Quickly address OTT streaming issues by monitoring with our Roku agent

If you’re an engineer who creates streaming applications for Roku’s platform, it's likely you've lacked visibility to ensure that those apps—and the devices they’re on—perform optimally. But that all changes with the New Relic Roku agent, which creates the first comprehensive Roku observability solution on the market. Now you can collect your Roku telemetry data to measure and improve network connectivity, viewer accessibility, and video quality. Having aggregate metrics makes it easier to analyze large quantities of data. The curated New Relic UI gives you interactive data visualizations that provide instant visibility so you don't need to manually build and run queries.

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The importance of observability for Roku applications

Online video streaming is already becoming the standard for how consumers access their favorite media and entertainment content. What you may not know is that 78% of U.S. households subscribed to at least one video streaming service back in 2021. As for Roku, the platform reached an all-time high of 61.3 million monthly active users earlier this year and with it a 50% market share of the North American connected TV (CTV) market. This alone makes optimizing Roku application performance more important than ever.

On top of that, the risk of subscription churn and lost advertisement revenue is already high due to the diverse selection of streaming services available on the market. You don't want to add unnecessary problems, such as downtime or a poor user experience, to the equation. 

But when building Roku streaming applications, you typically haven’t had the visibility needed to quickly detect and resolve issues such as channel crashes, HTTP request errors, latency issues, and rebuffering. Instead, you’ve probably had to wait for customer feedback and testing in order to notice any disruptions. Even then, it’s difficult to pinpoint whether the issues pertain to the application itself, the Roku device, or the network.

Own your Roku application with visibility into performance 

The New Relic Roku agent gives you a wide range of functionality to address the significant blindspots that exist with your Roku streaming applications. It provides application and device-level analytics that include HTTP requests and errors, the video quality of experience (QoE), and app restarts so you can better diagnose issues. It also gathers information on Roku devices, such as the model and the amount of memory used. 

Viewer journey tracking helps you quickly identify critical bottlenecks and the areas where they occur. Our upgraded agent now comes with a metrics API that collects performance metrics for aggregate analysis right alongside granular events. This allows you to better focus on the information that is most relevant to your use case when sorting through large quantities of data. And analyzing Roku performance data is quicker with preconfigured, interactive data visualizations. You get immediate, comprehensive observability without having to manually run queries or create your own dashboards.

• HTTP requests and errors, which can be manifestations of ISP or CDN issues, or unwanted changes to the application or backend services. Faster identification and understanding of these issues is critical to maintaining a positive user experience. 
• Video QoE analytics that correlates video quality issues with the rest of the streaming architecture and video playback sessions with backend systems. 
• Viewer journey tracking helps you inspect customers' video playback experience, including the path to reach their content and the time needed to complete critical actions. Pinpoint impacted services along with where bottlenecks and drop-offs occur.

With all your Roku performance data in one platform, you can detect and resolve over-the-top (OTT) streaming issues that impact your viewers’ ability to access their video content. Detecting and resolving these issues will help you reduce subscription churn, increase revenue, and maintain a positive brand image.