our learnings from the news business and our learning algorithm with Samsung’s leadership position in devices and distribution. Remember, globally Samsung is far bigger than Apple.
However, it’s not just about having a larger share of the smartphone market. It’s about the breadth of devices and platforms where we can provide news content. We are already on the smart watch, and we are also the news source on the Family Hub that Samsung enables on its smart refrigerators. Most recently, Samsung has integrated our news app on their ambient QLED TV screens. These are screens that just turn on when you pass them, and UPDAY news is what consumers will see when they interact with that screen – across 12 countries in Europe.
Now other industries are contacting us. The car industry is asking us to develop an integration for smart cars to deliver personalized news in the car. We are thinking about how we can provide news in this environment, and it’s clear that the news we aggregate will also have to be read out-loud to the consumer.
PAS: But that also brings challenges as humans can’t scale, and your editorial team will have to…
PW: It has to be scalable. And you do this by making sure all the content comes from a single platform. If we were to start building content data platforms for each of the platforms we serve — for smartphones, watches, fridges and now TV — we would be dead from the sheer complexity of it. All those different devices and platforms must be served from the same content engine, from the same algorithm engine, and from the same journalists who are curating the content. This is what we have built and manage.
It’s one content platform, and you see the same content — even the same headline — on all four platforms. But it will get more complicated when we add voice—and voice is coming. It’s very early in the market, and there are great text-to-speech engines around, but this is just the beginning. Some of our main challenges will be: How to deliver the most relevant content in the most convenient and appealing way to the audience? There are legal issues to solve and we have to consider ways to monetize most efficiently, of course.
Print media was disrupted by the Internet and mobile is disrupting online. Now voice is poised to disrupt everything we as an industry have known or done so far. The best preparation for a media company is to be paranoid. Watch everything, experiment everywhere and execute on the ideas with potential. It’s why we maintain and motivate a startup culture in our company determined to stay alert and always be open to drastic change. It’s better we disrupt ourselves from within than risk being disrupted by a trend or technology beyond our vision.
- Workflow Efficiencies: One of the largest benefits of AI is how much time it can save on the user side. Without AI, proper campaign optimization takes a lot of time and is absolutely more art than science. Just consider how much data is available on each individual. Even with a target persona in mind, sifting through vendors and guessing at which attributes will perform best is a costly and time consuming exercise at best. Once that’s done, the ad trafficker then needs to toggle pacing, pricing, and potentially dozens of other variables. AI can automate much of that. At which point, the user just needs to pick a goal the AI can optimize toward and let it run giving directional guidance where necessary.
- More Data Processing than Humanly Possible: Big data and AI go hand in hand regardless of the industry. JP Morgan even published a massive white paper on how they think those two trends will affect investing. When it comes down to it, programmatic trading isn’t all that different from programmatic advertising. It’s all about automated buying and selling to maximize value. AI can “see” and consider as many features as it’s been trained to, considering hundreds or even thousands of variables over the course of a campaign to determine significance. That’s just not something humans can do in any cost-efficient manner.
- ROI: What happens when you put workflow efficiencies and maximum data activation together? Cost savings. Lots of it. Assuming your AI strategy is working (and your mileage may vary), adopters of AI stand to reap massive benefits. Since AI requires less human capital to operate, adopters stand to gain from not having to hire as many heads and the heads they do hire aren’t focused on tweaking knobs and levers manually. Additionally, since AI learns as it goes, performance constantly improves over time as it begins to distinguish between what’s important and what’s irrelevant.
- Black Box Algorithms: Unless you’re building your own, it’s pretty difficult to know exactly how an AI algorithm works. Two primary reasons for this: 1) The features an algorithm considers are typically a company’s secret sauce, and asking a company to publicize everything that goes in is like asking KFC to share their 11 herbs and spices. 2) Even if there is a degree of visibility into what features are being considered for optimization, oftentimes the amount of data being processed is more than what a human can parsethrough (see Pro #2). Which begs the question…what’s the point of performance if you can’t explain it?
- Not All AI is Made Equally: If AI is a brain made to learn for a specific purpose, who’s to say whether you’ve chosen the AI equivalent of Einstein or your bratty seven-year-old neighbor? Every partner’s going to represent themselves like they’re Watson, but realistically, that’s impossible. Some partners are better for specific industries, some are probably pure vaporware. Choosing the right partner isn’t easy, and if everyone’s offering an AI solution it’s difficult to say which is the best one for you without at least some degree of upfront investment and a decent amount of research.
- ROI: Similar to how properly implemented AI can generate huge savings, it can also be a massive sunk cost. The initial barrier to entry – either investing in developing your own algorithms or paying a partner to use theirs – is going to be fairly substantial for most advertisers or publishers. There’s also no guarantee that it’ll work in every scenario. As much as partners would love for you to believe that their AI will make it rain gold bricks every Sunday, that’s just not true. When choosing a partner, don’t just think about their historic performance, but also whether they meet your needs in terms of transparency in both costs and reporting.
As far as AI pros and cons go, it’s hard to say whether AI is right for you. That said, AI is becoming an increasingly important part of a greater shift in the digital advertising ecosystem, and I’m personally interested in seeing how it adapts to other trends. Will AI specced for second price auctions succeed in first price environments? How about in a post-GDPR world? Will the new data restrictions affect performance and will new strategies arise as a result? Who knows, but I’m looking forward to finding out!
Today, more and more resources are available digitally. The days of manually searching for data in basement archives are long gone. However, given all of the digital resources available, the process requires significant human effort. AI and automated journalistic processes can help ease that burden.
The Tow Center and Brown Institute have identified three major journalistic AI achievements to date:
- Finding needles in haystacks: AI can find and fact-check faster than the human eye.
- Identifying trends: AI can parse through data, again faster than humans, and group findings into categories to identify trends.
- Examining an application of AI or computation as the subject of the story itself: Since AI algorithms are built by humans, AI can also proof itself for unintentional bias in its applications and outputs.
Further, there are several new successful applications of AI in newsrooms. One is Wibbitz, a resource used by USA Today to create short videos. Others include News Tracer, an algorithmic prediction tool that helps Reuters journalists gauge the integrity of a tweet and BuzzBot, software from BuzzFeed, which allows the collection of information from on-the-ground new sources. Still, journalists must be careful to evaluate the credibility of AI data, its sources and understand how the algorithms work.
Key steps to integrating AI in the newsroom:
- Train editors and reporters to incorporate AI as a new resource for storytelling.
- Develop and promote the use of AI guidelines regarding the ethical use of data. Further, public disclosure of methodology is a must especially in terms of editorial values and standards.
- Small operations for which AI is too expensive should consider partnerships with academic institutions.
- Reporters and journalists should continue to be transparent about AI usage in a report or how it’s used in the production of a story.
According to The Tow Center and Brown Institute, journalists have two main responsibilities. First, they need to present the information to the reader in a clear and concise manner. And second, they need to explain its authenticity. This includes the practice of AI, including full disclosure of details and formulas for its algorithms. Importantly, as AI helps facilitates the newsroom, journalists need to question and critique the process and the information received.
The Associated Press — a 170-year old news organization with teams in over 100 countries and one of the world’s most important archives of audio-visual archives of news, social history, sports, and entertainment — is going one better. It’s exploring new and rather unconventional opportunities, in areas ranging from data-mining to data journalism, to identify new markets and revenue opportunities for its wholesale and non-profit businesses.
Peggy Anne Salz, mobile analyst and Content Marketing Strategist at MobileGroove, speaks with Ted Mendelsohn, AP Vice President, Commercial and Digital Markets. They discuss the company’s mission to expand distribution of its archival content, extract value from its data, and enhance news-gathering capabilities.
PAS: On any given day, more than half the world’s population sees AP content. But that’s just one side of your business. Tell me more about your wholesale business and the opportunities you pursue.
TM: When I was brought into AP some 25 years ago, the commercial business focused on selling AP content into the federal government, corporate markets, and large clients, including Prodigy, LexisNexis, Dialog. Expanding this by identifying new markets and opportunities is very much what my job is about today.
Another part of the business is our retail business, where AP mobile comes into play. The focus is on making our own content available on AP-owned and operated sites and monetizing through advertising.
Finally, there are content services, where AP — because of its huge footprint worldwide and access to photographers and videographers — can work together with clients. It’s a service and a business opportunity that we see expanding. are exploring opportunities where brands might sponsor content like the AP Top 25 college basketball or college football rankings. There are also opportunities for companies to sponsor unique content. This might be along the lines of the top 5 things you need to know about ways you can improve health and fitness. We are open to doing more of that and that’s also where having our own platform opens a whole line of revenue and opportunities.
PAS: AP is perhaps best known for frontline, breaking news content…
TM: Yes, it’s our bread and butter. We’ve noticed that our audience is heavily engaged with our content — stories, photos and video — and that the sessions are long. In fact, in August 2017, a survey from NewsWhip showed that AP drove higher total engagement on Facebook than any of the Top 10 individual publishers in June and July. This achievement is also linked to our ongoing efforts to update our content and add value. We provide alerts, but we also add to the news content from every angle, enhancing the story with text, photos, and video.
PAS: You’re using technology to expand and enhance distribution of your content. What is the role of technology in the production of content?
TM: AI is a technology that has an impact at several levels. We’re using it, but we’re also educating the media by showing the example of how we use AI within our newsroom. A lot of our efforts around understanding and using AI in the newsroom is focused on producing the routine news, like sports scores, and have them generated through AI technology.
But it’s not just about automation; AI can open opportunities for our reporters to cover more important stories and produce the exclusive in-depth content that wins us — and our clients — audiences on mobile and other platforms. And that is what drives the higher engagement. A good example is one of our most successful stories, what we’ve been calling the “Seafood from Slaves.” Here our reporters won the Pulitzer Prize for Public Service for their investigation that exposed slavery in the Southeast Asian fishing industry.
PAS: What are the other technologies top of your radar?
TM: At one level, AP is a retail store, for lack of a better word. We focus on approaches that will allow us to appeal to our readers directly. We ‘sell’ them on our content on the platforms, such as mobile, where they want to consume it. But it’s also about understanding how other companies and platforms are going to impact how we engage audiences. A prime example here is voice and deciding how we engage with companies that are creating voice-activated content.
It means talking to the Amazons, the Apples, and the Samsungs — companies now looking for content that is driven by voice-activation. For us, it’s becoming a new way of engaging with the customer, if you will, by creating content and adjusting our content for this market and working with companies who are attracted by the content we have and the platforms we can serve.
In other words, it’s not just the technology that we use internally. It’s working with the companies who are really technology-driven and finding ways to use our content to improve their technology and, at the same time, to make our content available in new and different ways.
The number one question for AP is: how do we move our content and make our content play across the platforms? My first boss at AP used to say he wants to ride every horse in the race. And, in some ways, that’s what we’re trying to do. We are on the horse that allows us to display and distribute our content. And we are riding the horses that allow us to get our content to the companies out there that need our content to engage their customers.
PAS: AP is exploring AI, launching a VR and 360 video channel in collaboration with AMD, examining the opportunities around voice and Internet of Things. How do make choices about the companies or platforms to explore and the ones to ignore?
TM: It’s not about betting on the newest technology or the ‘Next Big Thing.’ You also have to be flexible enough to adjust to what is coming out on the market. As an industry, we couldn’t have anticipated a technology like Amazon Echo and its impact. We also couldn’t have known the content these platforms require. But once it’s gaining traction on the market, like it is now, the best advice I can give content companies is to be flexible. You cannot shut them out; you have to engage.
What do I mean by engaging? It starts with the way I organize my group. Specifically, I’ve brought people together who have a focus on vertical segments. Some are in continuous discussions with industry leaders — they are in talks with Amazon, Apple, Yahoo!, and so on. It’s not a discussion like “Oh, we have this content for you, why don’t you sign a deal with us?” It’s a dialog where we want to understand where they’re going and they’re coming back to us with insights on the tech and opportunities that have real potential.
PAS: Data is hailed as the new black gold, and you have stockpiles of it. How do you view the opportunities in unlocking that data for clients?
TM: On the data side — for example, election data — we are the primary source for our clients. We’re finding that election data, even older data, is highly valuable to hedge funds. We make that data available for them to study and make whatever algorithmic assessments they feel necessary based on the data.
Data is also at the core of our edge in investigative reporting, identifying trends and news ahead of the competition. For example, an AP analysis of charter school enrollment data allowed us to expose the growing level of racial segregation in schools. Recently we reported on crime in the cities, using the data to take a different perspective. Rather than look at crime growing in cities, we used the data to examine crime in particular neighborhoods. Data allows you to see this, and so we are finding ways to make this data available for our reporters and for other organization to use.
PAS: So, data has become a new commodity?
TM: Maybe commodity is not the right word. Let’s say it’s a valuable good that we can offer and sell because other companies — businesses, financial institutions, hedge funds — are evolving to use data in ways that we don’t.
There are two ways to look at the way marketplace for data is developing. One is the opportunity at the consumer level, where more and better data can improve marketing, advertising, and understanding your audience. The other is the opportunity at the commercial level. Companies need access to data — for example, election data — to identify and understand the trends, and make investment decisions based on the combination of data.
It’s early days, and frankly, no one is exactly sure where how data will play out. But we are seeing that a number of financial institutional are looking for data to enhance their own data. It’s why I have some people on the team who are working with financial institutions, trying to understand what they need so we can extract data to make these datasets available in the way our clients want them.
PAS: Content and data — the opportunity is in being flexible in your choice of platforms and models…
TM: Correct. And the third part is being flexible in how you do business. You can’t be limited in how you do business or the kind of business terms you negotiate. All of us in the media industry have models, pricing lists and stuff like that. I threw those models right out because I realized they don’t work. The next technology comes around, and whatever pricing model you have doesn’t work. Instead, you have to adapt to change. You have to adjust your content, and your business model has to be flexible as well.
Peggy Anne Salz is the Content Marketing Strategist and Chief Analyst of Mobile Groove, a top 50 influential technology site providing custom research to the global mobile industry and consulting to tech startups. She is a frequent contributor to Forbes on the topic of mobile marketing, engagement and apps. Her work also regularly appears in a range of publications from Venture Beat to Harvard Business Review. Peggy is a top 30 Mobile Marketing influencer and a nine-time author based in Europe. Follow her @peggyanne.
PwC’s Consumer Intelligence Series, Can you find that show I didn’t know I wanted to watch? How tech will transform content discovery focuses on the key questions of digital content discovery, its influencers and improvements needed in personalized recommendations.
The Appetite is There
Consumers have an appetite for new content. Half of consumers (55%) report they are looking for a new TV show or movie to watch at least once per week; 83%, a few times per month. Close to three-quarters (72%) of consumers are watching more video content than a year ago and just less than half (46%) are paying for more content.
Yet, consumers are frustrated with the content discovery process. Nearly two-thirds (62%) of consumers agree that they often struggle to find something to watch, despite there being many choices available to them. Further, the findings show that half of consumers (50%) are frustrated when they search for content to watch compared to finding content to read (37%) or music to listen (32%).
Interestingly, pay-per-view customers (38%) enjoy searching for new video content to watch more than cord-cutters (31%) and cord-nevers (23%). Even cord-cutters are bothered by the process of finding content to watch. In fact, 74% agree that despite there being a lot of choices available to me, I often struggle to find something to watch and 61% also agree that searching for something to watch is frustrating.
There are several key influencers informing consumers viewing decisions. Streaming content plays an important role in content discovery. Eight in ten of all consumers (79%) and 90% of consumers under the age of 30 years old agree that streaming services play a large role in their discovery of new video content. Social media also helps consumers find what to watch (50%), especially for those under the age of 30. Interestingly while there are frequent discussions on social media about video content to watch, consumers don’t necessary based their viewing choices on these discussions.
Further, less than half (48%) of respondents said they are influenced by what their friends and family watch. Meanwhile, FOMO (fear of missing out) reportedly also drives 25% of consumer viewing habits.
Pay-TV subscribers and non-pay TV streamers differ in the top influences on new content discovery. Personalized recommendations appear to be missing its mark ranking number six for pay-tv subscribers and ranking number four non-pay TV steamers.
Browsing is still a popular way for consumers to find video content to watch. Almost half of consumers (47%) report that they came across a new show they recently watched while browsing for something to watch. The other top responses included commercials/advertisements looked good (44%), read a great review (32%), recommended to me based on another show I previously watched (27%) and people I know wouldn’t stop talking about it (23%). Consumers are also unpredictable. Eighty percent state that what they choose to watch is largely driven by their mood on that given day.
While 79% of consumers report they’ve watched a TV show or movie based on a recommendation from a content service and 90% state like what is recommended to them, personalized recommendations are still not the go-to source for consumers. Four key contributing factors as to why personalized recommendations are not working for consumers include:
- Friends and family know better
- Personalized recommendations are the shows the services are promoting
- Not sure if the recommendation will be liked
- Don’t want to waste time on starting a new show that may not be liked
Consumers want more clarity as to what is behind the personalized recommends. Consumers report they are more likely to watch personalized recommendations if additional context is included:
- Provide criteria for high rating; provide details such as fast-paced, exciting, good characters (83%)
- Allow to access reviews directly from platform (75%)
- Quantify likelihood of enjoyment based on previous viewing habits or others with similar profiles (72%)
- Offer specific reasons for poor ratings, for example, boring (72%)
- Ability to link to reputable critics’ reviews directly from platform (68%)
Artificial Intelligence (AI) can help consumers find more content to fit their taste. AI can assist in content curation organizing content by themes. It can also use consumer insights to classify and target consumer segments to test recommendations. AI can look beyond genre categories and include a director, favorite actor, sub-sub-sub-genre, decade, special effects, costumes, etc. Personalization needs to be even more adaptive and include a temperature reading of mood. Content platforms should ask consumers how they feel. After all, 80% report their video content selection is often based on their daily mood.
Expanding the search function beyond individual platforms is also important to consumers. They not only want to know what to watch but where to watch. Helping consumers navigate the video content marketplace will assist the content selection process so that video gets found – and watched.