Player Diagnostics: Enhancing Your Netflix Experience

By: Ben Toofer, Baskar Odayarkoil, and Chandrika Kasi

Overview

The TV player team at Netflix leads in playback innovation, continuously developing cutting-edge playback features. As the backbone of the Netflix application, we collaborate closely with various cross-functional teams at Netflix to ensure an exceptional viewing experience. We strive for a high level of playback quality even on devices that have cpu, memory, and network constraints. In order to provide a great playback experience and scale across millions of devices, we also need to handle different playback errors and get the users back to streaming. This post describes the team’s work on building a playback diagnostics tool that dissects playback failures and finds an automatic resolution if possible.

What’s the problem?

Through extensive customer service research and field data, we’ve found that one of the most frustrating issues users face is encountering disruptive, ambiguous errors while watching content. Such errors disrupt the viewing experience, preventing users from enjoying their desired content. As a result, viewers often have to restart the application to recover, abandon playback altogether, or contact customer service for help. This leads to a negative experience with no clear resolution. Given the wide variety of devices and the inevitability of some errors due to hardware variations and aging devices, it is our responsibility to be proactive.

The success of our network diagnostics tool, which effectively helped both our customer service team and users proactively resolve network-related issues, inspired us to develop a similar tool for diagnosing playback errors. By leveraging these insights, we aim to not only assist users in real-time but also to learn from these issues and continuously improve our service.

What and why is Player Diagnostics needed?

Player Diagnostics is a vital feature designed to enhance the Netflix streaming experience by automatically resolving playback issues in real-time. It’s simply a procedure in which the app detects a playback error, and the player runs through a series of tests that modify the playback configuration to determine a possible resolution for the user.

Player Diagnostics aims to resolve these errors swiftly, ensuring minimal disruption and allowing users to return to their content without significant delays. By detecting and diagnosing errors proactively, the feature reduces user friction with the application and enhances overall satisfaction with the Netflix service. Additionally, it provides Netflix with valuable data-driven insights into common issues, enabling continuous improvement of the streaming technology. The feature also empowers users to troubleshoot issues themselves, by prompting them with the option to run the automated diagnostics tool. This reduces the need for customer support intervention and assists support teams with more accurate and effective solutions when necessary. In summary, Player Diagnostics ensures a smoother, more reliable streaming experience for all Netflix members.

Challenges

To determine the diagnostics tool’s success rate, we analyzed the overall reduction in playback errors. Given the small fraction of sessions that encounter errors, we needed a detailed analysis of playback data to understand how many sessions successfully resumed streaming after diagnostics.

We employed a custom approach to segment data from playback sessions eligible for diagnostics and correlated them with viewing hours post-diagnostics. This, combined with a deeper analysis of playback errors, allowed us to assess the impact of error resolution using this diagnostics tool.

Our findings showed a significantly higher likelihood of users returning to successful playback after running diagnostics, demonstrating the tool’s effectiveness in resolving issues and enhancing the user experience. Interestingly, there was a slight increase in playback errors, completely due to increased user engagement post-diagnostics. We found that diagnostics reduced the aggregate probability of encountering an error in subsequent sessions by approximately 19%.

This insight confirmed that Player Diagnostics not only encourages user engagement but also effectively reduces the likelihood of subsequent playback errors, thereby enhancing the overall streaming experience.

Results

We observed a significant improvement in the user’s return to streaming after running the diagnostics. Overall, we saw a total lift in users returning to streaming nearly quadrupled. We recognized this as a success because users returned to streaming rather than restarting or leaving the application.

Key Takeaways

• Return back to streaming: Providing a solution for resolving playback issues led to users returning to watching content.
• Reduced errors: Users who experienced an error during playback and ran diagnostics had a reduced chance of experiencing another error.
• Crash reduction: The number of crashes decreased after a playback error, which is attributed to the diagnostics resolving a corrupted state.
• High-resolution rate: We were able to resolve the majority of issues, fast-tracking users back to their desired content.

Conclusion

The introduction of Player Diagnostics has significantly enhanced Netflix’s ability to deliver an exceptional streaming experience. By proactively addressing playback errors and equipping users with tools to resolve issues in real-time, we have effectively reduced user frustration and improved overall satisfaction. Our research and experimentation demonstrated that users are more likely to re-engage with content after running diagnostics, indicating that the feature successfully encourages continued streaming even after encountering issues.

As we move forward, we will continue to refine and enhance Player Diagnostics by incorporating new tests and optimizing existing ones to further improve system reliability and user satisfaction. Through our commitment to innovation and user-centric design, Netflix will continue to set the standard for streaming excellence, ensuring that every member enjoys a seamless and high-quality viewing experience.

Acknowledgments

We would like to acknowledge the many members of the Netflix consumer product, platform, and data science teams who have designed, implemented, and tested this feature. In particular: Chris Hourihan, Angela Israni, Ethan Venitz, Louis Garcia, Risa Hiyama, Esther An, JP Abello