Roku is a popular streaming platform that allows users to access a wide variety of TV shows, movies, and other types of online video content. With its easy-to-use interface and affordable hardware, Roku has become one of the most popular streaming platforms in the world. At the end of 2022, Roku reported having over 70 million active users, with content available through 350+ channels.
This large selection of Roku channels fosters intense competition, requiring publishers to offer highly compelling services that can attract and retain viewers. However, if you’re currently a content provider who hosts a Roku channel, it can be challenging to troubleshoot and optimize the user experience (UX) for existing and prospective subscribers. Roku developer tools help with the beginning of this process. They enable your engineers to spot problems, such as memory leaks and deprecated components, or to capture performance profiles for a channel on a developer’s local device. To go further into problems such as understanding user actions leading to an error, or performance issues leading to users dropping out of a channel, teams also need tools that give details about individual Roku user sessions and shed light on past errors that users have encountered. To provide this deeper kind of UX visibility, Datadog Real User Monitoring (RUM) now includes a new library that enables monitoring for Roku channels.
In this post, we’ll show how Datadog can help you:
The Roku platform is designed specifically for streaming over-the-top (OTT) content, both video and audio, to a wide range of devices. Delivering the media to these devices is a complex operation, involving the coordination of many separate components between the backend servers delivering the content and the Roku device itself. And owing to this complexity, it’s crucial for teams troubleshooting customer issues to be able to analyze every part of the Roku technical stack. Otherwise, they risk missing the complete picture of the health of your channel. Inadequate visibility of this type can allow poor performance to go unnoticed, which in turn can lead to customer churn.
Datadog RUM shines a light on previously hidden components of the Roku technical stack, automatically tagging Roku user session information by model and OS version and making it easy to compare design efficacy across devices. If you notice different conversion rates for specific devices, you can drill down into sessions to investigate the issue. Or, if you receive complaints from users after deploying a new version of your channel, you can use session information to determine exactly where they’re running into difficulties.
RUM also provides insights that can help you streamline your UX. For example, if on your channel you recently added a call to action (CTA) to view the latest videos, you can use RUM’s funnel analysis feature to evaluate the conversion rate of that CTA. This funnel helps you visualize the percentage of users that complete a specific workflow, allowing you to pinpoint at which step you tend to lose customers.
An example funnel graph is shown below, indicating that customers are leaving your channel without watching the latest videos. You might begin troubleshooting this issue by reviewing individual user journeys and discover, for example, that slow resource loading on certain screens is preventing users from seeing the element displaying your CTA. In that case, you might then strategize ways to make resources load more quickly for the problem screens in question.
Error tracking will automatically aggregate crashes and errors from your Roku channel, helping you find the issues most impacting your users. If for example you receive an alert indicating that users are experiencing errors when they try to watch a specific video, you can investigate the issue by viewing the specific sessions in which the error has appeared. On the performance timeline for one of these sessions, for instance, you might notice that users are running into errors on the playback screen, so you could click to inspect that particular screen. Within that user session, you might then see that a local cache operation failed, pointing you to the source of the error this user experienced.
You can also use RUM with Datadog APM to help you quickly detect and diagnose QoE issues, such as slow video load times. If you’ve configured RUM to link with APM, Datadog will automatically connect frontend requests to your app with their associated backend traces. This allows you to debug the source of user performance issues regardless of where they originate in your stack. By viewing the correlated trace from this RUM view, for example, you might detect that a third-party API was having trouble responding to your Roku channel’s request to display an advertisement. In this instance, you would likely want to contact the API service’s support team to see whether they’re aware of any issues involving Roku devices.
Datadog RUM gives you detailed session and performance data for your Roku channels, helping you resolve issues and optimize your UX. With our Roku integration, you can analyze sessions and funnels to better understand user behavior, as well as leverage APM traces to identify root causes and ensure your users are receiving the smoothest possible experience.
If you’re an existing Datadog customer, you can install our Roku library to get started. Or, if you’re new to Datadog and want to get deep visibility into your Roku channels, sign up for a 14-day free trial.
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