As mobile becomes the de facto way of interacting with brands and services, how engineers think about observing their apps is changing.
Backend service metrics, traditional drivers of SLOs for performance monitoring, simply won’t cut it anymore, as they provide little true insight into what end users are experiencing. Why are customers abandoning a purchase flow? Why is it taking so long for them to log in? What’s making them rage-quit your app?
All of these are questions that require client-side observability to answer, and mobile-specific SLOs to effectively track.
Kate Holterhoff, Senior Analyst at RedMonk, and Andrew Tunall, President and Chief Product Officer at Embrace, explore this topic in greater detail via a RedMonk Conversation.
In this discussion, they cover:
- Some of the critical gaps in observability for the mobile frontend. Where traditional DevOps tools excel, and where they fall short – specifically, in capturing the nuances of user interactions on mobile devices.
- The relatively new concept of mobile SLOs. Like traditional backend SLOs, these metrics help engineering teams track and maintain performance thresholds, as well as prioritize reliability work. However, mobile SLOs focus on measuring and improving the actual user experience, going beyond technical metrics that look at individual services and endpoints. For instance, a Mobile SLO might track the time it takes for a user to successfully search for a product in an e-commerce app, considering all the contributing factors from network connectivity to app performance.
- The unique challenges of the mobile environment that make developing effective SLOs tricky. These include delayed data, wide device variability, rapidly changing network conditions, the influence of third-party services, and unpredictable user behaviors. Traditional observability tools typically struggle to handle these mobile-specific complexities. That’s why choosing the right solutions that focus on a user-centric approach to data collection and analysis is critical.
- Future trends in SLO development. These include predictive SLOs, which leverage AI and machine learning to anticipate and prevent potential issues before they impact users, as well as dynamic SLOs, which adapt to real-time changes in traffic and user behavior to ensure optimal performance during peak periods.
Catch the full conversation below:
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