When it comes to the core difference between traditional broadcast and streaming, it’s all about data. Broadcast provides very little information about the viewer. Even for those with Nielsen boxes attached to their television sets, it’s only basic demographic information tied to what’s been watched. It’s near impossible to understand the relationship between those demographics and viewer behavior to everything else the viewer might be doing across the digital universe. But with streaming, it’s completely different. Viewing behavior within streaming content can be related to other digital activities to create comprehensive insights which can be used to recommend or suggest new content, tailor ads for greater impact, and more. Many would argue that personalization is not only the future of the streaming video experience, but a critical element that streaming operators must provide to improve engagement and reduce subscriber churn. Below, we’ve provided a step-by-step understanding of how you can find the data, collect it, and piece it together for a richer data picture of individual viewers which you can use to personalize the streaming video experience.
It’s not hard to understand why a personalized video experience might be better than a generic one. When a viewer feels that an app interface, or content recommendation, or the player experience is tailored for them, they are probably more likely to stay engaged longer (whether that’s in the streaming platform or with a specific piece of content). And for ad-supported models, that means more impressions because that viewer is going to watch more video for longer. There isn’t much data about the impact of personalization on viewer engagement and subscriber retention, but some early indicators, such as in a survey by Concentrix, indicate that, “over 70% of subscribers say they only engage with personalized messaging, and nearly 65% will stop buying from brands that use poor personalization tactics.”
The Many Opportunities of Personalization
Personalization, though, isn’t relegated to just one dimension of the video experience. One of those, of course, is content. Raviteja Dadda, a Forbes Council Member, identified a number of ways the content could be personalized to a viewer including curation, recommendations, and promotions. For FAST providers, curating content could mean a personalized channel for each viewer that includes both recommended and promoted assets. But there are other places to personalize as well including advertising, the interface itself (adding contextual menus to content categories the viewer often watches), and even commerce opportunities. For example, a streaming platform could blend third-party data, such as purchasing history at a major retailer, with first-party data about content watching, to suggest purchases during the video experience itself. In all of these cases, the personalized experience communicates to the viewer that the streaming brand (the platform operator) understands their wants and needs. This makes the subscription all the more valuable.
Convincing the streaming industry that personalization is the future, though, isn’t hard. What’s hard is understanding how to carry it out and getting the data you need to make it happen.
What’s The Data And Where Can You Find It
At the heart of personalizing the streaming experience is data. Data about what’s watched (and for how long), when a viewer abandons a video (and where in the video that was), how other people like the viewer are behaving (cohorts), and third-party data such as that from Google, Amazon, and other sources. Although it’s up to you from where to source any third-party data, there is quite a lot of first-party data that might already be available to pull from within your streaming video technology stack.
When we built the Streaming Video Datatecture, we tried to capture all of the various systems that throw off data which might be used to personalize the video experience.
As you can see, there are not only multiple categories of data but multiple providers within each category. Depending upon how your stack is put together, you may be looking at ten, 20, or even 30 sources of data which can be connected and correlated to provide a more in-depth picture of an individual user. The data is not just about what happens in the video player, though. It’s also about other metrics such as user behavior within the interface, abandonment, clicks, and even the bitrate ladder. For example, if a viewer often selects 4K videos but the data shows that bitrate is never reached, it might make more sense to suggest content to them that is 1080p HDR (which may also reveal a wider library of assets for the viewer).
To personalize the video experience, there are three key steps. First is to collect the data. Second is to correlate the data together. And, third is to connect the resulting data set to your video platform in real-time.
Step 1: Collecting the Data for Streaming Personalization
It might seem like an easy task, but it’s actually not. Although many components within the streaming video technology stack make their data available (often through APIs), some don’t. And even if you can gather the data easily, it’s not a one-off task. You’ll need to build a proper data pipeline with connectors to each data source. This can be quite resource intensive out of the gate. You’ll need developers to build those connectors and ensure that they can be maintained easily going forward in the event that an API call or end-point is changed by the component owner.
Step 2: Correlate the Data for Streaming Personalization
Once you have access to all of the data in a pipeline, you’ll need to send it somewhere for post-processing. Usually, that’s a visualization tool. And although such a tool may be useful for analytics purposes, it’s not necessarily useful for real-time personalization. Because of that, your data pipeline will need to feed a datalake against which business rules can act on the data to normalize it and stitch it together (imagine data cubes for each individual viewer). In some cases, this may require standardizing specific metrics, like a user ID. Because of the need to normalize, just like with Step 1, there’s some heavy lifting that needs to happen up-front. But, once you get your business logic set up, you probably won’t need to change it very often unless the data sources themselves change.
Streaming Personalization Won’t Succeed Without A Good Data Model
The business logic that you build around the individual viewers isn’t just about normalizing and processing. It’s also about thresholds, or indexes. For example, you’ll want to build a data model for personalization that includes scores for specific personalized elements. When thinking about personalizing the user interface, each dynamic menu item will need to have a score to appear. So if a specific viewer has an index of .33 for a given interface item, it won’t display. But a viewer with an index of .79 would see that interface element. To make this more concrete, imagine that your interface includes menu items for specific genres of video. If the index is above the threshold for a viewer in a specific genre, that genre menu item would appear helping them to discover content more easily that the data indicates they prefer to watch. This can also apply to recommended content as well. Just because a viewer has watched one content asset doesn’t mean they are immediately going to be interested in something related (either by actor or genre, or within their cohort). Each content asset should, for each viewer, eventually have an index that determines how it fits into individual cubes of viewer data. As you can imagine, this is no easy task but it pays off in the long run because those index scores become the knobs and switches that you can use to optimize personalization.
Step 3: Connecting The Personalized Data To Your Video Platform
It’s probably safe to say that your platform isn’t built with personalization in mind. Although some elements, such as a video asset reel, are probably dynamic and can be easily integrated with a personalized data set, your interface may not be. So you’ll invariably have to do some development work to enable personalization for other aspects of your platform. But, once this is done, you’ll be able to leverage those individual viewer datasets to truly create a customized experience.
Streaming Personalization Isn’t a One-And-Done Activity
Even if you follow those three steps, your work isn’t done. That’s because the underlying challenges aren’t just acquiring, normalizing, and integrating the data. There’s another obstacle that can actually undermine the whole effort: real-time. Personalization can’t happen once for each viewer. It must be continually updated. And, to make it truly impactful, it should be carried out even as the viewer is interacting with the platform. Think about it this way: each piece of content the viewer watches, each click, each ad viewer, adds to their cube and can affect recommended content or a changed interface element. For example, what if a viewer watching a horror movie finally tipped them past the threshold of displaying the “horror” category in their interface menu? You wouldn’t want them to wait until logging in again to see that. You would want to display it immediately, perhaps even with a “new” badge, so that they would jump right into the next piece of content without having to find it.
But getting data in real-time can be difficult. Most streaming operators aren’t staffed to create real-time pipelines. Yet if personalization is truly the next evolution of the streaming experience, no operator can afford to ignore the need to build out a data function within the organization, one that can create connectors, build business logic, define a data model, and integrate everything with the platform itself. It’s hard work, no doubt, but the benefits of improved engagement rates and less subscriber attrition, are well worth the efforts.
Datazoom specializes in enabling streaming operators to create data pipelines. If you’re interested in understanding more, and even seeking a resource to help you collect the data you need (and assist you in buildind a real-time data pipeline), contact us today!