ReadingUser Observability for Technical Support

User Observability for Technical Support


On Technical support for SaaS

In the realm of customer support, what’s core to SaaS companies is technical support. The efficacy of technical support is often the differentiating factor between a satisfied customer and a frustrated one; and is thus one of the biggest priorities for a founder or CCO.

Beyond L1-L2 support workflows like knowledge-based deflection or SOP-driven handling, L3-L4 technical support is most unique and hard to establish at scale for many SaaS products. Situations handled by L3-L4 support teams are by definition uncommon and thus non-trivial; since support for the most common customer problems are made repeatable with streamlined SOPs, runbooks and automations at L1-L2 levels.

Troubleshooting these customer situations can often be unique to each user. Traditionally, such troubleshooting relies on technical diagnostics and backend service logs/alerts. However, it’s difficult to relate this data with what happened in the users’ environment and the product behaviour that they faced.

Without the outside-in view of a user’s situation “as they saw it”, the inside-out data on application logs and metrics remains an incomplete story making technical support difficult and inefficient.

This in turn leads to -

Increased back and forth for user inputs

Remember being asked on a support ticket “what’s the error that you saw on the system?”, “what page did you face the error on?”, “what actions were you taking before it”, etc.

We’ve all been in this situation at some point or the other. And as technology companies engineering SaaS products, we all realize the need to do better — for our users and for ourselves.

Inefficient support collaboration and resolutions

For the most uncommon of technical customer issues, support engineers need to collaborate with architects, product and engineering folks.

When creating this 360° view of the user situation is so non-trivial, passing it along for further collaboration is prone to information loss at each communication hop.

Unpredictable root cause detection

Without enough context on the user’s situation, support engineers fall back to stitching a story with available data on service metrics, traces and logs joining these hard facts with guesswork and hypothesis. At this point, this is most dependent on the individual competence of the support engineer in play; and thus difficult to replicate.

The best support teams and user observability

It’s common knowledge that support teams optimize for resolution times and support efficiency as output metrics, to the north star outcome of user satisfaction.

As technical support teams realize their overlaps with product and engineering teams, the best in the business need to innovate on leveraging machines for optimum diagnosis context, repeatable troubleshooting playbooks and automation. Especially as “machine agents” become more prevalent, there's a growing recognition of the invaluable role user observability can play in enhancing support processes.

Understanding User Observability

User observability entails gaining insights into user interactions and behaviors within a software application, broadly revolving around three key elements -

User and Session Metadata for diagnosis context

User and session metadata provide crucial context for understanding the user journey and the specific circumstances leading up to an issue. This includes information such as user traits, device details, geographic location, and session timestamps. By integrating this data into support workflows, L3/L4 support teams can quickly contextualize reported problems, thereby expediting the troubleshooting process.

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Session Replays for Reproduction

Session replays offer a powerful tool for support teams to observe and reproduce reported issues exactly as they occurred for the user. By capturing user interactions in real-time, including clicks, inputs, and navigation paths; support agents can be given firsthand visibility into the problem environment. This not only aids in understanding the issue more comprehensively but also facilitates more accurate diagnosis and resolution.

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Usage Events and API Logs for Root Cause Analysis

Usage events and API logs provide a granular view of system activities, highlighting potential anomalies or errors that could be contributing to reported issues. These logs offer insights into backend processes, network requests, and system responses, enabling support teams to trace the root cause of problems more effectively. By correlating user-reported issues with relevant log data, support agents can identify patterns, pinpoint bottlenecks, and implement targeted solutions.

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Leveraging User Observability in L3-L4 Support

Support teams can leverage user observability for their support operations, specially L3/L4 support workflows, to affect the following outcomes -

  1. Faster diagnosis and resolution: With access to comprehensive user context, session replays, and detailed logs, support agents can swiftly identify the root cause of reported issues, reducing resolution times and minimizing user downtime.
  2. Enhanced collaboration: Democratized user observability can make collaboration between support teams and development/engineering counterparts efficient by providing shared visibility into user-reported issues. This alignment streamlines communication, accelerates issue resolution, and fosters a culture of user centricity.
  3. Improved user satisfaction: And finally, leveraging observability tools to gain a deeper understanding of user experiences allows for personalized, empathetic assistance; ultimately enhancing user satisfaction and loyalty.

Harnessing the insights provided by user context, session replays, and detailed logs, support teams can diagnose issues more accurately, resolve them more efficiently, and ultimately deliver a superior support experience.

This makes a strong case for the valuable opportunity available with user observability — for organizations to elevate the effectiveness of L3-L4 support, towards driving greater operational efficiency and user satisfaction.

Coming up soon, we’ll cover more details on how DevRev can help reshape L3-L4 support for your support team including with use of observability. Stay tuned!