video programmers complain OTT platforms aren’t sharing enough audience data

Video programmers that are going direct to consumer by launching streaming apps are grappling with third-party distributors that have access to huge audiences, but offer very little in the way of audience data.

Today, video app programmers have a growing number of third-party distribution options. Marketplaces such as the iTunes App Store and Google Play ensure that their apps are available on almost every smartphone and tablet. Streaming video devices such as Amazon’s Fire TV, Roku and Apple TV offer similar app distribution on TV screens. Amazon and Roku are also channel resellers, which allows them to sell media companies’ apps as “channels” inside their own branded apps. (Apple is reportedly working on a similar product for its TV app.)

All of these distributors, whether it’s an app marketplace or channel reseller, allow users to subscribe through the distributor’s ecosystem (and billing system) instead of having to sign up directly with the programmer. This has created a challenge for video programmers, which need to be on third-party distribution platforms in order to reach a wider user base, but get a limited view into who is actually subscribing and how they are using the apps.

For instance, video programmers that agree to be on Amazon Prime Video Channels and The Roku Channel do not receive the email or credit-card information of those that have elected to subscribe. That information remains with Amazon and Roku, which also take a share of the revenue generated by their wholesale marketplaces. Last year, Amazon expanded its channels program to include ad-supported channels, which meant that the company does deliver back some anonymized user data on viewership trends and age and gender demographics, sources said. But again, there is no identifiable information that programmers could put to use outside of Amazon’s own ecosystem.

“Amazon wants you to do well on ads, because they want people to come back and buy stuff on Amazon, so they’ll give you some anonymized data,” said an executive at a TV programmer that distributes on Prime Video Channels.

“Amazon Channels and the wholesalers, in general, are basically a black box; you don’t really see what’s happening in terms of what people are watching,” added an exec at a niche subscription video programmer.

And yet, the potential to find new audiences in both Amazon and Roku’s customer base — Amazon’s Fire TV platform alone has 30 million active users and Roku has 27 million active users — makes both distributors difficult to ignore. It’s a big reason why Amazon Prime Video Channels has become a significant driver of subscription revenue for OTT programmers, accounting for anywhere from 25 percent to more than 50 percent of a subscription streaming app’s total number of users. (The fact that both Amazon and Roku also supply the technical infrastructure and handle billing services can be another benefit for programmers that don’t have the resources to handle both.)

The data challenge with OTT wholesalers also varies by programmer. For instance, ad-free subscription programmers such as HBO, Showtime and Starz are not as gung-ho about audience data as those that sell ads.

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Study: Consumers Who Watch Ad-Supported OTT Are Younger, Higher Income

It turns out there’s a large group of Americans who don’t watch just Netflix or other ad-free video services: 45% of consumers who regularly watch video online say they mainly watch ad-supported over-the-top services.

That’s according to a new study from the Interactive Advertising Bureau. The online-advertising trade group’s research also found that the largest audience segment of ad-supported OTT viewers comprises adults 18-34 years old, and on average they have higher incomes than the overall U.S. population (with 34% of ad-supported OTT viewers reporting income of $75,000 or more).

In addition, consumers who mostly watch ad-supported OTT services skew higher among men; black and Asian consumers; and households with children, the IAB study found.

As a cohort, ad-supported OTT viewers are harder for advertisers to reach through conventional TV (while pure subscription-based video-on-demand services like Netflix, Amazon Prime, or HBO Now do not carry advertising). On average, primarily ad-supported OTT viewers watch 10.4 hours of cable TV per week versus 14.7 hours among TV-only viewers. Meanwhile, about 52% of ad-supported OTT viewers are cord-cutters or cord-shavers, with over one-third citing “better content on streaming services” as a reason for choosing ad-supported OTT over other services.

IAB released the findings at its inaugural NewFronts West advertising event in L.A., which runs Oct. 9-10. Sue Hogan, the trade group’s SVP of research and measurement, said the study points to “the high value that brands should place with increased investment in ad-supported OTT.”

The IAB’s study defined ad-supported OTT video viewers as those who watch video through a free streaming service with ads (such as YouTube, Pluto, the Roku Channel, Crackle or Vevo); via an online pay-TV provider (e.g., Sling TV, DirecTV Now); through a streaming app that requires a cable, satellite or telco login (e.g., Discovery Go, FX app, WatchESPN, Comcast Xfinity); or through a subscription-streaming service that includes ads (e.g., Hulu or CBS All Access with limited ads).

The IAB study also found that the predominantly “ASV OTT” cohort showed higher ad receptiveness than those who mostly watch SVOD or only watch TV — which is not surprising, but a key point for marketers. About 59% of ASV OTT users agreed that “I don’t mind seeing ads if I’m getting to watch content when I want,” compared with 47% of primarily SVOD viewers and 34% of TV-only viewers.

In addition, ad-supported OTT viewers reported spending more on online subscription purchases — $119 per month — than subscription VOD viewers, at $89 per month. ASV OTT fans also are more likely to follow social influencers: 25% said they regularly watch videos from YouTube personalities, vs. 17% of SVOD-dominant consumers and 5% of TV-only viewers.

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Facebook Limiting Information Shared With Data Brokers

Facebook is curbing the information that it exchanges with companies that collect and sell consumer data for advertisers, as the social-media giant tries to calm an uproar over its handling of personal information.

The measures, part of which Facebook announced late Wednesday, affect a group of so-called data brokers such as Acxiom Corp. and Oracle Corp.’s Oracle Data Cloud, formerly known as DataLogix, that gather shopping and other information on consumers that Facebook for years has incorporated into the ad-targeting system that is at the core of its business.

Facebook said it is ending an ad-targeting option called Partner Categories that lets such data brokers target specific groups of Facebook users—people who buy a certain product, for example—on behalf of their ad clients. Facebook believes shutting that system down “will help improve people’s privacy on Facebook,” Graham Mudd, product marketing director at Facebook, said in a post Wednesday.

​In addition, Facebook is halting its practice of providing anonymized data from its platform to such information brokers that they use to measure the effectiveness of their ad campaigns, said people familiar with the matter. But the company is trying to find more secure ways to share data with these brokers to measure ad performance, one of the people said, at a time when advertisers are clamoring for data that proves that Facebook ads work.

Facebook is making the changes as part of a broader internal review of how it handles user information. The company is reviewing its relationship with data brokers in part because it is concerned about how those firms are obtaining their data and how accurate it is, one of the people said.

Late Wednesday, Acxiom confirmed Facebook had informed it of plans to end Partner Categories, which Acxiom estimated will reduce its fiscal 2019 revenue and profit by as much as $25 million. Acxiom’s brief statement didn’t give a reason for Facebook’s decision.

“Today, more than ever, it is important for businesses to be able to rely upon companies that understand the critical importance of ethically sourced data and strong data governance,” Acxiom CEO Scott Howe said. “These are among Acxiom’s core strengths.”

Oracle declined to comment.

Facebook has battled criticism over its user-data practices since it said on March 16 that personal information was improperly obtained by Cambridge Analytica, a data-analytics firm that worked for the 2016 Trump campaign.

Chief Executive Mark Zuckerberg apologized last week for a “major breach of trust” in that episode and outlined steps the company has taken and plans to take to better protect user data. On Wednesday, the company also announced measures to make it simpler for users to examine and change some of the data about them that the social network tracks.

Curbing its relationships with data brokers could affect Facebook’s value proposition to advertisers, removing a layer of information that has helped some marketers target ads with greater precision. But the impact is likely to be limited, industry executives said.

Facebook’s partnership with data providers has particularly helped brands that lacked detailed customer data, such as consumer packaged-goods makers, said Lance Neuhauser, CEO of 4C Insights, a digital-ad service provider. However, he said, advances in Facebook’s own targeting capabilities have “made the need for some of this third party targeting a little less important.”

Facebook and other internet companies also are under pressure from European Union authorities to make sure all of its targeting data is collected with user permission, as part of the EU’s General Data Protection Regulation scheduled to take effect in May. Verifying that could be difficult with data from brokers, Mr. Neuhauser said.

In a memo to advertising agencies, Carolyn Everson, Facebook’s vice president of global marketing solutions, said the data-broker relationships would be phased out in six months. Advertisers can still target audiences on Facebook but they must use “data that they have the rights, permissions, and lawful basis to use,” she said. “We understand this may impact your clients advertising efforts on our platform, and we will work with you through this transition.”

While Facebook has a huge amount of data on users—sites they’ve liked, their interests and detailed demographic information, even their chat history—brokers such as Acxiom, Oracle Data Cloud, and Epsilon Data Management LLC have reams of information on people’s purchases, household income and other characteristics.

That information is matched to Facebook profiles, allowing brands to target ads at people who have bought certain products—and extend those campaigns to Facebook users with similar characteristics. These relationships helped Facebook beef up its ad-targeting capabilities in recent years, former employees say.

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Netflix’s Use Of Big Data: Lessons For Brand Marketers

by Jonathan Cohen, principal brand analyst at Amobee.

Last Friday, Netflix founder Reed Hastings celebrated Netflix getting its 100 millionth subscriber, a major milestone for a company that has spent the past 20 years thriving on science and analytics.

Netflix has arguably been the biggest disruptor of the decade to the TV and film industries, and it’s impossible to describe its success story without recognizing the central role big data has played every step of the way.

Its business model depends on using analytics to understand its audience better than its competitors. For brand marketers, for whom understanding audience behavior is equally essential, Netflix is a great case study on how to leverage big data correctly.

I see three ways in which Netflix has successfully used actionable analytics that can be relevant for brands.

Outreach Needs To Be Personalized

Even before Netflix was a video streaming service, its recommendation engine played a critical role on its website. Back when its existed solely as a DVD rental-by-mail-business, Netflix didn’t have enough inventory to ship the biggest new releases to all its customers overnight, so it created an algorithm that suggested movies its customer would be interested in, based on their previous picks, and didn’t emphasize new releases.

The strategy worked, and in 2006 new releases represented [PDF] less than 30% of Netflix’s total rentals, compared to new releases making up 70% of total rentals at standard video stores.

Since it made the shift to online streaming, a more sophisticated recommendation engine has been successfully surfacing content that’s personally relevant and engages users to the point that they spend on average 17.8 minutes browsing before selecting a program to watch, compared to 9.1 minutes of browsing for cable users. That keeps Netflix’s monthly churn rate in the low single digits, extending the lifetime value of customers and saving an estimated $1 billion-plus per year in retention efforts.

Minimizing Data Loss Is A Strategic Advantage

“Big data helps us gauge potential audience size better than others,” explained Ted Sarandos, Netflix’s chief content officer, in a 2016 interview.

That’s true, but it’s also important to recognize why it’s able to take advantage of analytics to an extent that traditional broadcast and cable networks can’t. Netflix has exact data at the individual user level as a content platform and creator in a walled-off ecosystem.

Netflix paid $100 million in advance for 26 episodes of “House of Cards” because it knew people who watched the British version also loved Kevin Spacey and David Fincher movies, an insight that’s only possible in a walled-off ecosystem, not from estimated ratings.

Additionally, when it came time to promote “House of Cards,” Netflix had enough audience data to serve different variations of its ad to different audience personas. For instance, “Thelma & Louise” fans saw a version focusing on the female characters, while people who viewed Kevin Spacey movies would see him as the focus.

Relating that to brand marketers, the more unified their digital spend (while minimizing the challenges of working with multiple vendors and metrics), the less data loss there will be, allowing for more educated and effective campaign optimization efforts.

Adapt The 13-Millisecond Rule

Netflix understood it needed to capture a member’s attention within 90 seconds or they’d leave the site. And acknowledging recent research that found the human brain can process an image in as quickly as 13 milliseconds, Netflix began A/B testing the box art thumbnail image for select films, allowing users to pick between six options. Video viewing increased by 20%-30% for the winning images, with photos showing facial expressions that reflected the tone of the film or TV show tending to do well.

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Prime-time is still king, however you watch

It’s becoming increasingly clear that, despite the fact we can watch anytime, we continue to gravitate to traditional prime-time hours. And that is regardless of the device we are using.

Data from comScore shows that however we watch video, the most frequent time for viewing is between 8 and 11PM each night. Television, of course, has lived by primetime for decades. However, it looks like the DVR was made for it. In the peak viewing hour, 9 to 10PM, 17% of household DVR viewing takes place. Both television and online viewing see a much smaller peak, about 8%, occurring in that hour.

The BBC confirms that the peak of on-demand viewing occurs at almost the same time as regular television viewing. At 9PM, TV viewing peaks with 27 million viewers. The peak in iPlayer usage occurred slightly earlier in February 2017. There were 763,000 iPlayer TV requests about an hour earlier than the TV. Unlike TV viewing, however, the peak in iPlayer requests is sustained much longer, through until about 10PM.

Incidentally, the BBC points out that the Internet peak, which includes all consumer usage, not just video, occurs at 4PM.

Viewing peaks at primetime through all devices

In the recent free nScreenMedia white paper The Secret Life of Streamers, new Conviva data reveals that primetime is peak viewing time regardless of the screen used. The two biggest screens, the connected TV and the PC, show the biggest peaks at 9PM each night. During that hour, connected TV video plays are 2.7 times higher than in the average hour. PC video plays are 1.7 times bigger than average hour plays.

The tablet and smartphone peaks are twice the average hour plays for each device.

This data reflects the very different usage patterns for each of the connected devices. The connected TV is a favored device for consumers to turn to when they want to enjoy their favorite shows on SVOD. For example, Netflix reports most of its viewing is on connected TVs. Whereas, the smartphone is used more consistently throughout day. When it comes to nighttime viewing, consumers turn to the biggest screen at their disposal to watch high value content.

One interesting thing to note about the PC is the peak in viewing that occurs around noon each day. This may suggest that a favorite lunchtime activity at work is catching up with a favorite show, or the latest YouTube video.

On-demand viewing, bigger screens preferred for prime-time

This data suggests two things:

On-demand platforms support and enhance the viewers desire to watch during prime-time
Viewers continue to gravitate to the biggest screen available between 8-11PM.

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