Gone are the days when a sports fan could locate their favorite team’s game quickly on a predictable outlet. Instead, broadcast contracts are divided among many media outlets, with sporting events appearing on dozens of broadcast, cable and regional sports networks, as well as streaming services. In fact, it’s gotten so tough that ESPN thinks that the biggest win for sports fans may just be having an easy way to figure out where an event is being offered in time to catch the opening kick-off, tip-off or puck drop.
Disney’s ESPN set out to solve the sports discovery problem with its new “Where to Watch” feature. Offered on its main app and website, the feature helps viewers instantly locate any sports event appearing on ESPN platforms and elsewhere, including cable and broadcast networks or streaming services. ESPN is aiming for this feature be comprehensive across the market, not just for ESPN and ABC properties, because the goal is to solve fan fragmentation and frustration.
Where to Watch, which debuted in August, showcases tens of thousands of events across dozens of leagues. Included are events from the NFL, NCAA football, NCAA men’s and women’s basketball, MLB, NHL, NBA, WNBA, NASCAR, UFC, F1, PGA Tour, MLS, tennis majors, Premier League, Champions League, and other live sports events that air on Disney’s ESPN platforms—with plans to grow.
We recently spoke with Casey Grabbe, senior director of ESPN Strategy, and Chris Jason, executive director of ESPN product management, on the development and objectives of this ambitious feature.
The feature aims to solve fragmentation
“Where to Watch is an easy-to-use guide for sports fans to locate any sports event on ESPN platforms and beyond. That includes broadcast, cable and regional sports networks and streaming services,” Jason explained. “From Where to Watch, fans can view all the sports events for an entire day, along with the network or service on which to find them, with quick one-click access to ESPN network streams for pay TV authenticated users and ESPN+ subscribers.”
Beyond just ESPN, fans are also linked directly to select partner networks, which currently include regional sports networks such as NESN and Monumental Sports, Jason said. Fans can search for events, filter, and customize the guide to prioritize their favorite teams and leagues.
“This makes for a fast and easy to discover what they care about most, all tied to their ESPN profile and personalization preferences,” Jason explained.
The motivation behind the Where to Watch feature was simple: reduce complexity.
Disney’s internal research found that sports fans are confused about where to find games, according to Grabbe. As sports viewing has become fragmented across many TV networks and streaming platforms, it has also become difficult and confusing for people to know where they can watch their favorite teams, players, and sports.
“We are hoping to solve that consumer pain point by creating a centralized home for sports viewing information with an intuitive interface that is easily accessible from within their daily routine of visiting ESPN.com and the ESPN App,” Grabbe explained.
How Where to Watch works
Where to Watch is designed to be a simple, scrollable, time-based guide of sports events, Jason said. It is powered by a proprietary event database, managed by the ESPN Stats and Analysis team.
The event database aggregates ESPN and partner data feeds along with originally sourced information and programming details from more than 250 media sources, including television networks and streaming platforms, Jason explained.
“We currently support coverage of tens of thousands of events across dozens of sports and leagues, and other live sporting events airing on ESPN platforms,” Jason said.
In order to watch an event, fans need only press boldly colored “watch” buttons on live game selections, which takes the viewer directly to the broadcast. That is, provided that they are a subscriber to ESPN+ or a pay-tv service. Fans can also customize the feature to highlight a specific sport or league.
Event-driven database drives discovery
Where to Watch is currently available for free to all ESPN App and ESPN.com users, which do not require a paid subscription. The feature employs an event database that was created by and is managed by the ESPN Stats and Information Group. The Stats group aggregates and analyzes data from ESPN and partner feeds. It combines that data with that of more than 250 other media sources. This includes television networks and streaming services. ESPN has a partnership arrangement in which it links users on the ESPN App directly to partner feeds to view content, in an effort to cut down on the friction of finding and assessing sports content.
Sports fans using the Where to Watch service see two primary features: A Favorites element and the Guide. If the fanhas a favorite team, sport or league they wish to watch, they can set that information into the feature and it will display upcoming games or events at the top of their screen. The viewer need only click on the event they want to be directed to. The viewer can personalize or change favorite settings at any time. Otherwise, the Guide feature will display all of the options available to watch at a given time on a given day.
Early feedback says Where to Watch is a winner
Jason notes that the Where to Watch feature was designed with the sports fan desires in mind, and that seems to have paid off so far.
“Fan feedback has been overwhelmingly positive, primarily in that this is focused on solving a real pain point for sports fans,” Grabbe said. “We see this sentiment reflected on social media, through various media outlets following launch, and ongoing interactions with sports fans. Several million fans have already used the feature, which is a really promising sign that this can become an indispensable utility going forward.”
Initial partnerships have been formed with only a few regional sports networks – NESN and Monumental Sports – to link fans directly with their programming, with plans to increase the number of these partnerships.
“We want ESPN to be a part of every sports fan’s daily routine,” Grabbe stressed. “Providing fans with this added functionality is helping to further strengthen ESPN’s position as the preeminent digital sports platform. We are always thinking about how we can put the sports fan’s needs first.” ESPN also plans to launch a new stand-alone direct-to-consumer product in 2025, and hopes to include its Where to Watch feature.
“Our near-term focus is to expand coverage across more sports events and leagues,” Jason said. “We are also working on adding additional utility within the experience, for example giving fans the ability to set reminder alerts for games they are interested in. In parallel we continue to monitor fan feedback to evaluate additional ways to improve the experience.”
These days, digital media companies are all trying to figure out how to best incorporate AI into their products, services and capabilities, via partnerships or by building their own. The goal is to gain a competitive edge as they tailor AI capabilities to their audiences, subscribers and clients’ specific needs.
By leveraging proprietary Large Language Models (LLMs) digital media companies have a new tool in their toolboxes. These offerings offer differentiation and added value, enhanced audience engagement and user experience. These proprietary LLMs also set them apart from companies that are opting for licensing partnerships with other LLMs, which offer more generalized knowledge bases and draw from a wide range of sources in terms of subject matter and quality.
A growing number of digital media companies are rolling out their own LLM-based generative AI features for search and data-based purposes to enhance user experience and create fine-tuned solutions. In addition to looking at several of the offerings media companies are bringing to market, we spoke to Dow Jones, Financial Times and Outside Inc. about the generative AI tools they’ve built and explore the strategies behind them.
Media companies fuel generative AI for better solutions
Digital media companies are harnessing the power of generative AI to unlock the full potential of their own – sometimes vast amounts – of proprietary information. These new products allow them to offer valuable, personalized, and accessible content to their audiences, subscribers, customers and clients.
Take for example, Bloomberg, which released a research paper in March detailing the development of its new large-scale generative AI model called BloombergGPT. The LLM was trained on a wide range of financial data to assist Bloomberg in improving existing financial natural language processing (NLP) tasks, such as sentiment analysis, named entity recognition, news classification, and question answering, among others. In addition, the tool will help Bloomberg customers organize the vast quantities of data available on the Bloomberg Terminal in ways that suit their specific needs.
Launched in beta June 4, Fortune partnered with Accenture to create a generative AI product called Fortune Analytics. The tool delivers ChatGPT-style responses based on 20 years of financial data from the Fortune 500 and Global 500 lists, as well as related articles, and helps customers build graphic visualizations.
Generative AI helps customers speed up processes
A deeper discussion of how digital media companies are using AI provides insights to help others understand the potential to leverage the technology for their own needs. Dow Jones, for example uses Generative AI for a platform that helps customers meet compliance requirements.
Dow Jones Risk Compliance is a global provider of risk and compliance solutions across banks and corporations which helps organizations perform checks on their counterparties. They do that from the perspective of complying with anti-money laundering regulation, anti-corruption regulation, looking to also mitigate supply chain risk and reputational issues. Dow Jones Risk Compliance provides tools that allow customers to search data sets and help manage regulatory and reputational risk.
In April, Dow Jones Risk & Compliance launched an AI-powered research platform for clients that enables organizations to build an investigative due diligence report covering multiple sources in as little as five minutes. Called Dow Jones Integrity Check, the research platform is a fully automated solution that goes beyond screening to identify risks and red flags from thousands of data sources.
The planning for Dow Jones Integrity Check goes back a few years, as the company sought to provide its customers with a quicker way to do due diligence on their counterparties, Joel Lange, executive Vice President and General Manager, Risk and Research at Dow Jones explained.
Lange said that Dow Jones effectively built a platform which automatically creates a report for customers on a person or company, using technology from AI firm Xapien. It brings together Dow Jones’ data that is plugged into other data sets, corporate registrar information, and wider web content. It then leverages the platform’s Generative AI capability to produce a piece of analysis or a report.
Dow Jones Risk & Compliance customers use their technology to make critical, often complex, business decisions. Often the data collection process can be incredibly time consuming, taking days if not weeks.
The new tool “provides investigations, teams, banks and corporations with initial due diligence. Essentially it’s a starting point for them to conduct their due diligence, effectively automating a lot of that data collection process,” according to Lange.
Lange points out that the compliance field is always in need of increased efficiency. However, it carries with it great risk to reputation. Dow Jones Integrity Check was designed to reshape compliance workflows, creating an additional layer of investigation that can be deployed at scale. “What we’re doing here is enabling them to more rapidly and efficiently aggregate, consolidate, and bring information to the fore, which they can then analyze and then take that investigation further to finalize an outcome,” Lange said.
Regardless of the quality of the generated results, most experts believe that it is important to have a human in the loop in order to maintain content accuracy, mitigate bias, and enhance the credibility of the content. Lange also said that it’s critical to have “that human in the loop to evaluate the information and then to make a decision in relation to the action that the customer wants to take.”
In recent months, digital media companies have been launching their own generative AI tools that allow users to ask questions in natural language and receive accurate and relevant results.
The Associated Press created Merlin, an AI-generated search tool that makes searching the AP archive more accurate. “Merlin pinpoints key moments in our videos to exact second and can be used for older archive material that lacks modern keywords or metadata,” explained AP Editor in Chief Julie Pace at The International Journalism Festival in Perugia in April.
Outside’s Scout: AI search with useful results
Chatbots have become a popular form of search. Originally pre-programmed and only able to answer select questions included in their programming, chatbots have evolved and increased engagement by providing a conversational interface. Used for everything from organizing schedules and news updates to customer service inquiries, Generative AI-based chatbots assist users in finding information more efficiently across a wide range of industries.
Much like The Guardian, The Washington Post, The New York Times and other digital media organizations that blocked OpenAI from using their content to power artificial intelligence, Outside CEO Robin Thurston explained that Outside Inc. wasn’t going to let third parties scrape their platforms to train LLM models.
Instead, they looked at leveraging their own content and data. “We had a lot of proprietary content that we felt was not easily accessible. It’s almost what I’d call the front page problem, which is you put something on the front page and then it kind of disappears into the ether,” Thurston said.
“We asked ourselves: How do we create something leveraging all this proprietary data? How do we leverage that in a way that really brings value to our user?” Thurston said. The answer was Scout, Outside Inc.’s AI search assistant. Scout is a custom-developed chatbot.
The company could see that generative AI offered a way to make that content accessible and even more useful to its readers. Outside had a lot of evergreen content that wasn’t adding value once it left the front page. Their brands inspire and inform audiences about outdoor adventures, new destinations and gear – a lot of which is evergreen and proprietary content that still had value if it could easily be surfaced by its audience. The chat interface allows their content to continue to be accessible to readers after it is no longer front and center on the website.
Scout gives users a summary answer to their question, leveraging Outside Inc’s proprietary data, and surfaces articles that it references. “It’s just a much more advanced search mechanism than our old tool was. Not only does it summarize, but it then returns the things that are most relevant,” he explained.
Additionally, Outside Inc’s old search function worked by each individual brand. Scout searches across the 20+ properties owned by the parent company which include Backpacker, Climbing, SKI Magazine, and Yoga Journal, among others. Scout brings all of the results together, from the 20+ different Outside brands, from the best camping destinations, to the best trails, outdoor activities for the family, gear, equipment and food all in one result.
One aspect that sets Outside Inc.’s model apart is their customer base, which differs from general news media customers. Outside’s customers engage in a different type of interaction, not just a quick transactional skim of a news story. “We have a bit of a different relationship in that they’re not only getting inspiration from us, which trip should I take? What gear should I buy? But then because of our portfolio, they’re kind of looking at what’s next,” Thurston said.
It was important to Thurston to use the LLM in a number of different ways, so Outside Inc launched a local newsletter initiative with the help of AI. “On Monday mornings we do a local running, cycling and outdoor newsletter that goes to people that sign up for it, and it uses that same LLM to pick what types of routes and content for that local newsletter that we’re now delivering in 64,000 ZIP codes in the U.S.”
Thurston said they had a team working on Scout and it took about six months. “Luckily, we had already built a lot of infrastructure in preparation for this in terms of how we were going to leverage our data. Even for something like traditional search, we were building a backend so that we could do that across the board. But this is obviously a much more complicated model that allows us to do it in a completely new way,” he said.
Connecting AI search to a real subscriber need
In late March, The Financial Times released its first generative AI feature for subscribers called Ask FT. Like Scout, the chat-based search tool allows users to ask any question and receive a response using FT content published over the last two decades. The feature is currently available to approximately 500 FT Professional subscribers. It is powered by the FT’s own internal search capabilities, combined with a third-party LLM.
The tool is designed to help users understand complicated issues or topics, like Ireland’s offshore energy policy, rather than just searching for specific information. Ask FT searches through Financial Times (FT) content, generates a summary and cites the sources.
“It works particularly well for people who are trying to understand quite complex issues that might have been going on over time or have lots of different elements,” explained Lindsey Jayne, the chief product officer of the Financial Times.
Jayne explained that they spend a lot of time understanding why people choose the FT and how they use it. People read the FT to understand the world around them, to have a deep background knowledge of emerging events and affairs. “With any kind of technology, it’s always important to look at how technology is evolving to see what it can do. But I think it’s really important to connect that back to a real need that your customers have, something they’re trying to get done. Otherwise it’s just tech for the sake of tech and people might play with it, but not stick with it,” she said.
Trusted sources and GenAI attribution
Solutions like those from Dow Jones, FT and Outside Inc. highlight the power of a brand with a trusted audience relationship to create deep, authentic relationships built on reliability and credibility. Trusted media brands are considered authoritative because their content is based on credible sources and facts, which ensures accuracy.
Currently, generative AI has demonstrated low accuracy and poses challenges to sourcing and attribution. Attribution is a central feature for digital media companies who roll out their own generative AI solutions. For Dow Jones compliance customers, attribution is critical to customers, to know if they’re going to make a decision based on information that is available in the media, according to Lange.
“They need to have that attributed to within the solution so that if it’s flowing into their audit trails or they have to present that in a court of law, or if they would need to present it to our internal audit, the attribution is really key. (Attribution) is going to be critical for a lot of the solutions that will come to market,” he said. “The attribution has to be there in order to rely on it for a compliance use case or really any other use case. You really need to know where that fact or that piece of information or data actually came from and be able to source it back to the underlying article.”
The Financial Times’ generative AI tool also offers attribution to FT articles in all of its answers. Ask FT pulls together lots of different source material, generates an answer, and attributes it to various FT articles. “What we ask the large language model to do is to read those segments of the articles and to turn them into a summary that explains the things you need to know and then to also cite them so that you have the opportunity to check it,” Jayne said.
They also ask the FT model to infer from people’s questions when it should be searching from. “Maybe you’re really interested in what’s happened in the last year or so, and we also get the model to reread the answer, reread all of the segments and check that, as kind of a guard against hallucination. You can never get rid of hallucination totally, but you can do lots to mitigate it.”
The Financial Times is also asking for feedback from the subscribers using the tool. “We’re literally reading all of the feedback to help understand what kinds of questions work, where it falls down, where it doesn’t, and who’s using it, why and when.”
Leaning into media strengths and adding a superpower
Generative AI seems to have created unlimited opportunities and also considerable challenges, questions and concerns. However it is clear that an asset many media companies possess is a deep reservoir of quality content and it is good for business to extract the most value from the investment in its creation. Leveraging their own content to train and program generative AI tools that serve readers seems like a very promising application.
In fact, generative AI can give trustworthy sources a bit of a super power. Jayne from the FT offered the example of scientists using the technology to read through hundreds of thousands of research papers and find patterns in a process that would otherwise take years to read in an effort to make important connections.
While scraped-content LLMs pose risks to authenticity, accuracy and attribution, proprietary learning models offer a promising alternative.
As Jayne put it, “The media has “an opportunity to harness what AI could mean for the user experience, what it could mean for journalism, in a way that’s very thoughtful, very clear and in line with our values and principles.” At the same time, she cautions that we shouldn’t be “getting overly excited because it’s not the answer to everything – even though we can’t escape the buzz at the moment.”
We are seeing many efforts bump up against the limits of what generative AI is able to do right now. However, media companies can avoid some of generative AI’s current pitfalls by employing the technology’s powerful language prediction, data processing and summarization capabilities while leaning into their own strengths of authenticity and accuracy.
Over the past few years, publishers have seen formerly reliable sources of traffic like Google and Facebook dry up, with no new platforms on the horizon to make up for that loss. Shifts in user behavior and changes in algorithms have left publishers scrambling to respond across both search and referral channels. And with the rise of generative AI-enabled search, the ability to find new sources of referral traffic and build a loyal audience has become even more critical for publishers.
To gain a better understanding of how traffic declines are affecting them and the steps they’re taking to address challenges, Arc XP partnered with Digiday to survey 115 publishers for the report The state of publisher traffic: Framing the evolution and impact of search and referral in 2024. We asked about the referral traffic trends they’re seeing, how those trends have impacted their revenue, and the steps they’re taking to either rebuild their traffic or find other ways to reach and grow their audience.
In this article, we’ll focus specifically on what we heard from publishers about their referral traffic from social media platforms (like Facebook and TikTok), news aggregators (like Apple News), and other third-party platforms (like Reddit).
Publisher referral traffic trends in 2023
Referral traffic is an important revenue driver for publishers, with 98% of survey respondents saying that referral traffic has a moderate or very significant impact on their annual revenue. But 2023 proved to be a challenging year for publisher referral traffic, with most survey respondents saying they experienced a 1% to 20% decline.
The publishers in our survey experienced traffic declines across the major social media channels. Respondents named Facebook as a channel where they expect continued declines (82%), followed by YouTube (67%) and TikTok (57%). Given Meta’s announcement that it will de-prioritize news content on its platforms, the decline in referral traffic from Facebook is not surprising. And across all social platforms, opaque changes to algorithms have made it difficult for publisher content to stand out among the vast array of options presented to users.
According to survey respondents, the primary ways referral traffic decline impacts their revenue are decreased advertising ROI (63%) and changes in collaborations with brands, influencers, or other publications (54%). They also cited a change in competitive positioning, change in quality of audience, and a decline in social media engagement (each named by 43% of respondents).
How publishers are responding to referral declines
When asked what challenges they face around responding to the trend of declines in referral traffic, 54% of respondents named building/maintaining strong relationships with external platforms as a challenge. This was followed by adapting to social media trends (52% of respondents). Accurately pinpointing referral sources (49%) and constantly changing algorithms and updates (46%) were also top challenges.
Despite these challenges, the publishers we surveyed are forging ahead with tactics to combat referral traffic decline, including experimenting with new forms of video content and increasing their presence across social channels. 81% of respondents said they are experimenting with live streams and long-form video content, and 70% said they are focusing on short-form original vertical video for TikTok, YouTube Shorts, and Instagram.
Long-form video content will ultimately offer publishers more control over monetization options than short-form videos created specifically for social channels. With long-form videos, publishers can incorporate in-depth reporting that sets them apart from other content sources and encourages deeper reader engagement and return visits.
The “pivot to video” isn’t new for publishers. Unfortunately, they’ve been burned before by making big bets on video content that didn’t pan out. This time around, publishers need to think carefully about what they want to accomplish with their video strategy: is it about getting advertising revenue from the videos? Driving readers from other channels to their website? Or creating longer-term audience relationships?
Surprisingly, only 56% of respondents said they are increasing direct-traffic efforts (newsletters, owned podcasts, etc.). Given the inherent unpredictability of social platforms, all publishers would benefit from thinking about how they can build more direct connections with their readers.
Publisher referral traffic expectations for 2024
When publishers look ahead to 2024, they are optimistic that referral traffic will rebound. Most of the publishers we surveyed expect referral traffic to increase by 1% to 20% this year, a trend that will likely driven by newsworthy events like the summer Olympics and the U.S. presidential election.
Publishers’ cautious optimism about 2024 might also reflect confidence in the tactics they’re implementing to combat referral traffic declines. But with platform changes and user behavior shifts it’s likely that referral traffic will never fully rebound to previous levels. Publishers will need to continue exploring ways to boost traffic across all channels, including owned channels that enable direct connections with readers.
Young streamers are more likely to gravitate to user-generated platforms like TikTok and YouTube for their entertainment needs over subscription-based products like Netflix and Max. Streaming services need to update their user experiences, particularly around findability and discoverability, to offer more-personalized content recommendations over generalized suggestions, according to the findings of a new report.
Deloitte’s “2024 Digital Media Trends” study found that 60% of Generation Z video consumers are more likely to watch user-generated content because they “don’t have time to spend searching for what they want to watch.” Half of all respondents say they “abandon an entertainment experience because they can’t find what they’re looking for.”
The findings are problematic for entertainment giants, which are committing large budgets toward the production of original content that aren’t attracting significant audiences. Deloitte says the top six subscription streaming platforms are likely to spend $100 billion on original content production and marketing in 2024 alone. Jeff Loucks, the Executive Director of the Deloitte Center for Technology, Media, and Telecommunications who co-authored the Digital Media Trends report, said that investment is likely to be wasted effort if streamers can’t easily connect to those shows and movies.
The discoverability dilemma
According to Loucks, one big reason why shows and movies aren’t being watched is because streaming platforms make it difficult for that content to be readily discovered, likening the experience of sifting through shows and movies on a streaming service to “the old days of Blockbuster.”
“You’re searching through a bunch of titles, and you can’t agree on what to watch. It’s going to take an hour and a half of your time,” Loucks said. “The content discovery has got to get better.”
Industry experts who spoke with Digital Content Next said they were largely unsurprised by Deloitte’s findings that streamers — particularly younger audiences — were increasingly turning to user-generated content platforms like TikTok and YouTube for their entertainment needs. One often overlooked reason is that platforms like YouTube and TikTok have made heavy investments in their search and discovery algorithms that identify what a person is watching on the regular, and then serve up more content that caters to their interests.
“YouTube and TikTok are scarily accurate and predictive and elemental, whether the content is large-scale or bite-sized,” said Tim Hanlon, the founder and CEO of the media consultancy firm Vertere Group. “Those are all independently describable and ascribable elements, data-rich elements that can be mixed and matched together.”
Loucks agrees that younger viewers are increasingly attracted to short-form content, which can be easily skipped for something new if it isn’t appealing. “Sometimes, people are telling us that they’ve got a lower attention span, and smaller, snackable content is something they’re willing to watch — it’s easier to consume,” Loucks said.
Addressing fragmentation
Embracing short form and the push-based UI of user-generated content platforms won’t serve as a silver bullet that will solve the complex challenges of streaming search and discoverability, however. There are still plenty of consumers who prefer to be entertained by watching feature-length films and episodic TV series — and they’re having the same challenges finding interesting things to watch across apps and platforms, too.
According to a report from Accenture, about 36% of people say they’re exhausted from having to constantly look across platforms and services to find something they want to watch. And 60% of consumers say they’ve churned out of a service because a movie or TV series was dropped, or they thought they’d watched everything there was to watch.
Fragmentation is accelerating these trends, because content that is relatable to a person is spread across different services. “The net impact of fragmentation is the fact that consumers can’t simply find content in consistent places where they want to spend their time,” pointed out Dallas Lawrence, a former communications executive for Roku’s platform.
Lawrence spent a lot of time thinking about this problem at Roku. He noticed that companies spend a lot of time and marketing money drawing customers into their streaming services only to “fail at the five-yard line.”
“They’ve failed to actually consumers with a piece of content they want to watch, and that’s probably one of the biggest challenges today, both from a streamer perspective — to keep people from cycling out — but also from a consumer perspective.”
Lawrence is now the chief strategy and communications officer for Telly, a startup that grabbed headlines last year after promising to offer a free, dual-screen smart TV. Telly packs a lot of features that are meant to entice consumer interest — from a premium screen to an integrated high-fidelity sound bar. They also promise to play nice with any streaming platform that a customer wants to use.
Telly is rooted in the idea that the TV will pay for itself over time through advertisements shown on a secondary screen that sits beneath the main display. Lawrence said Telly is uniquely positioned to help ease the challenges of streaming discovery for consumers and services alike because the device is able to evaluate what someone is watching across any service.
“If I’m watching Bridgerton on the top screen of my Telly, the device is recognizing that, and we’re going to say, hey, maybe you’d like to watch Gilded Age on Max as well,” Lawrence said. “We can throw that ad on the second screen, and when you’re done watching Bridgerton, you just pick up your remote and click into Gilded Age. The ability for us to recognize what someone is watching now, and then pull them into new content with a single click before they’ve turned off the TV or cycled out, that will have huge benefits.”
Streaming discoverability beyond the EPG
For now, Telly is the only dual-screen device on the market that can seamlessly pull off this experience. However, the idea of using viewing habits to deliver personalized results and improve streaming discoverability is not unique and can be franchised by other services.
Instead, streaming services seem to be defaulting to antiquated ways of browsing across content, complains former CBS executive Adam Wiener, who now operates his own media consulting firm Continuous Media.
Wiener says that subscription-based platforms have adopted the “endless scroll,” which allows consumers to quickly flip through movies and TV shows. Often, however, this approach fails offer personalized content recommendations the way user-generated platforms like TikTok and YouTube do. Free, ad-supported platforms and some premium pay TV services that deliver linear content are even worse, Wiener notes. That’s because they’ve embraced the grid-style electronic program guide (EPG) that was used by cable and satellite platforms for decades, but hasn’t kept up with the times.
“The problem with the EPG is that it’s the clunky thing of yesteryear, and it also doesn’t include all the things that you may be interested in,” Wiener opined. “An EPG should know that I never, ever watch reality shows, and it should never show those things to me.”
Endless scrolling and EPGs also reveal another problem: There is a lot of stuff to watch. According to Nielsen’s State of Play report, there are now more than 2.7 million unique titles across hundreds of streaming services, and the sheer volume of content libraries can leave consumers feeling extremely overwhelmed and make finding something to watch seem impossible.
“It’s the paradox of choice,” Wiener says. “The age-old discussion that it becomes tiring to scroll through a screen, the thought that maybe if I continue scrolling right, I might find something more interesting…and then it feels like you can’t choose, like you have to settle for something, and you just hope that it’s good.”
Hanlon agrees: “When everything is a choice, there’s a paralysis that occurs when you either revert to something you know from the past, or you’re looking for signals to grasp,” Hanlon said. “What services wind up having to do is dumb things down and simplify it to the point where it becomes pages and pages of tiles or, worse, a search box.”
AI leads the way for streaming discoverability
Wiener and Hanlon both point to generative artificial intelligence (AI) as a solution that can help ease a lot of the pain points associated with streaming search and discovery. At least one company is already embracing the idea: Earlier this month, Cineverse said it was working on a new AI-powered content recommendation engine called cineSearch.
Using metadata provided by Nielsen’s Gracenote and an AI platform powered by Google’s Gemini language model, cineSearch will power a forthcoming consumer chatbot called Ava that aims to offer personalized TV shows and movies across apps and services — even if the content isn’t offered by Cineverse itself.
“Our partnership with Gracenote increases the number of films and TV shows that are discoverable by users and allows us to offer cineSearch users the highest-quality title information with intensity rankings – when paired with a user’s viewing history, streaming service filters and content preferences – will help solve a major consumer issue and the leading cause of viewer frustration,” Tony Huidor, the Chief Technical Officer at Cineverse, said in a statement.
Deloitte’s Media Trends report suggests companies like Cineverse are on the right track. According to the report “streaming services should look to the engagement models of social media services to improve their own content delivery strategies, making a more concerted effort to leverage user data and AI technologies to target content toward individual viewers.” Loucks affirms that video platforms should want to embrace generative AI solutions to improve their content recommendation engines.
“Each service is going to have to work on having a user interface that is good and that works better, and I think generative AI is going to be a big part of that,” Loucks said.
Discoverability delivers streaming audiences
Audiences expect personalized experiences. According to a November 2023 report by Google Cloud, 81% of streaming video viewers “expect streaming services to provide highly personalized experiences,” and 31% will switch out of a service if they can’t find something they want to watch. Worse, nearly half of respondents say they’ve canceled a service in the past “if they couldn’t find something to watch,” Google Cloud revealed. Clearly improving streaming content discoverability is critical for success.
The experts and the data reveal common themes: Streamers don’t want to spend a lot of time trying to find something to watch. And if a video platform makes an investment in personalized search and discovery, that is where audiences — especially younger viewers — will spend their time.
From Google to Facebook and Instagram to TikTok (and so many more), publishers have spent the last couple of decades chasing their audiences from one platform to another—only to be betrayed by changing algorithms and shifting platform priorities. For years, popular wisdom held that you had to go where the audience is. Now, despite the fact that audiences (particularly younger ones) seek out news and information on social platforms, those platforms are “backing away” from making that content visible. But regardless of a media brand’s position on social media, search has remained the undisputed path to traffic.
Now, publishers face a whole new threat: generative AI search. Years of fine-tuning search engine optimization strategies may all be for naught as Google embraces AI-driven answers in lieu of links to relevant content. Meanwhile, Gartner predicts that traditional search engine volume will drop 25% by 2026 as users shift to AI chatbots and virtual agents for their answers.
The Wall Street Journal reports that publishers expect a 20% to 40% drop in their Google-generated traffic if the search giant rolls out its AI search tool to a broad audience. So, what are media executives supposed to do in the face of yet another shift in the technology landscape that threatens to put them on the outs once again? There’s really only one solution: devise a plan to regain control of their audience relationships once and for all.
Discovery: a problem as old as algorithms
AI search has yet to reach its full potential, but referral traffic is already taking a hit. AI-driven search results that fail to link to the content they scrape from is just one part of the problem. Searchers are often satisfied with AI “answers” and have little need to click through for more. And platforms from across the web are trying to keep more users within the walls of their gardens, and that means the likes of Facebook and Google have gone from partners in traffic acquisition to the opposition.
“We’re seeing an industry in real crisis,” says Jim Chisholm, a news media analyst. While Chisholm says he is not seeing evidence that AI is impacting traffic just yet, that does not mean publishers are not already feeling the squeeze from elsewhere.
Liam Andrew, Chief Product Officer at The Texas Tribune, says that while his team expects generative AI to impact search traffic, they are still waiting to see a substantial impact. The bigger problem facing the Tribune now is social media traffic or the lack of it.
While social platforms across the spectrum are pulling the rug out from under publishers, our old friend search is slowly changing the rules of the game. “Search is still working,” Andrew says. The Texas Tribune sees that explainers and guides still drive traffic and even subscriptions. However, other sites have not been so lucky.
Back in October 2023, Press Gazette found that of 70 leading publishers, half saw their search visibility scores drop—and 24 of those saw double-digit dips. That was the result of one update—more bad news is certain to follow as new updates make their way to the masses.
AI bots: To block or not to block
Publishers may be preparing for a more significant battle when it comes to traffic. However, right now, there’s another fight on their doorsteps: bots are crawling their sites and using their work to train the AI poised to steal their traffic. Some are already taking steps to stop the free—and possibly illegal—use of their content. The Reuters Institute found that 48% of the most widely used news websites across 10 countries blocked OpenAI’s crawlers by the close of 2023. Far fewer—just 24%—blocked Google’s AI crawler.
For Andrew and The Texas Tribune, blocking AI crawlers is not a major concern. They already have an open-republishing model and are used to seeing their content scraped and used on other sites (often without the requested attribution). “It improves our readership and impact, but we compete with ourselves for SEO,” he says. He also says they see versions of their stories on news sites where the content is entirely AI-written. However, it is “not affecting our core audience traffic,” according to Andrew. So — at least for now — The Texas Tribune is not planning to block the bots.
Meanwhile, Google is reportedly paying publishers to use its AI tools to write content. While in the short term, this may offer (smaller) publishers relatively small sums as well as an easier way to create low-lift content, like other Google News Initiative (GNI) projects, there’s an underlying concern that Google is not focused on publisher health in the long term.
Developing a direct-to-reader strategy
As Andrew and the product team at The Texas Tribune look toward a less search-dependent future, they are changing strategies. For 2025 and beyond, “we are not going to be focusing on a really good SERP [Search Engine Results Pages] unnecessarily,” Andrew says. Instead, they’ll focus on products built directly for readers.
“Newsletters have been part of our model for over 10 years. It’s nothing new, but we’re continuing to see success with it,” Andrew says. Not only do the newsletters still drive traffic, but they also drive conversions. Subscribers become members at a higher rate, vital to a publication that does not depend on paywalls for revenue.
DCN conducted an informal survey on concerns around the impact of AI search on traffic, and while the sample size may not hold up to scientific scrutiny, it was clear that newsletters are a crucial tactic for publishers looking to own their audiences. Other stats suggest this is a good move. Storydoc research found that 90% of Americans subscribe to at least one newsletter. That number goes up for younger audiences as 95% of Gen Z, Millennials, and even Gen X receive newsletters, compared to 84% of Baby Boomers.
Experiment with engagement approaches
The solutions to the Google problem don’t end at email, though.
“We also have a big robust event system,” Andrew notes. The Texas Tribune holds dozens every year. They range from “pre-gaming the Texas primary” to deep dives into transportation in the Austin/San Antonio area. They gather experts and pundits to share their expertise on topics that interest readers. The team also live-streams these events — a universally important tactic for engaging younger, more diverse audiences. These events also turn out to be effective for converting casual readers into subscribers and members.
Andrew alluded to products his team is working on that are still under wraps. Still, it’s clear that, like many publishers, The Texas Tribune is preparing for a future when search no longer drives most traffic.
Chisholm thinks mobile apps are another excellent direct-to-reader strategy, and research backs this up. Pew reports that “A large majority of U.S. adults (86%) say they often or sometimes get news from a smartphone, computer or tablet, including 56% who say they do so often. This is more than the 49% who said they often got news from digital devices in 2022 and the 51% of those who said the same in 2021.” Cultivating a relationship with readers through their mobile devices—where you can use push notifications and other native capabilities to grab their attention—will likely be one of the many tools publishers must deploy going forward.
“I’ve been in the news industry – which I love – for 48 years. Now we are at a crossroads,” says Chisholm. “Either we choose the road to recovery, rebuilding relationships with our readers, or we continue down the road we are on, subject to algorithms, more confusion between legitimate news and social media infested with AI nonsense.”
Google has issues. Lots of them. Unfortunately for our industry, Google’s issues are our issues too.
Last week, I observed a strange turn of events when an eye-opening HouseFresh blog post titled, “How Google is killing independent sites like ours” quickly evolved into a bizarre critique on the quality of work by and trust for premium publishers. While that may be a subject of debate, one thing we know for sure is that Google’s search experience often puts profits ahead of surfacing quality information—from small sites, independents, or anyone else that isn’t delivering dollars to its bottom line.
Mind you, this conversation was happening in the very same week in which Google’s outsized role in shaping public perception was on display as their newest product was found to be generating racially diverse Nazis. (Yes, you read that right). Was Google trying to rewrite history? Or just further demonstrating that its product and profit priorities can quickly get dangerously ahead of its historically unique and dominant role in determining how information and value gets distributed to the world.
Anti-trust me
Meanwhile, a little-known collective comprised of the United States Department of Justice and nearly every state attorney general was filing its closing briefing for its antitrust trial against – you guessed it – Google. Suffice it to say that this monumental case didn’t get nearly the column inches it deserved despite damning evidence. There’s something almost poignant about an industry undermined by Google for decades lacking the resources, or public interest, to cover a case that rivals other once-in-a-century trials on par with standard oil (likely surpassing AT&T and Microsoft) in its impact on the free market and competition.
Google has monopoly power in general search services and the advertising that comes with it.
Google has systematically captured more than half of this market by paying roughly $30 billion per year for exclusivity on devices (Apple and Samsung) and browsers (e.g. Firefox and Safari). I left out the most popular operating system and browser, Android and Chrome, as Google gets them for free because it owns them.
The result of this monopoly power is 95% market share on mobile and orders of magnitude advantage in data (aka “signals”) allowing Google to maintain its market dominance through superior data for ranking, local queries, and new queries, along with the all-important ads. And finally substantial advantages in emerging markets like artificial intelligence.
A case of convoluted logic
What does Google say about this in its own defense? Well, each of their claims sound weak to those of us who have been around the block a few times in the digital media world. I’ll lay them out here with my own redirect:
Google spends its first 15 pages arguing it competes in search with Amazon, TikTok, Facebook, Yelp and the list goes on. Have you ever searched Amazon for a local restaurant? Have you ever searched Facebook for a new television? Have you ever searched Yelp for a BBC report? This attempt to throw out the entire definition of general search is a waste of ink. (But, hey: maybe they used Gemini to formulate it.)
The next chapter from Google is an argument that, even if they accept the market of “general search,” they still don’t have monopoly power in it. To the contrary, a good deal of internal evidence demonstratesGoogle’s ability to raise prices, lower privacy and weaken consumer experience without missing a beat. An experience utterly degraded, particularly on mobile where it is clearly pay to play and self-preferencing of Google’s own products.
Finally, Google would like you to believe that all its deals with devices and browsers are not ‘exclusive’ as they merely make Google the default search engine. This may be left to the Court on semantics. But again there are, in evidence, internal decks from Google regarding the power of defaults that explain their significance to maintaining market share along with a damning letter sent to Microsoft claiming it would “deprive consumers of a competitive choice in search” by making Microsoft the default search. That litigation threat was sent by – surprise – Google.
Google is not your friend
I now return to my central point: Google puts the enemy in frenemy. No doubt they are the most significant business partner for nearly every publisher on the planet. They often send the largest monthly revenue checks from their near ubiquitous role in distribution and monetization of the web.
But as it relates to how they shape its design, the tools we use, the incentives it proffers, and how the welfare gets divided, they are certainly not the friend of media. Nor are they a friend to consumers who seek out quality information and would benefit from an open competitive marketplace.
Next up, we’ll see the UK Competition and Markets authority flex as it has closely examined Google’s plan for third-party cookie deprecation. The CMA insightfully notes that maybe the solution isn’t further delaying Google’s cookie deprecation – consumers don’t want to be tracked and surveilled – but instead putting limitations on Google’s abuses. A M E N.
Stateside, Google next faces the US Department of Justice in an adtech lawsuit set to go to trial on September 9th, 2024. The exhibits, depositions and evidence in this case will shine a light on every aspect of how our modern media economy is designed, distributed and dominated by a company that just surpassed $300 billion in revenues. To give that number some relative scale: Google earns the annual revenue of the largest news organizations (be it CNN, The New York Times, FOX News) in the world in just three days.
Google’s money-making results
Of course, none of this is to suggest that media endeavors aren’t in the business of making money, if only to support the work of journalists as in the case of non-profit models. Yet again, this week, we learn that Google is reportedly paying “smaller publishers” to use its AI tools. As with other Google News Initiative projects, this one is cloaked in the veneer of helpful benefactor here to support the creation of news.
Putting the relatively small sums involved aside, publishers are wise to remember that Google is not a friend. There is little doubt that this scheme is intended to benefit Google far more than any of the publishers involved. We may not see it yet, but if the evidence coming to light in the current spate of antitrust trials suggests anything, it is that we can look forward to the revelation of internal documents demonstrating the self (ish) motivation driving this organization.
Even as we explore the discussion around HouseFresh’s revelations about the questionable quality around the search results for product reviews we can’t forget that the root cause is Google’s profit-first approach. The engine that drives our traffic was really only built to drive revenue for one company. Results that favor commerce favor Google.
So, as the media allocates resources for courtroom coverage, let’s not forget how high the stakes are here. This goes far beyond the health of the media industry, or the chilling effects of monopolistic power on innovation. Ultimately, it is the marketplace of ideas, the value exchange of information, and the ability to surface essential news and entertainment that suffer in a system so profoundly gamed by one player.
The Associated Press (AP) has launched an innovative artificial intelligence (AI)-powered search experience, which it says will enhance the efficiency and accuracy of content discovery for AP Newsroom users.
The global news agency believes that the new search functionality represents a significant shift in the discoverability of photos and videos that align with users’ search criteria. Unlike metadata-based searches, the AI-powered tool is able to understand descriptive language and generates search results based on the user’s description. It can also identify specific moments within an entire video clip, regardless of its length.
This opens up new possibilities for users, allowing them to easily pinpoint precise moments of interest in videos, even if they haven’t been tagged or captioned.
Dramatically improved results
Traditional searches for visual content depend entirely on descriptive information, which means many visual assets are difficult to find.
“AP’s visual archives stretch back to the beginnings of photography, comprising tens of millions of photos and videos” explains Paul Caluori, the AP vice president of global products. “Across that history and volume, the amount and kind of descriptive information varies widely, and sometimes information is not as complete as we’d like it to be.”
However, the AP’s new AI-enabled search is able to recognize elements within photos and videos and “understand” more specific search terms and concepts to find those elements.
“This yields some dramatically improved search results and we are very excited to make this available to our customers,” says Caluori. “We believe it will speed their searching time, get them more useful results and ultimately help them be more successful with their projects.”
The search engine driving the new technology is NOMAD™, developed by MerlinOne, a software company specializing in AI applications for visual objects. NOMAD™ is the result of four years of in-house advanced AI effort, and the developers say it is the first such tool that understands natural language and concepts.
AI pioneer
This is far from the first time AP has used AI. It was one of the pioneering news organizations to leverage this technology back in 2014 and issued a guide to using AI in the newsroom in 2017. At the AP, the Business News desk took the lead in the use of AI to automate articles covering corporate earnings and sports. This undertaking not only enabled the brand to to experiment with new projects, it positioned AP as a thought leader in the AI space, inspiring other news organizations to embrace the technology.
So, while generative AI has captured much of the limelight since ChatGPT’s 2022 introduction, NOMAD™ is a purely visual AI search technology that builds upon the APs long standing use of AI.
Addressing common concerns
With AI moving so quickly, it is easy to anticipate that this technology will become the new normal for other agencies in the future. But what of the negative impact of AI-powered search engines, in terms of job losses? As media mogul Barry Diller recently told Time magazine, artificial intelligence (AI) could be as “destructive” to news publishers as free online news was in the early aughts.
However, unlike the threat of Generative AI, Caluori states that when it comes to their new search engine, “nobody is losing a job over this.”
“Our journalists search our archives every day and this tool will make it easier for them; it improves the usefulness of our archives to people both inside and outside the AP.”
Another concern around the use of AI is algorithmic bias. Consumer Reports has just released a series of videos, called BAD INPUT, in partnership with the Kapor Foundation which explore the biases present in algorithms and data sets, and the harm they can cause, particularly within communities of color.
David Tenenbaum, CEO of MerlinOne, has led their machine learning efforts for the last seven years. He explains that issues of bias center around the content sets used to train the model. If they are small and exclude crucial data (in this case faces representing all genders and races in equal proportions) bias is certain. On the other hand, if you use a huge training set, reflective of our world, the probability of bias is greatly diminished.
“Recognizing the danger that bias can creep into any system that uses a small, unbalanced training set, NOMAD was trained on over a billion images in a collection that is highly reflective of the world we all live in,” says Tenebaum. “No AI system will ever be perfect, but with millions of searches done, we have not had a single complaint about biased results.”
New normal
AP initially launched the service in a beta mode on its self-serve site, for customers who have access to its ecommerce service for photos and video. The AI search facility is now being rolled out to all customers over the next couple of weeks.
With an ever-expanding archive that adds new content every minute from around the globe, AP’s visual collection consists of over 60 million photos, catering to the needs of professional image buyers and commercial customers alike. The enhanced search tool is designed to make much more of this content discoverable. With NOMAD™ for Video, editors can find a short clip in just a few seconds, freeing up time to focus on high-impact journalism.
Additionally, new ecommerce capabilities improve the AP Newsroom experience for non-subscription customers. With streamlined self-serve licensing and pricing models, users can easily license images and video, including for ad hoc usage.
Caluori described the AI-powered search as a “real sea change” in content exploration and discovery within the AP’s vast archives.
“Want to see a fragment of Winston Churchill in a garden feeding birds? You can find something that specific using natural language, rather than typical one or two keyword searches that yield many unuseful results. The ability for customers to find very specific moments within a video or just the right photo is a powerful key to our archives.”
Search is a topic media companies often overlook. Most of us associate the word search with search engines like Google/Bing/DuckDuckGo. These organic channels are often how visitors (and at times internal team members), will search a content catalog. But it’s time to give some serious thought to your internal, on-page search.
There are many reasons to optimize internal search such as:
The way in which it reveals clear ROI as it complements social media and external search.
The way that it helps clarify user intent, which informs you about navigational issues and content needs.
How it allows you to reveal the depth of your catalog by exposing visitors to more content
The fact that optimized search empowers visitors to find solutions to their problems, meaning they are happier overall.
It empowers journalists to discover content on your owned channels as opposed to external ones.
The good news is that creating optimized site search may be easier than you think.
7 tips to achieve a best-in-class search and discovery experience
Tip 1 : Know your user’s intent
Your goal may be for users to consume content. However, before building the ideal path to that content, you must clarify their intent:
Are they looking to find a specific piece of content e.g. “yesterday’s premier league score”?
Are they researching a specific topic or theme e.g “eco-friendly lifestyle”?
Or are they looking for inspiration? Catching up on news?
Each user’s intent(s) are solved with different discovery patterns: search, guided discovery, or recommendations. It’s critical that you identify what your specific user’s intent and motivations are. Make sure that you spend time mapping this out, to then serve each user individually.
Tip 2 : Audit your content catalog
How many long-lasting pieces of content do you have vs. short lived items?
Among your live pieces of content, what percentage of content is actually being consumed today?
Are there opportunities to expose more content, perhaps resurfacing historical archives or adding in new partner content?
These types of questions will help you to define priorities for your discovery strategy.
On top of that, the quality of your metadata (date of publication, theme, topic, etc.) is crucial to ensure a good user experience. Be clear on the attributes that will determine how your content ranks when queried. Think about what uniquely differentiates your content catalog such as freshness, particular niches, short or snappy content, exclusivity, etc.
Tip 3: Identify your priorities, KPIs, and North Star metric
In order to build a great search and discovery experience, you need to be clear on your priorities and key metrics. Perhaps that’s to increase time spent, increasing engagement to support an ads-based model. Or it might be to increase premium subscriptions.
It’s not uncommon for media companies to run on several business models: ad-based, subscription-based, and even ecommerce. Also, priorities, goals, and primary metrics may shift and change over time. Common video industry on-demand models include AVOD (advertising-based video on-demand), SVOD (subscription video-on-demand), and TVOD (transactional video on-demand). Identifying your primary model(s) and goal(s) is critical to building great user experiences to achieve those goals.
To achieve your goals, consider:
engagement and discovery patterns like related content recommendations, or topic refinement with suggested tags.
building content discovery widgets that provide a glimpse of your content catalog from third-parties and partner websites.
personalized recommendations and other ways to engage loyal subscribers. Help them discover new content and gain more value from your platform.
Tip 4 : Build your discovery map
After identifying your goals and core metrics, you should then build a discovery map that reflects your objectives and specific needs. Here is a template and example to use.
On the X axis: describe your different content types: Fresh news and short reads, Reports and long-form, archives, niche content, etc.
On the Y axis : your various user’s or persona’s intents
In each content type box of this matrix, you then describe a “Discovery scenario”. For example, what is the preferable touchpoint (e.g “Search box” or “Discovery tab” or “Home Page”), or what is the most important ranking criteria for your content (e.g. “date of publication” and “topic”), and/or what is the CTA that compliments your North Star metric (e.g “read another article” or “sign in”)
Tip 5 : Evaluate your existing search and discovery
Next, audit your existing setup. Starting by evaluating your various discovery scenarios and note their pros and cons.
Below are examples of other items to evaluate throughout your audit. How do you manage:
typos? Ex: “I want to watch lalalnd”
broad queries? Ex: “I want to watch romantic comedies”?
natural language queries? Ex: “I want to watch Rom Coms”?
There are many more. Remember: The better you analyze your existing search & discovery shortcomings and opportunities, the better you can move faster on optimizing them.
Tip 6 : Find the right balance between AI-led and human-led curation
Curation strategies vary a lot across the media industry. While publishers often rely heavily on editorial teams, video platforms are often algorithmically curated. There is no right or wrong way to do this; finding your balance is key.
AI, for example, can be a way to surface what you have outlined in your content discovery map. Among the discovery scenarios that you have considered, think about how AI can help augment your team’s work. It can bridge gaps or free up editorial time. Finding this balance allows for increased efficiency and a focus on quality.
There are many different ways to leverage AI, here are a few. It can:
entirely power content blocks or rows leveraging various recommendation models
be used on top of manually curated blocks to dynamically reorder content, depending on their popularity
shorten the path to content by leveraging intent detection, and displaying personalized suggestions of content or categories
Tip 7 : Select the right solution for you
After following the tips outlined throughout this, you will be in a better position to select the right solution for your business and team. Your implementation may consist of building your own search and recommendation engine. It might consist of building from external platforms that are made for developers. Or perhaps you’ll buy off-the-shelf solutions.
In making these decisions, here are a few more considerations that are important at that stage.
Think through your unique requirements in terms of short- and long-term scalability. Not all solutions are equal in terms of a geographical footprint, expansion, and the ability to manage audience peaks, for example.
Similarly, understand your team’s unique situation when looking at how architectures and services selected will be built and maintained. If building things out in-house looks to be your best decision, consider what it takes to maintain, scale, and handle regular change requests and develop features and iterations.
Think about the future of discovery: What you have defined today in regards to your discovery map will likely evolve and change as quickly as consumer’s behaviors do. Consider a solution that will be future-proof, enabling you to consistently offer the most enjoyable and rewarding experience for your customers (and teams).
Our hope is that these tips will help you create the most optimized experiences for both your customers and your teams. Best of luck in planning, auditing, and creating your unique search and discovery experience. It’s worth it because effective search and discovery will help engage your site visitors and convert them into fans for the long-term.
Twenty years ago, search engines put in motion a revolution in how we access and consume information. In the span of two decades, billions of people across the globe now have a world of information at their fingertips. However, this came with a tradeoff. In exchange for search engines providing information, advertisements appeared on the search page. In the beginning, consumers didn’t mind when they were occasionally shown an unobtrusive ad along with organic results.
Ad supported search
A lot has changed since then. As traditional search engines grew, they faced the daily pressure of returning value to their shareholders by prioritizing advertisers and ad revenue. This has had several unintended consequences. These include an ever-increasing ad-load, ad driven content that can be misleading and harmful, and company practices that value profit over user privacy.
Furthermore, with more ads on top of the search results, the information consumers are searching for gets pushed down and deprioritized. It’s the difference between searching for flu symptoms and being shown an ad for cough syrup rather than a medical article factually outlining the symptoms. Then that ad for cough syrup will follow consumers across the Internet for weeks. And in a worst case scenario, the consumer might be shown an ad containing misleading information about the flu or flu treatments.
Antitrust-worthy
These problems are further exacerbated by the fact that there is a lack of competition in the search space. With one company controlling more than 90% of the market, consumers have few viable alternatives and entrepreneurs may be hesitant to enter the space. The lack of competition means fewer incentives for traditional search engines to spend the resources and time creating innovative features that better serve consumers.
Moreover, any industry where one company has outsized control is generally bad for society. This is even more relevant when that industry is responsible for how billions of people gain access to knowledge and information.
These are certainly not unique problems faced by search. Many industries rely on ad-supported models to drive revenue. And many industries, such as healthcare and agriculture, are witnessing consolidation on a scale unseen since the early 1900s.
History and experience have shown that monopolies are bad for innovation and bad for consumers. Society and our democracy are better served when you have more choice in the products and services you use. More choice and market competition leads to a better experience for consumers. With more competition and opportunity in the market, there’s greater chance for new companies to create innovative products and compete with the existing leading platforms.
Opportunities for innovation
Alternative models not only offer choice, they foster creativity — in the tool, yes. But also in the underlying business model. For example, one of the core tenets with an ad model is to keep individuals on the platform for as long as possible to increase the visibility of ads and the likelihood they will click on those ads. In reality the sole purpose of a search engine should be to best answer a user’s query as quickly as possible. Whether that person remains on the site for one second or one hour should be independent from the need to satisfy advertisers. The two goals could not be farther apart, yet, that is what the last 20 years of search has delivered.
When the search business no longer relies on advertisers for revenue, it gets the freedom to dream up new ideas and build the type of search experience that couldn’t exist before. This means giving more choice to consumers on the preferences for news outlets or filters that enable a shopper to search for only small retailers or companies that ethically source materials. Instead of showing an entire page of results, the right answer with an infographic could be shown in the search bar allowing a user to start their journey without ever landing on the search page.
Not only do alternative models have significant impacts on a consumer’s overall experience, they can have major implications for privacy. Tracking personal data is at the core of today’s digital advertising ecosystem. However, without ads, strong privacy policies and user protections can be core features of the product rather than a business tradeoff.
Experience matters
Consumers are increasingly aware of the way companies exploit their personal data and they want alternatives. According to a Pew survey, nearly 80% of Americans are concerned about how much data is collected about them by companies. Moreover, Last year, 52% of Americans said they had decided not to use a product or service because they were worried about how much personal information would be collected about them. In a separate McKinsey study, nearly 70% of consumer respondents were concerned about privacy with search.
We know that competition leads to more creativity, more choice, and ultimately a better experience for the consumer. Alternative approaches to traditional business models such as an ads free, private search, shouldn’t be the exception to the rule, they should be the norm and we should continue to demand a level playing field to let innovation thrive.
About the author
Sridhar Ramaswamy is the co-founder and CEO of Neeva, the world’s first ad-free, private subscription search. He formerly oversaw all of Google’s Advertising products, which included search, display and video advertising, analytics, shopping, payments, and travel.
Content providers have always known that word of mouth can work wonders. Now, they have to apply that principle by speaking up—literally—in order to reach more consumers via voice search.
Voice searches already account for about 20% of mobile queries. By next year half of all online searches will be performed via voice. Yet only 4% of businesses surveyed regard themselves as voice-search ready. Publishers who are behind this important tech curve risk being silenced by rivals intent on commanding the conversation.
The talk on the
street
“It’s no surprise that voice as an interface for technology and information is taking off so quickly,” says Rachel Reed, senior innovation manager for food, lifestyle and entertainment company Meredith Corporation. “As consumers, we are drawn to technology that understands us, empathizes with us, and can ultimately predict our needs and wants. As publishers, voice paves the way for us to create more personalized and meaningful connections with our audiences.”
Bryan Osima, software engineer and CEO of Uvietech Software Solutions, says people are relying more on voice search for three primary reasons: convenience, speed, and accuracy. “We are bombarded with tons of information every day and we have shorter attention spans. So, the speed of getting the information we need is paramount,” he says. “The primary devices for that today are smartphones, smart speakers, and wearables with built-in smart home assistants like Alexa and Google Assistant.”
Publishers who choose not to optimize for voice search are leaving a lot of traffic on the table, Greg Secrist, cofounder of BKA Content, believes. “This area is especially beneficial for news organizations and other content providers who want to increase their chances of their content being found and consumed by readers,” says Secrist.
Real world success
stories
Reed can attest to how crucial voice
search is becoming for content companies. For years, Meredith has employed a
predictive framework that enables its brands to dynamically serve content to
targeted users. Now, they’re extending this personalization and predictive
framework to voice to make informed predictions about and deliver useful voice
content to individual users.
“We employ a two-pronged approach. First,
we’ve implemented voice search optimization to ensure our content surfaces in
response to general user queries. We are seeking to own the phrase, not just
the keyword, by providing shorter responses to longer queries and implementing
concise headlines and summaries,” notes Reed. “Second, we are building
data-informed voice-first experiences designed for different platforms. This
involves identifying the most meaningful use cases for voice and launching
interactive experiences to address what we see as unmet needs in each space.”
Currently, Meredith is focused on voice search categories related to health and wellness, entertainment, and food. It recently launched two skills: Balance by Health, which offers daily wellness inspiration and motivation tips; and Entertainment Weekly’s The Must List, which provides an exclusive look at the top movies, TV shows, and movies that its editors recommend.
Secrist, whose business writes and packages customized content for clients, has also been working hard to ensure his team’s compositions are properly optimized for voice. “We use conversational language and semantics to make sure our content is compatible with voice search. Thinking about the way a customer would use their voice to access certain information is important when creating voice search-friendly content,” says Secrist.
Best practices for
voice search
To improve voice search optimization (VSO)
results, Osima recommends the following best practices:
Analyze all content carefully and think about as many contexts and questions users could have about that piece of content.
Provide concise yet comprehensive answers to those contextualized questions that could be returned in a voice search result.
Structure the content as clearly as possible with logical headings and sections that explicitly detail what each section is focused on. Content found in a frequently asked questions page often get the best returns on voice search. While you can’t make every page on your site FAQ, you can provide a similar structure: Employ a heading that’s descriptive and which could be a contextualized question or query a user would have, and then provide the answer immediately after.
Use structured metadata, rich snippets, and other tags made primarily for search engines to ensure that they precisely understand what your content is about.
Make sure your site and pages load very quickly.
Increase backlinks to your site to improve your domain authority. Search engines want to be sure that, as much as possible, they are returning results from very credible sources. Your overall domain authority is the biggest indicator of that.
Pay attention to semantics by targeting long-tail keywords related to the subject matter. For example, a consumer may be searching for “pants,” while others might refer to them as “trousers”; be sure your content addresses both terms.
Keep
it simple for success
However, “skip buzzwords, as they don’t work in voice search. Drill down into the most generic keywords your business uses to attract an audience,” suggests Andrew Schrage, CEO of the popular personal finance blog Money Crashers. “Remember that voice search inherently involves brevity. So, you want to strip things down and find out what customers really want to know about your business in as few words as possible.”
Also, aim for content that reads in a conversational manner. That means ensuring a high Flesh-Kincaid readability score – 70 or higher is ideal.
“Having
simple answers to questions is important, as voice search is looking for the
simplest way to answer the users question. If your content has lengthy
paragraphs answering something, it is not voice search friendly,” says Secrist.
Lastly,
keep your eyes on the prize: Make a strong effort to identify the value and utility
your brand provides and choose a unique way to deliver that utility via voice.
“Give
users a reason to come back,” Reed advises. “Test, learn, listen to feedback,
and continue to optimize.”
With the plethora of media choices, how do consumers choose their content? There are several opinions as to how we determine if a piece of content is worthy of our attention. Clay Johnson, author of The Information Diet, advises people to include content from sources outside of their personal interests and opinions. Eli Pariser, chief executive of Upworthy, agrees that people need to get outs of their “filter bubble.”
However, most searches and social feeds are based on algorithms that select the information a user wants to see based on their user information (profile). In new research from the Knight Foundation, The Filter Map: Media and the Pursuit of Truth and Legitimacy, Deen Freelon introduces three criteria to help consumers determine which content will be most valuable to them.
The filter map criteria:
Agreeableness – aligning with your preexisting opinions with the content.
Truth value – determining whether a given message is true or false.
Legitimacy – when its genuinely safe to assume an opinion is considered acceptable.
Agreeableness
Generally, people seek content that fits their interests, often opting for information streams that are low on disagreeableness. Consumers tend to seek content that fits their interests and filter out information that does not. Sometimes peoples’ filters, through no fault of their own, allow disagreeable information to confront them. At that point, they need to assess the information and chose to accept or not. If they accept, they may, even if only slightly, allow new information to influence their thinking.
True Value
True value is about the fundamental faith in realism. It’s about determining what is and isn’t true by observing the world. Unfortunately, it is not as easy as it sounds. Today, some believe what is disagree with, is false. On the contrary, disagreeing with information does not mean the information is false. Agreeability and disagreeability have little to do with whether something is true or not. Understanding this important distinction is critical to the filter criteria.
Legitimacy
Legitimate opinions generally align with moral principles that are preserved across a broad array of societies, governments, philosophies, and religions. Content legitimacy, like true value, does not to include agreeability. Agreeableness is essentially a subjective characteristic; what is agreeable to one person may or may not be to another person. Legitimacy, on the other hand, is intersubjective. It receives its status from moral values shared across governments, cultures, religions, and philosophies. It’s not uncommon for opinions to be both be agreeable and illegitimate or disagreeable and legitimate views.
It’s important to incorporate content filters, especially, when engaging with social media platforms. Freelon’s filtering criterion allows for both agreeing and disagreeing with legitimate opinions. He readily admits that implementing such a system would be complex, particularly given the individual variations in his criteria. However, he believes that we can build a system that delivers true and legitimate content from across our personal agreement spectrum.
Consumers are swamped with video content options. New services continue to emerge in a fragmented marketplace of distribution platforms. It’s overwhelming for consumers and, as a result, much video content is left undiscovered and unwatched. Today’s video publishers need to do more than create (or acquire) must-see content. The need to attract and engage consumers and provide a return on investment to marketers.
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.
The Influencers
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.
Discovery
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.
Get Personal
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%)
Get Smart
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.
