利用元数据和用户数据实现FAST和OTT货币化
There’s an old maxim in the publishing business that writing is writing but publishing is timing. 在媒体和娱乐界, 由此推论，内容为王, 但将内容货币化则完全是另一回事. Quality and quantity matter, of course, but every bit as critical as the creation and curation of a content library is the science that supports and sustains the art.
尤其是在已经饱和的OTT内容市场, FAST, SVOD, and AVOD, delivering great shows is barely half the battle in the struggle to catch and captivate eyeballs and convert viewership into profitability. OTT’s upside, even in the days when it lagged far behind traditional linear TV in audience share, 是否能够更精确地定位目标受众, personalize experiences, and leverage recommendation engines to surface the right content for the right viewers.
Of course, nothing makes recommendation engines—and every other means of profit-making leverage—go, go, go like data in its many varieties. Metadata in particular is the key to personalizing viewer experiences and optimizing content portfolios. AI plays an ever-greater role in enhancing the metadata quality and improving recommendations. As analysts focused on the M&E industry and insiders at major media companies playing critical strategic roles agree, data is also essential to understanding customer journeys and making informed decisions on how to reach and retain subscribers and, on the ad-supported side, deliver the right ads to audiences and the right audiences to brands.
元数据:一切的关键?
In a panel at Streaming Media Connect 2023 in November, Chris Pfaff of Chris Pfaff Tech Media 将元数据描述为“关键”, the DNA of everything” when it comes to driving ROI for OTT and FAST.
Paul Erickson，创始人和负责人 Erickson Strategy & Insights, calls metadata “table stakes” for an effective OTT monetization strategy and “a potential force multiplier for overall service ROI, if you leverage it correctly.”
这一切都归结为“持续参与”,” Erickson explains, 这是留存率和盈利的基础. It’s directly connected to personalization and search and recommendation. If we think about how efficiently you can target and address specific audiences with your advertising, that specificity is aided tremendously by the metadata that helps advertisers understand the nuances of that audience that they’re trying to hit. There’s a lot of value to be reaped by those parties that can effectively enhance and enrich metadata, typically using AI to help do so, 特别是当涉及到平衡不同库中的元数据质量和丰富程度时，这些库可能包含来自不同来源的新内容和遗留内容, 就像你在FAST频道中看到的那样.”
Any cost/benefit analysis must also acknowledge not just the return but the investment side of ROI, as well as the opportunities for cost reduction that metadata brings, Erickson continues. “元数据还可以帮助你了解内容组合中表现不佳的部分的具体细微差别，并让你了解其中的原因, 取决于元数据的丰富度和质量. Thus, it can help you optimize your existing content portfolio and inform your future content acquisition strategy.”
Also critical to effective use of metadata, of course, is having metadata that’s accurate and clean. Bethany Atchison，分销合伙人管理副总裁 Vevo, highlights the importance of having well-organized data at a service whose library consists of 800,000 music videos “across a variety of decades across genres” in order to deliver a high-quality user experience. “Organizing our data is really important to keep track of all of that content. We really need to have clean metadata in order to organize everything,” she says. “We have a programming team that uses their expertise to program each of our individual FAST channels, but they need to leverage AI and data to really comb through all of that and surface everything to them because there’s just so much content to go through. 对我们来说，这真的等同于我们渠道的成功.”
Content recommendations on Vevo
Dan Trotta, product manager at Warner Bros. Discovery, 通过描述他的公司和其他优质内容服务如何使用元数据来智能地确定当观众暂停节目时屏幕上出现的“暂停广告”，以及品牌广告可能放置的位置和不放置的位置，强调了元数据的价值. “假设你正在看这样的东西 Breaking Bad, and you pause on one of those moments in the show where maybe you don’t want somebody walking into the room, 你也不希望广告客户的广告在上面弹出. Streaming services are working toward metadata that runs through the episode to show you when areas are safe and when they are not,” he explains. “It allows you to take this ad format and distribute it across more of your programming hours than you otherwise would have. 那是你口袋里的钱. And it’s a better viewing experience than dropping an ad where it shouldn’t be.”
Pause ads on Max
Contextual vs. Behavioral Data
在互联网上，许多针对特定受众的广告都利用行为数据，表明用户的兴趣或人口统计数据，为相关的用户提供适合的广告. Contextual data analyzes the content itself and aligns ad campaigns with interests suggested by that content. 文本驱动的上下文广告相对简单, 依靠扫描关键词来匹配相关广告的内容. The more sophisticated contextual ad solutions applied to OTT content analyze it for sentiment and tone as well.
Eric Berger of Common Sense Networks, which develops ad solutions designed to serve age-appropriate ads alongside children’s content, 分解使用上下文数据的好处, 以及目前实施的一些限制. “The problem with contextual data today is that people are using metadata that’s created by creators in the case of platforms like YouTube, 而且上下文也不太吻合,” he says. “The better you can be at finding the actual match between the content that’s on the screen and the advertising, 消费者体验越好, and the higher the engagement is. 有时是一个话题，比如一个带有REI广告的远足视频.
但有时也是情绪和情绪的问题——团队合作, role models, 同理心——连接并吸引你. 这就是为什么许多人更喜欢上下文而不是行为.”
Common Sense Networks的上下文广告定位和匹配
Another advantage of contextual over behavioral data in generating the desired consumer response, Berger says, is its timeliness. “行为是基于过去的行为，”他解释道. “How many times have you looked for a pair of shoes and then [shoe ads] follow you all over the internet, but you’ve already bought them or made the decision not to buy them? 上下文更多的是在当下.”
有效的上下文广告是, if anything, 甚至比行为更依赖于数据, 尽管它本身并不利用用户数据. “It’s really important to get the data piece right and to scale as much of the sea of content as possible. 我们从评估内容中获取了3年的内容数据, 我们与德勤合作，利用机器学习技术将这些数据集放大，这样我们就可以有效地对适合孩子的海量内容进行分类，并能够在不需要任何用户数据的情况下，通过300多种不同类型的指标对其进行切片.”
Part AI, Part Human
As Berger describes, machine learning and AI are critical tools in taking data analysis to the next level, 无论是广告对齐还是内容推荐. As recommendation engines become more sophisticated and more efficient in their use of metadata and user data to improve recommendations or ad relevance, enhance the user experience, or hold viewer attention longer, the question arises of how much AI factors into the equation and how much human intelligence still contributes directly to the engine’s decision making.
“你在流媒体主页上看到的很多内容都是针对你、你过去的观看行为以及与你相似的观众过去观看过的内容而个性化的,” says Warner Bros. Discovery’s Trotta. “The human element there that’s still really vital is pointing the algorithm in the correct direction. 这意味着要设置关键输入和关键输出指标.人的因素也决定了关键战略问题的答案:“你是否打算在一天内为最活跃的观众进行优化?? Are you going to optimize for the most viewing hours you can get on your service in a given day? Are you going to optimize for the frequency with which users come back in a month? That gets sped through the pipeline that ultimately becomes what the user sees. But that’s the fundamental question to kick everything off each time.”
“人工智能将是一个巨大的游戏规则改变者，”他说 TVREV 联合创始人兼首席分析师艾伦·沃尔克. 他创造了“FAST”这个词，指的是免费广告支持的电视，当时它是数字化长尾和传统内容的一种出口，FAST频道提供商发现，这些内容可以作为商业上可行的媒体享受第二次或第三次生命, thanks to the improved targeting opportunities available through OTT and streaming. 人工智能的真正力量在这个领域, Wolk contends, 是深入挖掘节目来分析情绪和情绪, as Berger attests, 并在广告投放等方面做出更明智的决定.
“有些公司就像 AiBUY and KERV that are actually able to go through and identify what’s going on in different scenes,” Wolk says. “Nobody wants to run their funny commercial during the funeral scene of a show.” Until recently, he explains, “It’s been very hard to identify that. 现在他们可以通过理解所涉及的情绪来做到这一点.”
These capabilities also create opportunities for a level of contextual insight and content-matching unheard of before AI became involved. For example, Wolk says, “If I’m Cadillac, 马修·麦康纳也在其中, I want to run my ad there, since he’s in my ads too. It creates a nice synergy. It can even understand what sorts of shows viewers like and personalize their feeds. That’s going to be huge.”
普法夫认为，越聪明的人, targeted, 数据和人工智能广告策略的策划性质也可能有助于减少“广告频率和广告疲劳”——这是流媒体货币化越来越以广告为中心的一个关键问题, with even the most stalwart subscription-based services adding and emphasizing their hybrid tiers.
“Ad overload and poor targeting are always going to be a net negative for engagement or retention, 这会随着时间的推移明显影响你的投资回报率吗,” Erickson concurs.
Taking It Personally
With all of the reliance on using personal data that personalization demands, 问题是，依赖于这些数据的内容公司以及其他数字数据采集公司是否会面临一场灾难. 随着消费者越来越意识到他们正在放弃的隐私，以及他们的数据对收集这些数据的公司的价值，他们可能不会那么愿意放弃——提供这么多关于他们的偏好和行为的信息，只是为了让他们更快地看到他们想要的节目，或者训练人工智能模型为他们提供他们可能购买的产品的广告.
Publishers Clearing House recently did an extensive study on consumer attitudes regarding data collection and usage for a range of purposes, 包括M的个性化&E content and advertising. 与ESHAP CEO共同制作 Evan Shapiro and others, the findings were published in November 2023 in a white paper called It’s All Personal.
PCH's It's All Personal report
Although “privacy/security of personal data” ranked second-highest in the survey among Americans’ top concerns, 夹在生活成本和经济状况之间, a fairly low level of data literacy seems to be inhibiting their ability or inclination to act on that concern. 大多数受访者承认，当他们看到像网站的cookie政策这样的数据共享选项时，他们并不完全确定自己会同意什么. Only 50% said they’d be willing to adjust their phone’s privacy settings to guard their data more closely, and even fewer expressed an interest in knowing more about what data is being collected.
Many respondents seemed not to fully recognize the value of their personal data to the companies that collect it. 但他们似乎确实重视个性化给他们带来的好处. Fewer than half (45%) said they’d trade less personalized ads for more control over their personal data. 调查显示，在容忍能够瞄准OTT内容的数据收集类型方面，用户总体上是被动的，并且愿意放弃一点点隐私以换取个性化的娱乐体验.
“当我们观察人们对广告的态度时, I think we expected people to be a little turned off by personalization of advertising, 但事实似乎并非如此, especially for younger consumers,” says Shapiro. “They seemed to be totally chill about their data being used to personalize ads towards them.”
“The data was pretty much divided equally among people who want to have personalized content and people who don’t want to have personalized content,Publishers clearing House助理副总裁Smriti Sharma说. “Everybody talks about privacy, but do people really understand what privacy is? Because clearly the only way you can provide personalized content is by capturing their data and identifying what they like.”
对大多数媒体消费者来说，目的似乎证明了手段是正当的. “If you have the right content,” Sharma says, “people are going to follow it, and they’re ready to pay for it.”
Overall, 出版商信息交换所的研究表明，为个性化提供动力的个人数据访问不太可能在短期内减少——至少不会因为消费者出于隐私考虑而采取行动.
Striking the Right Balance
埃里克森断言，这与个人化同样重要, clean metadata, 以及人工智能增强的广告定位, no single factor contributes more significantly to streaming service ROI than sound content budgeting. (This may well be the fate awaiting premium content providers that have overspent on original content and tried to reverse their fortunes with ad tiers, sharing crackdowns, price hikes, and the like.) And naturally, matching the right content to users presupposes having the right content to begin with.
“内容是任何视频运营的命脉, 无论是传统的广播, a streaming service, a FAST channel, what have you, 它可以成就或破坏你的服务的经济效益,” Erickson says. “The right content brings people in, keeps them there, and brings them back. 但内容不足会导致他们流失, 在这些内容上花费太多, 不管它有多好，多相关, will put you upside down ROI-wise. So today, 我认为我们终于看到了一个严肃而明智的内容支出审查，当涉及到收购和佣金时，以确保整体内容预算得到有效和盈利的使用.”
埃里克森认为M&2023年作家和演员罢工引发的内容创作实际上可能会带来一些重置，这可能会促使内容获取和开发模式进行必要的调整，并重新优先考虑质量而不是数量. “Obviously, great content is always valuable to bring people in and keep them in a subscriber fold from the service perspective. 但是如果你不能委托制作那么好的内容, we have a situation now where the strike perhaps is allowing the industry to sharpen its skills in a different area,” Erickson says. “Not all great content in your portfolio needs to be original commissions. You can license and leverage some amazing content from around the world if you internationalize it properly or it’s already internationalized. 随着时间的推移，你的用户基础会变得更加多样化, 留住他们成为一个企业变得越来越重要, licensing gives you a lot of agility to make sure you are serving or super-serving all of those particular nuances of those unique audience segments. Whether you are putting content in the portfolio for an AVOD service or SVOD service or FAST channel, 你仍然需要具备一定程度的内容灵活性, 但是，这些内容支出可能会从优先考虑较少的原始佣金，转向非常谨慎的内容授权，以服务于特定的受众群体.”
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