A New Era of Streaming Wars Is Here—and This Battle Is Getting Personal
Amid fierce competition, streaming services are looking to new personalization strategies to increase content engagement, tackle churn, increase advertising revenue, and encourage users to return to their apps. What new and emerging data-driven approaches should streaming services use to boost their content discovery user experience?
It’s no secret that streaming services are feeling the pinch of both increased competition, and the inflationary pressures on household budgets worldwide. The streaming wars have evolved into an ongoing pitched battle of customer retention. When it comes to convincing users of the value of a streaming service, content engagement is the new king.
Churn has always been a challenge for subscription services, but when even Netflix is turning to ad-funded options, you know existing methods of churn reduction aren’t enough. Advertising-based business models bring their own customer retention and content discovery challenges, of course, just ask the services pushing at the vanguard of the current trend for free ad-supported streaming TV (FAST).
The Personal Touch is Worth the Effort
So what’s the answer? Adding more and more content is great, but then consumers find themselves overwhelmed with choice. Helping them to find something attractive to watch, as quickly as possible, is essential. Recommending content based on a user’s viewing history is a great way to shorten the time to content in the streaming user journey. It’s a sensible use of data that you’ve already got, and is great for customer satisfaction, no matter what the business model.
Want proof that personalization can really move the dial? Aggregated data from our own customers show that, on average, users who interact with sections of an app featuring personalization watch a greater duration of content, and engage with a larger variety of individual content items than those who only interact with generic sections. One of our customers saw the average number of content plays per user session rise by 133% when users were interacting with recommended content!
Metadata Plus User Data: The Art of Recommendations
The challenge for streaming services is that implementing personalization is not a trivial thing. It’s an art as well as a science, requiring a complex blend of metadata and behavioral insights. Metadata can help you match one crime drama with other crime dramas, or one Tom Cruise movie with another that he stars in. On the other hand, behavioral insights will tell you that the only Tom Cruise movie your viewer has watched is Far and Away from 1992, so maybe they're more interested in historical romance than Mission: Impossible. Or that a significant proportion of people who watch your new gardening show are also watching a particular cooking show, making each of them ideal for cross-promotion.
What’s New in Personalization?
24i is seeing a surge of interest from both subscription and ad-funded services in new and emerging ways to make user-specific adjustments to their streaming user experience. Here are five questions to ask yourself to determine if your streaming service is making the most of modern personalization methodologies:
Are you using data to determine the order of your editorially derived content?
Algorithmic recommendations are often seen as a threat by editorial teams. In my own past working at Sky’s streaming service NOW TV (simply known today as NOW), we’d regularly see the editorial and data teams sending out competing emails to see which would perform better. This “them and us” attitude is such a waste of great talent. If your editorial team isn't already leveraging the power of data science, then my first and most important recommendation is to get started as fast as humanly possible.
It’s true that no algorithm can know your content (or your specific business goals) as well as your editorial team. Equally, of course, no editorial team can know each consumer individually. Together you get the best of both worlds. Start with your hero banner. Let the editorial team pick the content you want to showcase, then use personalization algorithms to determine the order in which to display that content for each user based on their viewing history and what is most likely to appeal to them. It’s such a quick win, and you’ll be amazed at the instant impact it has on your engagement.
Have you thought about the psychology behind the way you label your data-driven content sections?
Many streaming services have “Recommended For You” or “Trending” rails. Other common titles you’ll see for data-driven sections include “Most Popular,” “More Like This,” “Because You Watched,” etc. Each of these can be successful, but some will work better in one part of your app than another. And although some may seem like interchangeable titles for the same content, they work best when paired with subtly different data sets.
If you’re serious about personalization, you need to think carefully about the psychological impact of your section names and make sure they are suited to both your specific user base and your goals. Expert help and a lot of A/B testing will help you find the optimum mix for your service and your goals. For example, our experience suggests you should use different algorithmic models for “Most Popular” and “Trending.” Equally, a “Recommended For You” message is most effective when you want to help users discover something new. In business terms, that translates to encouraging your user base to watch a wider range of content from your library.
Are you using making the most of “post-play moments” to encourage additional viewing?
The Merriam-Webster dictionary suggests “binge-watching” was first used as a term in 2003. In less than 20 years, it’s become a common concept for a large proportion of the global population. Auto-playing the next episode in a series is a great way to keep consumers within your service, which is why it’s widely used by streamers of all kinds and sizes. What happens at the very end of the series, however? Or at the end of a one-off movie or documentary that doesn’t have an obvious sequel?
Too many streaming services have gone all-in for binge-watching but are still wasting a prime opportunity to make personalized content recommendations at the same post-play moment for their non-episodic content. If you’ve got a “Because You Watched” algorithm in action already, this should be a really quick win to implement.
Is your metadata optimized to ensure the best content-to-content recommendations?
Metadata is, quite frankly, the biggest hurdle most streaming services face when it comes to recommendations. Every single company I speak to tells me they’d like to improve the quality of their metadata because that’s how you make good content-to-content matches. This is one area where you definitely want quality over quantity. We had one company proudly tell us they had over 50,000 different keywords, but if each keyword is used on only one or two pieces of content in your library, how does that help you make relevant matches?
There’s no magic bullet to metadata quality, but we have made significant improvements for customers by using advanced metadata-prediction models (machine learning). The first step is to analyze their existing metadata, then we define a structured framework of keywords—not too many! The machines can then fill in the blanks to tidy up the keyword usage across the library.
Have you taken personalization beyond the app and into your marketing messages to drive repeat visits?
Encouraging existing users to remember your app via marketing messages is an important step whether you’re looking to combat churn or increase ad-revenue. Integrating personalized content recommendations into those same messages takes you a step further; it gives the user a concrete reason to return. If you’re not using personalized content recommendations in your emails and push notifications, then now is the time to consider it.
There are many variables to consider, particularly around the timing of your communications, but if you get it right, you can see a big impact. One of our customers conducted A/B testing that compared the success rate of a regular email newsletter versus an email containing personalized content recommendations. Conversions were a jaw-dropping 121% higher in the group who received the personalized emails.
Personalization is a never-ending story
The specific blend of data models and algorithms that are ideal for your streaming service can only be determined through regular testing and refinement. That’s why recommendations can’t be a simple “set it and forget it” process. They need continuous tuning—not least because your content library and user base is likely to shift over time. That is also why we offer personalization as a “managed service” so we can work continually to make incremental gains every month and adapt as content and UX changes.
Over the next few weeks, Streaming Media will publish a series of further articles in which I’ll break down some of the strategies outlined above in more detail. If you can’t wait that long, you can check out our e-guide: Five engagement-boosting strategies every streaming service should adopt right now.
[Editor's note: This is a contributed article from 24i. Streaming Media accepts vendor bylines based solely on their value to our readers.]
Personalized content recommendations are a hot topic in streaming. Extending them to your emails and other marketing messages is a logical way to increase repeat visits to your apps and counteract churn.
Metadata quality is often the biggest challenge for any streaming service looking to increase content engagement with targeted recommendations. Can machine learning come to the rescue?
Many streaming services have got binge-watching nailed for episodic content, keeping viewers hooked on their hottest series with auto-play of the next installment. But are you wasting a golden opportunity to recommend new content at the end of a series or when a viewer finishes watching non-episodic content?
If you're looking to add or upgrade your data-driven recommendations, should you go for Recommended For You, Because You Watched, or Trending to bring the greatest increase in engagement? 24i's SVP of Data Products, Damien Read, argues the choice of section names and the data models behind them should be tuned to your user base and your specific business goals.
Higher engagement with content is streaming's holy grail. More eyeballs on a greater range of content is great for tackling churn and for growing advertising revenue. But where should you invest your effort--in editorial curation or data-driven recommendations?
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