8 Core Video KPIs you can build with Datazoom’s Data Dictionary
Every video player exposes data points differently. Datazoom’s Data Dictionary defines how we normalize different player terminologies into a common nomenclature. When data is standardized across platforms, players, and centralized across the video delivery chain (encompassing encoders, to CDNs, to playback data), we have a more holistic view of performance and operations, as well as the ability to understand the causes behind the numbers we see.
Here, we’ll review eight KPIs you can build in any analytics system based on Datazoom’s Data Dictionary. We’ll provide you with some generalized formulas, with Data Dictionary terms bolded.
You can then translate these expressions into the specific querying language of the tool you’re using, or another format your system of choice may require. Keep in mind that these formulas are sample starting points. They are by no means the “end all and be all” formulas. Part of the beauty of the Data Dictionary is its role as a springboard for customizing metrics in a fashion which best suits your organization.
However, please note that similar to how players expose data points by different names, each player exposes different data points entirely. Thus, some of the metrics below may not be supported across players. To check if the players your team relies on are supported, check out our documentation here.
General Metric Formulas
1. Play Requests
Alternatively called “Play Attempts,” this metric is the summation of total user attempts to initiate video playback. This QoE metric provides a good way to obtain an understanding of an audience’s interaction with a specific video asset title.
Using Data Dictionary nomenclature, a general formula is:
=(sum of Play_Requests)
2. Play Starts
Sometimes referred to as simply “Plays,” this QoE metric is the total count of First Frame events. As such, this metric indicates the number of playback experiences which successfully initiated.
Using Data Dictionary nomenclature, a general formula is:
=(sum of First_Frame)
3. Video Start Failures
This QoE metric is an important gauge service performance. Reflected as the percentage, Video Start Failures indicate the total play requests which fail to reach First Frame. In other words, this metric compares Play Requests with Play Starts. Under ideal circumstances, a value close to 0% is desired.
Using Data Dictionary nomenclature, a general formula is:
=(((sum of Play_Requests) – (sum of First_Frame))/(sum of Play_Requests)))*100
4. Average Bitrate
This metric reflects the mean bitrate persisting over the course of a playback experience. It is useful for understanding the average data being transferred per second of playback.
This QoE metric is useful for understanding the average connectivity of the end-user during their experience.
Using Data Dictionary nomenclature, a generalized formula for Average Bitrate is:
=((sum of bitrate)/(count of events with bitrate))/1000
5. Average Time to First Frame
This metric reflects the mean time which has elapsed between the user initiating playback by pressing the play button and the commencement of said playback. This is an important QoE metric which, under ideal circumstances, should be kept as low as possible. A metric complementary to Average Time to First Frame is Exits Before Video Start (EBVS).
Using Data Dictionary nomenclature, a generalized formula for Average Time to First Frame is:
=(sum of timeSinceRequested for First_Frame event)/1000
6. Exit Before Video Start
This metric computes the percentage of users who exit a video playback experience before the first frame is visible. Exit Before Video Start is a useful metric for gauging the percentage of viewers discontinuing their playback experience before the first frame commences, thus indicating start times are lasting longer than a user’s interest in remaining in their experience.
Using Data Dictionary nomenclature, a generalized formula for Exist Before Video Start:
=(count Play_Request – count First_Frame)/count Play_Request)*100
7. Total Time Watched
This metric reflects the total playback time viewed by users. This KPI is useful for obtaining an understanding of the total time users spent viewing content.
Total Time Watched is also a useful example of the versatility of the Data Dictionary as a foundation for metrics. Our team has identified two different generalized formulas which will yield a value for Total Time Watched.
Using Data Dictionary nomenclature, some generalized formulas for Total Time Watched are:
=sum (timeSinceLastFluxData from event.type=FluxData)/1000/60
OR
=sum(max(totalPlayTime for each unique sessionViewId)/1000/60
8. Rebuffer Ratio
Alternatively called the “Buffer Ratio,” this metric, reflected as a percentage, compares the amount of time a viewer spends re-buffering (waiting for video) against time spent watching a playing video. Rebuffer Ratio is useful for understanding the fraction of a user’s playback experience spent loading the video again once playback commenced.
Note that for this particular calculation, our formula does include buffer events generated during the initialization of the playback (i.e. the original “buffering” period), though some calculations would evict these events.
Using Data Dictionary nomenclature, a generalized formula for Rebuffer Ratio is:
=sum(timeSinceBufferBegin from event.type=BufferEnd)/sum(max(totalPlayTime for each unique sessionViewId))
Another way to view Buffering is to create a time-series metric that charts the average time viewers are in a Buffering state:
=time series chart with a span=10sec of avg(timeSinceBufferBegin from event.type=Buffering)
See for yourself
Equipped with these metrics, you can begin to visualize video KPIs in new analytics and data visualization systems ranging from application performance monitoring (APM) tools to customer analytics tools.
You can start building these metrics now when you visit app.datazoom.io/signup and begin your 15-day, 5GB free trial of Datazoom. Reach out to us if you want more information on how to get started or customizing your plan. We would love to hear out your use-case so that together, we can create an action plan to assist you in operationalizing your video data.
How to build a new Data Infrastructure to support your streaming strategy
We all agree that a business’ performance is only as good as the data backing its insights. Yet, many streaming media organizations struggle with approaching, let alone identifying, the best way to renew their data strategy. Change may be the only certainty in the market, but that doesn’t mean all change strategies are the same.
When you aren’t sure of the combination of data, metrics, and tools you want to use, rebuilding your data strategy becomes even more of a headache. We’re prone to encountering a “chicken-and-egg-like” problem: you need data to understand the metrics and tools you want to use, but today we simultaneously need these metrics and tools if we are to have any access to data at all.
A New Approach to Data
In response to that conundrum, our team at Datazoom wants to share an approach that’s been successful for some of our customers who are currently planning their renewed data strategy with us, while also facing resource constraints. The strategy rests on establishing the minimum datapoints and toolsets for their web platform and then replicating and expanding onto other platforms like mobile and OTT.
Initiating your Datazoom experience on web SDKs is a worthwhile place to start because they: (1) are drop-in integrations that (2) can be updated using the Datazoom UI to add or remove data points and Connectors, while (3) enabling our team to act as a resource to code and build-out net new data points for your chosen Collectors which can then be activated from our UI with zero development or code changes.
A Plan for Action
Once we’ve established the right combination of datapoints and Connectors for the web properties, we use this as the foundation for developing, replicating, and expanding into other Collectors for efficient development and timely deployment.
For the above approach we:
1) Scope out 30 minutes with the web development team to perform the integration and “turn it on.”
2) Identify one SDK to remove/intermediate, using 3-5 “sanity metrics” to ensure parity between our SDK and the native SDK before phasing out the latter.
3) Repeat this process with other Connectors, one by one.
4) Deliver a finalized a list of Data Dictionary datapoints that all Collectors should supply, and begin the gap analysis (and any development planning) for other Collectors.
This approach minimizes the use of your team’s development resources and enables an iterative process for scoping and ongoing product planning. The reality is that defining the product and analytics stack before data is akin to “putting the cart before the horse,” which could result in issues when the strategy is deployed.
These problems include compatibility issues or setting timelines which are unachievable. Using Datazoom as a way to plan and test implementations gives you a place to perform experimentation while providing feasibility and timing feedback for structured, realistic, deployments.
You can set this plan into action right now when you visit app.datazoom.io/signup and begin your 15-day, 5GB free trial of Datazoom. Reach out to us if you want more information on how to get started or customizing your plan. We would love to hear out your use-case so that together, we can create an action plan to help you operationalize your video data.
Improving the performance of CDNs with Real-Time Video Data
CDNs and Publishers
Unless your business has access to significant resources and has a large footprint like that of Facebook, Amazon, Netflix or Google, delivering video around the world to global audiences is a particular type of challenge. In order for video content publishers (VCPs) like NBC or Hulu to reach audiences at all times, they use one or many public Content Delivery Networks (CDNs) which take on the task of transporting content on their behalf.
The relationship between VCP and CDN is often quite strategic. While prices in the CDN market have decreased significantly, these costs remain among the largest cost centers for VCPs. Considering the significant responsibility placed on CDNs, one would assume that VCPs would have mechanisms in place to monitor CDN performance. But this is not the case.
VCPs know that maintaining a high quality of experience (QoE) is mandatory if they want to keep audiences engaged. Unfortunately, CDN vendors are often the first parties blamed by VCPs when QoE issues arise, even when the issue originated at another link in the delivery chain.
Recently, I was part of a discussion with the CTO of a growing OTT video company. He expressed to me a concern weighing heavily on his mind – a lack of useful data. He said:
We have plenty of analytics that tells us if there’s an issue with our streaming experience. We know that some of our devices and viewers experience (QoE) issues, such as buffering (among other) problems…because analytics provide the metrics that can tell us that. But we don’t know why devices are buffering. We don’t know why it’s happening, and if the problem is caused by [the CDN] or another part of the video delivery ecosystem.
This problem persists because the video delivery ecosystem has become much more complex.
The Quest for Better Data
In response, CDNs developed combined offerings for core components that video publishers needed: CDNs, video players, and analytics. These all-in-one solutions could leverage the same data flowing between systems to adjust and thus create a homogenous video technology stack. However, adoption of these end-to-end offerings has not been wide-spread. VCPs have instead opted for building their own heterogeneous mix of what has grown to become 10-20 “best-of-breed” core technologies (CDNs among them), to customize and to better differentiate their services for end-users.
However, the rise of superior independent technologies obscured a bigger picture: that they had to work synchronously, and aligning these different and diverging systems requires the standardized data all in one offerings report to provide.
With regard to data, no standards were proposed, and almost none have been adopted en masse, to encourage the interoperability and communication between systems. Incongruencies in data mean that VCPs are blind to the pain-points in their own video delivery chain. Attempts to identify the root causes of failure within the mix of diverging systems are impossible.
For CDNs, a lack of consistent data translates into a lack of visibility into how a video transmits to audiences. Therefore, VCPs are without the resources required to perform the difficult task of achieving parity with the QoE viewers have come to expect from traditional television. For instance, identifying specific “smoking gun” nodes or peering issues with a CDN can take hours, even days and requires the laborious task of manually organizing and interpreting CDN logs.
Matters for CDNs are complicated further as they themselves rely on other entities, like Transit providers and ISPs, to complete the delivery chain. VCPs have no insight into what happens to their content during these exchanges. In response to CDN instability, VCPs adopted CDN-switching technologies. Yet, while such technologies assist VCPs in avoiding certain types of delivery failures, subjective metrics and not objective data is the basis for switches. Thus the crux of the issue is unaddressed – the lack of homogenous, centrally-collected and contextualized, data.
Win-Win: Aligning the Video Delivery Stack
Streaming video providers need a tool for centralizing data collection and control. With a common dataset as a point of reference in lieu of metrics, CDNs, and VCPs to begin to construct a transparent, contextualized, and collaborative dialogue.
Enabling two-way data-share and building a real-time feedback loop between VCP & CDN, a video data control platforms offers the centralized control required to accomplish this task and serve as a foundation for truly innovative projects such as incorporating AI/ML into video delivery. Such tools securely capture very granular data from disparate sources and normalize the huge expanse of information obtained into a correlatable dataset from which to draw insight and put to work.
CDNs and VCPs can build a real-time feedback loop for mutual improvement so long as they agree on a central “point of exchange” through which captured data be collected and standardized. It is a win-win scenario for every player in the delivery ecosystem, from CDN to publisher to end-user.