Understanding Video Exits
This is the second blog in a series talking about how video streaming data, pulled from various parts of the workflow, can be used to support business goals. This post looks at will look video exits and how that data reflects on streaming revenue.
Perhaps one of the most obvious uses for data pulled from components in the streaming video technology stack is to improve Quality of Experience (QoE). Because what does a poor QoE result in? Video exits. So, at the heart of improving QoE is reducing the number of video exits. But why does one user exit a video when another doesn’t? Is it the quality of the content? The content type? Unfortunately, QoE can be subjective. Different viewers have different thresholds for when the viewing experience has degraded enough to warrant abandoning a view. Rather than rely on a subjective definition then, or guessing about QoE, you can use data gleaned from elements within the workflow which correlate directly to user behavior to define what an exit means for your streaming platform, use data to identify them, and, most importantly, mitigate them when appropriate.
What is a Video Exit?
The first step is to use the data to define a video exit. By analyzing when a viewer exits a video, through a unique playback_session_id element which would be generated each time the user viewed a video asset, you can breakdown exits into the following specific reasons:
Exiting after an error
Exiting before the video starts
Exiting during stalls
Exiting during playback
Exiting during an ad break
Exiting After an Error
Users experience an error which is either fatal (the video player crashes or the video ceases) or unpleasant enough to make them leave. Data from the workflow can be used to track down the error and correlate it to the number of unique playback_session_ids thereby generating a general View Error Rate and, being more granular, a User Error Rate.
You can use the View Error Rate to determine if there is a disproportionate amount of errors for specific content types or content IDs. Conversely, you can employ the User Error Rate to see if users of a particular geographical location, device type, ISP, or subscription privileges are more likely to experience an error. Once that has been identified, you can configure monitoring to alert on these error rates.
Digging even further, you can look at the specific error rate or total errors for a time period and break it down by error code and error detail. This is useful for operations and engineers to see which is the top contributing video error and inform them how to best approach it.
Exiting Before the Video Starts (EBVS)
In this type of exit, the video took too long to start or was initialized improperly. To determine that, you need to be able to differentiate between the two. You can accomplish such differentiation by capturing the viewer’s Playback Intent, which is when a viewer clicks or taps on the button to trigger the player to render and the video to play back. In many cases, Playback Intent occurs before the player is rendered.
In the case of improper initialization, you can identify this when the player renders with no preceding playback intent. The cause could be that a button is triggering the playback intent when it shouldn’t (i.e., a JavaScript event firing on page load when it should fire on mouse click) or it is behaving as expected, but the user doesn’t expect it. The result? The user closes the player which fires the exit event and generates EBVS data. Data like this can be correlated across various playback intent locations (i.e., an application, a website, etc.). If one of them has a disproportionately high EBVS, then the user likely doesn’t want to trigger playback at that point. This can be corrected to remove this EBVS data from the overall EBVS data picture to get a truer sense of why viewers are exiting before a video starts playing.
When a video takes too long to start, that is the time between playback intent (the player has launched and the video will start playing) and the first frame displayed to the user. The question to ask, then, is “why is the video taking too long to load?” One of the ways to answer this question is to understand when the user dropped off during the video delivery path:
During playback intent?
When the player was rendered?
When the playback was requested?
When the buffering started?
When the buffering ended?
When the playback started?
The answer to those questions benefit from additional insight by looking at the various components related to playback and their relative time. These components will be relative to your specific implementation, but they might include:
GraphQL
Player creation
HLS validation
Status call
JSON parsing
Dash validation
Exiting During Stalls
Buffering is a natural part of the playback process and must happen. What users get frustrated with is when the player stalls to fill the buffer. As such, you can look at the frequency of stall starts (so when the player starts after stalling) and the rate at which users exit the video. This is often encapsulated in a popular QoE metric, Rebuffer Ratio.
One of the primary reasons for a stall is the bitrate is too high. By pulling log data, you can correlate bitrates to stall start events. Generally, you want them all to be the lowest bitrate, which means you are already sending the smallest amount of data available. If stalls are occurring on higher bitrates, it means the bitrate logic is not working properly. In this case, where the bitrate adaptation is self-managed, the player should be scaling down the requested bitrate when the buffer starts to empty.
Exiting During Playback
Although this is a powerful metric, which shows when users have abandoned a specific video, it is difficult to immediately identify a reason, even when looking at the data. That’s because this can be a highly subjective measure. For example, is it about the content? Is it not interesting or compelling enough? You can look at the distribution of playhead position for the heartbeats in a given title to help determine this. If viewers are abandoning a piece of content around the same place, it could help shape future content decisions. But if there is a spike in viewership near the end of the content, seeing the jump in playhead position could mean viewers are skipping parts of the content they find unappealing. Further analysis of minute distribution across a video title can further enlighten content decisions by signifying what users find exciting, or boring.
Another angle on playback exits can relate to video quality. Even though it varies by region, it’s pretty much universal that viewers don’t like to watch poor quality video (i.e., less than 420p). To understand the impact of bitrate on video exits, you can split up the playback video exits by region and resolution. If certain regions are seeing a lot of low resolution exits, you can consider a closer CDN or modifying the compression. To cross verify this claim, you can look at the exits which occurred within minutes of a resolution change. It’s not a direct attribution, but you can see if it correlates with other low resolution video exit findings.
Exits During Ad Break
Advertising can be a death knell for viewership. Many users will leave a video during an ad break because they perceive the content as less valuable than the time and annoyance of watching an ad. To determine how tolerant your viewers are towards ads, you can split the exit data by content title. This answers the question, “are users willing to watch a specific ad only in certain titles?” When the answer is yes, the viewer clearly perceives enough value in the content to sit through the ad.
Another possibility could be the quality of the ads being shown. Similar to other types of abandonment measures around quality, monitoring the bitrate, stall ratio and time to first frame for ad content can uncover issues with ad delivery quality that may be adversely impacting viewership and engagement. If low quality ads are coming from a certain ad network, you might consider blacklisting that ad network to avoid the risk of losing engagement with your audience.
Aligning Exit Data to Business KPIs
There usually isn’t a business KPI specifically linked to video exits. But, as you can probably surmise, video exits do have a correlation to subscriber retention. If a user is exiting videos consistently, something should be done to understand why and then intervene in probably churn. This might be special incentives, such as a free month of service or access to exclusive content (for a lower-tier subscriber). Regardless, failing to address viewer exit behavior can be detrimental to the KPIs which do count, such as viewing hours per user, and subscribers per month.
Subscriber Retention: The Role of Data in Identifying Growth Opportunities
This is the first blog in a series talking about how video streaming data, pulled from various parts of the workflow, can be used to support business goals. This post explores the role of data in subscriber retention.
The Role of Data in Subscriber Retention
The streaming workflow is a complex connection of different technologies. Some are on-prem, some are in the cloud, and others are controlled by third-parties. This data is critical to identifying issues that may result in a degraded video experience and subscriber churn. But there’s more to streaming video data than just identifying problems. With the right data, you can reduce subscriber churn and even identify opportunities for growth.
Using Data to Drive Subscriber Retention
At the heart of a successful video platform is viewership, not just subscribers. Why is that important? Because a user who doesn’t watch anything is bound to churn at some point. But an active subscriber who is watching a lot of content is doing exactly what you want them to do. By continuing to consume, those active viewers clearly perceive value in their subscription and are far less likely to churn. Perceived value is the basis for retention.
So how can data retrieved from the streaming workflow be employed to support goals for retaining existing subscribers and even growing the subscriber base? By focusing on three key areas: geographic saturation, device segmentation, and marketing campaign to viewership.
Geographic Saturation
Understanding where frequency of viewership is occurring can be easily understood through a combination of CDN logs and player data. This can then be used to identify which geographic regions are saturated and which have opportunities for growth. The resulting analysis can then be employed to target specific marketing campaigns at those regions to improve viewership. Remember, this isn’t about acquiring new subscribers, per say (although this data can help identify that when viewership isn’t tagged to active subscribers, such as for a free tier of content), it’s about ensuring retention of existing subscribers. So if there is a geographic region in which a lot of subscribers aren’t actively watching content, it’s a prime target for marketing campaigns to promote viewing.
Device Segmentation
The next place where data pulled from log analysis can help is in understanding which devices are most used. This will help direct company resources to ensure those platforms have adequate support and feature development to promote more viewing. Again, this can be tied to data from the subscriber database to see which users are favoring which platforms. Marketing campaigns can then be targeted to those device owners, again in those geographic regions where opportunities are present, to promote new subscriptions as well as viewing.
Marketing Campaign Measurement
The last step of tying workflow data to subscriber retention is in measuring the success of those targeted marketing campaigns. Looking at a specific geographic region where a device-specific marketing effort was employed, such as advertising about watching a title on the streaming platform using a certain device, data from the CDN logs and the player can show if viewership increased both for new subscribers and existing. This can help to fine-tune future marketing campaigns targeting device-specific viewership in targeted geographies.
You Need the Right Tools for the Job
In addition to employing a platform like Datazoom to capture, normalize, and deliver your streaming video data to a storage location, you need other tools as well. For example, manually reconciling marketing campaign effectiveness in-region and targeting certain devices/users, is incredibly hard and time-consuming. Instead, you can use other software to further maximize the opportunities to target specific subscribers. Identity management solutions, like Kochava, and marketing attribution solutions, like Branch, are powerful applications that can utilize streaming video data about user behavior, device information, and more to optimize subscriber retention and growth efforts.
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How Complex Streaming Video Ecosystem Data Impacts Business Goals
Democratizing streaming video ecosystem data, if done effectively, can both improve viewing experiences and reach business goals. The time to shift towards ROI and profitability is now. The first two months of quarantine in the U.S. saw an additional four percent of households sign up for their first streaming service, which quadrupled the previous growth rate. Viewing time for U.S. streaming services are 50 percent above 2019 levels in June. This is likely the result of streaming services launched in the last 12 months, including Apple TV+, Disney+, HBO Max (AT&T), and Peacock (Comcast).
Orchestrate data collection to power a data-driven strategy
The first goal in streaming video ecosystem data collection should be to build a high-quality and engaging user experience. To do this, it is critical to have detailed monitoring in place, have the flexibility to run queries needed in real-time, and then act on it.
This starts with understanding the current state of the video system. To start, there must be a comprehensive understanding of data access and availability. Today, companies should be capturing data and forming video metrics throughout the video preparation and delivery pipelines.
This real-time data across various workflows should be integrated to add context to insights, identify the downstream impact of failures, and pinpoint the root cause of problems. This type of data integration across systems within the same workflow helps expose underlying issues and inefficiencies at every step.
Implementing data-driven actionability
Once the necessary streaming video ecosystem data has been collected, and cross-system monitoring is in place, content providers can begin to build their strategy to turn insights into actions. Acting on data can be either “online” or “offline.”
“Online” actions can be making automated changes to encoding ladders, updating recommended content options, or changing a delivery pathway or provider given a set of conditions or thresholds that are reached. These actions may be powered by leveraging machine learning (ML) models that are refined over time and using decision trees to automate a process that may have previously been manual. “Offline” actions can be the preparation and execution of run-books in a triage scenario, or simply knowing when and where to involve experts.
Calculating ROI
Measuring outcomes against changes in business goals and KPIs will enable businesses to measure the impact that decisions have made to their top and bottom line. Ultimately, this means measuring lift and incorporating ROI calculations into technology investments. This could be looking at changes and improvements to QoE-related churns, changes to technology costs, or trends time spent to close customer support tickets or customer satisfaction surveys aligned with average customer lifetime or overall churn percentage.
State of streaming video ecosystem data
For most video publishers, the economies of scale are so great that it doesn’t make sense to build out most core services (such as players, CDNs, origin servers, ad server, and backend services) in-house, and therefore the industry has been compiling their own technology stacks using best-of-breed solutions provided by video data platforms. With this strategy, the burden lies with the video publisher to ensure the system works and is efficient end-to-end. However, the trade-off comes with third-party services building their data and analytics capabilities to measure their individual functionality, not exposing their performance within the context of a workflow.
Until recently, the strategy of third-party services was to provide a dashboard or report that exposed the usage and performance of that service. Furthermore, each provider built their data and analytics systems independently before standards were created, or simply not adhered to. Therefore there are ways to understand how a single service perform, but it’s hard to compare the performance between competitive services, and determine the impact on efficiency and performance end-to-end
As the demand from publishers to provide capabilities for end-to-end observability and optimization has grown, so has the demand for solutions to provide and expose more real-time data, so that it can be aligned with data from upstream and downstream systems. The good news is that today there is greater access to more data at faster speeds than ever before. But the challenge of cleaning, standardizing, enriching, and aligning data still remains.
The Challenge
With streaming video ecosystem data stuck in too many silos, there’s a struggle to understand the underlying factors that shape results. There is too little data standardization, which doesn’t support actionable analysis.
As an example, when ad revenue has dropped, we need to look at different variables that impact revenue, and look at various data sets to uncover the answer:
Have CPMs dropped? Why?
Data: Look for insights and correlations between player and ad server data
Have audience levels dropped, or changed? Why?
Data: Look for insights and correlations between audience and demographic data
Is ad delivery facing technical challenges? Why?
Data: Look for insights and correlations between player, ad server, and ad stitcher data (depending)
Does the content face technical challenges that cause users to drop-off before the ads? Why?
Data: Look for insights and correlations between player, encoding, CDN, and origin data
When it comes to marketing dollars maximizing ROI, the cost of acquisition compared to revenue generation matters immensely.
Are users converting from our marketing campaigns to engage with the platform?
Data: Look for insights and correlations between player and attribution data
Are those users delivering revenue?
Data: Look for insights and correlations between player and subscription (SVOD) or ad server (AVOD) data
Which campaigns deliver users with the highest MRR or LTV?
Data: Look for insights and correlations between player, 1st party data, and financial data or ad server data
Gaining actionable insights
When we’re looking to make changes, we need to have already identified the priorities and the goals. First, what does the business want to achieve: Are we prioritizing growth? Profitability? The user experience? Secondly, what does success look like: 10 percent revenue growth? 10 percent subscriber growth? 10 percent cost reduction? For each business goal, metrics need to be agreed upon that are used to measure success.
What’s next is to ensure the underlying data is accurate behind the calculations/goals and can be measured to show progress. Establishing a single methodology for what and how things are monitored and measured will create a common understanding of system performance. Therefore, when insights are tied together, it can easily deduce what variables impact end-to-end workflows and thus control the outcomes and results, matching the business metrics.
Democratizing the complex streaming video ecosystem data: Why it matters and what the future could hold
Without the ability to understand what’s possible and to make improvements, so much of the efficiency and value of using best-of-breed solutions will be lost. Data is the only way to uncover and measure the impact of actions taken, and to track progress against business goals. Looking into the future, service providers should be evaluated not only for their capabilities but also for their ability to be a strong provider of data.
The result will be greater efficiency for the video streaming industry. Traditional “video analytics” tools or other single-system analytics diminish, and end-to-end analytics and workflow analytics rise in their place. With data as “building block” solutions that support data engineers in reducing the time instrumenting, piping, standardizing, and cleaning data will grow and power new innovations for real-time adaptability.
As our technology improves, how we approach measurement should evolve as well. Analytics only matter if customers know how to leverage these insights and transform them into impactful changes for their business. It’s critical that everyone in the organization understands the same goals, uses the same metrics, and speaks the same “data language.” In 2021, the industry will focus on making this data align with business goals for the organization – ensuring it is made accessible and contextualized for all relevant stakeholders.
Creating sustainable growth in the video business is about setting goals and priorities, defining metrics, and measuring progress in the context of revenue, lifetime customer value, and profitability. To get there, the industry needs investments in data, collection, standardization, and enrichment and measurement at every stage of the business.
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