What is EBVS (Exit Before Video Start) metric in streaming?

Imagine you're at a movie theatre to watch the latest blockbuster. You’re excited, popcorn in hand, and eagerly waiting for the movie to begin. However, things start to go wrong—the lights don't dim, the screen remains blank, and time drags on without any sign of the film starting. Frustrated, some audience members lose patience and leave.

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This scenario is akin to what happens when users encounter delays in video streaming. They come for the content, but when the video takes too long to start, they exit before the first frame even appears. This phenomenon in streaming is known as "Exit Before Video Start" (EBVS).

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What is EBVS?

EBVS is a critical metric in video streaming that measures when users leave the platform before the video begins playing—specifically before the first frame appears on the player. This metric is particularly important for developers and product engineers integrating video streaming into their applications, as it directly impacts user satisfaction, engagement, and ultimately, revenue. By closely monitoring EBVS, they can make informed decisions to enhance the user experience and ultimately user retention. For example, studies show that reducing the EBVS rate by just 10% can increase viewer retention by up to 15%.

When analysing "Exit Before Video Start" (EBVS), it's important to account for specific scenarios that might skew the data or misinterpret user behaviour. Not every video initiation represents a user's genuine intent to watch the content. Accidental clicks, where a user mistakenly clicks on a video and immediately exits, should ideally be filtered out when analysing EBVS metrics. Sometimes, a video might not start due to a clear and immediate error (e.g., "Video unavailable," "Playback error," or "Network issue"). In these cases, users exit the video not because of a delay or dissatisfaction but because they are aware of the problem. This situation should not be classified as a typical EBVS case.

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Why does EBVS occur?

One of the main reasons for EBVS to occur is due to delay in Video Startup Time. Video Startup time is the time elapsed for the moment the user clicks the play button to the moment the first frame of video starts playing.

• According to a study by Akamai Technologies, viewers start to abandon a video if it takes more than 2 seconds to start up, with each incremental delay of 1 second resulting in a 5.8% increase in the abandonment rate. Nearly 50% of viewers will abandon a video if it takes longer than 10 seconds to start playing.
• In a report by Conviva, a 1-second delay in video loading time can result in a 7% increase in video buffering and a 2.4% decrease in video start rate.

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Several factors contribute to high video startup latencies:
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Network issues

• Network latency: Slow or unstable internet connections can cause longer buffering times.
• Geographical factors: Distance from servers or peak time congestion can slow down access

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Content delivery

• CDN issues: Poor or overloaded Content Delivery Networks (CDNs) can delay content delivery.
• Server-side problems: High server load or inefficient backend systems can cause processing delays.

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Device and encoding

• Video encoding: Complex or on-the-fly transcoding increases video processing time.
• Device compatibility: Older or less powerful devices may struggle with video processing.

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Content-related factors

• Large file sizes: High-resolution videos take longer to load and buffer.
• Pre-roll ads: Lengthy ads or slow ad loading times can delay the main content.
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Now having understood what and why of EBVS, the burning question now is how do I measure it.

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How to measure and improve EBVS

At FastPix, we offer an advanced analytics solution to measure and optimize Exit Before Video Start (EBVS). By simply integrating our Data SDKs into your video player, you’ll immediately start seeing critical Quality of Experience (QoE) metrics on our intuitive dashboards. Our data shows that sessions with a QoE score above 95 had an EBVS rate below 2%, compared to a 15% EBVS rate for sessions with QoE scores below 65.

These metrics can be analyzed and compared across various dimensions, including geo-location, player-specific, device-specific, network-specific, and stream-specific data. This in-depth analysis enables you to drill down into the root causes of the high EBVS, helping you enhance the overall streaming experience for your users and eventually retain users by optimizing the content delivery.

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Example: Reducing EBVS with FastPix

More recently, a regional OTT streaming platform experienced a high EBVS rate due to lengthy pre-roll ads. By using FastPix's analytics tool, the platform identifies this issue and shortens ad durations, resulting in a 15% reduction in EBVS and a corresponding 10% increase in viewer retention. This explains how targeted optimizations based on EBVS metrics can enhance user experience and retention.

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To quantify QoE, we use a scoring system where a viewer session that exits before video start is assigned a QoE score of 50. For context:

• Ideally a QoE score above 95 is considered Excellent.
• Score between 80 and 95 is Good.
• Score between 65 and 80 is Average
• Score between 50 and 65 is Below Average.
• Below 50 is considered Not Satisfactory.

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Example use case:

Let’s say a streaming platform using our analytics tool notices a high EBVS rate during peak hours. By drilling down into the data, they discover that most of these exits occur on older devices struggling with high-resolution streams. The solution? Implementing adaptive streaming and optimizing video encoding for these devices. As a result, EBVS decreases, QoE scores improve, and user retention rates increase.
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How to implement EBVS tracking

Implementing EBVS tracking requires careful consideration of your video player’s architecture. Here's a step-by-step guide:

1. Integrate FastPix data SDK: Start by integrating the FastPix Data SDK into your video player. This enables real-time data collection on EBVS and other QoE metrics.
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2. Set up event listeners: Configure event listeners to track user interactions, such as play button clicks and video start events. (This step is automatically handled by the SDK)
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3. Filter out false positives: Implement logic to filter out accidental clicks and immediate exits, ensuring accurate EBVS tracking. (Exists before a timeframe of 1 second is considered accidental clicks as default)
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4. Analyze data: Use FastPix's dashboard to analyze EBVS data, identifying patterns and opportunities for optimization.
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5. Optimize settings: Based on the data, adjust your video player’s buffering thresholds, preload settings, and other parameters to reduce EBVS.
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Impact of improving EBVS on viewer engagement and user experience

• Firstly, enhancing EBVS directly leads to better viewer retention. By reducing delays in video startup, viewers can begin watching content more quickly, which helps keep them engaged and less likely to abandon the video before it starts. Faster load times ensure that users don’t leave out of frustration or impatience, thus improving retention rates.
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• Secondly, user satisfaction is significantly boosted by minimizing EBVS. A seamless video startup process contributes to a more enjoyable and smooth user experience. When users don't face long waiting times, they are more likely to have a positive perception of the platform and feel less frustration.
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• Improving EBVS also positively affects engagement metrics. With faster video starts, users are more inclined to stay and watch, which results in higher view counts and longer viewing sessions. This increased engagement is a strong indicator of a successful user experience.
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• Additionally, optimizing content delivery through reduced EBVS ensures better performance across the platform. Efficient content delivery contributes to a higher quality of service, which can be a competitive advantage in the streaming industry. Addressing issues related to EBVS helps in delivering content more efficiently and effectively.
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• Moreover, better EBVS can lead to increased revenue potential. For platforms that include ads, reducing EBVS can result in more ads being viewed, potentially increasing ad revenue. Additionally, satisfied users are more likely to continue their subscriptions and recommend the platform, boosting overall revenue and growth.
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• Lastly, improved analytics and insights are a significant benefit of monitoring EBVS. Platforms gain valuable data on user behaviour and potential issues, allowing for targeted improvements and strategic decisions. Understanding and addressing the root causes of high EBVS leads to more effective solutions and enhancements, further enhancing the user experience.
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Strategies to optimize EBVS in video streaming

Here are some effective strategies to reduce EBVS and improve video startup times:
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• Optimizing video startup time is crucial. Preloading critical video data before the user hits play can significantly reduce startup delays. Additionally, adjusting buffering thresholds to start playback with less pre-buffered content helps balance between quick start times and smooth playback.
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• Improving network performance is another key strategy. Utilizing a Content Delivery Network (CDN) with a global reach ensures faster content delivery and reduced latency. Continuous monitoring and optimization of network performance are also essential to address issues such as packet loss or slow connections.
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• Enhancing video encoding can contribute to faster startup times. Employing efficient video codecs, like H.264/AV1, and optimizing bitrates based on network conditions and device capabilities can improve loading speed and playback quality.
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• Server performance should be optimized by implementing load balancing across multiple servers to prevent bottlenecks. Additionally, improving server configurations and resource management helps handle high traffic and processing demands effectively.
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• Reducing initial load time involves optimizing the loading and playback of pre-roll ads. Minimizing the duration and complexity of these ads helps shorten the wait time before the main content starts.
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• Improving device and player compatibility is also important. Keeping video players and device firmware updated ensures compatibility with the latest streaming technologies. Implementing adaptive streaming protocols, such as MPEG-DASH or HLS, provides the best quality based on device and network conditions.
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• Error handling and recovery strategies are essential. Ensuring the video player can handle errors gracefully and provide alternative solutions or retry mechanisms helps minimize user frustration. Detailed error reporting allows for quicker identification and resolution of issues.
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EBVS vs. other video streaming metrics

While EBVS is critical, it's important to understand how it compares to other key video streaming metrics:
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• Rebuffering rate: Measures the percentage of time spent buffering during playback. While EBVS focuses on startup, rebuffering rate impacts user experience throughout the viewing session.
• Playback failure rate: Tracks instances where a video fails to start at all. This metric is more severe but less frequent than EBVS.
• Average bit rate: Indicates the quality of the video stream. Higher bit rates offer better quality but may increase startup times, influencing EBVS.
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By analyzing these metrics together, developers can gain a comprehensive understanding of their platform's performance.
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EBVS in different streaming scenarios

EBVS can vary significantly depending on the type of your streaming scenario:
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• Live streaming: EBVS in live streaming is mostly caused by delays in signal transmission or insufficient buffering, making it critical to optimize real-time data delivery.
• On-demand streaming: In case of on-demand video, EBVS is usually tied to video file size and encoding complexity. Preloading and caching strategies can help mitigate this.
• VR/AR streaming: In VR/AR environments, EBVS can result from high latency or insufficient hardware capabilities. Ensuring compatibility with VR/AR devices and optimizing content delivery is key.