Enter password to view case study
CASE STUDY
Customer support environments often struggle with complex interactions that require coordination between multiple teams and adherence to policy frameworks. Our legacy infrastructure relied on fragmented, manual processes. Tracking progress across different teams required significant effort, and essential data was scattered across disparate systems. This led to high cognitive load for support agents, delayed resolution times, and difficulty in identifying the root causes of recurring issues.
THE PROBLEM
Agents couldn't see when issues were actually resolved
No clear ownership or accountability when a problem spanned multiple touchpoints
Context was fragmented across chats, tasks, and other entities, so agents lost the thread
No way to track time spent or resolution status
No structured way to verify resolution or measure satisfaction
MY ROLE
I led the design strategy to consolidate these fragmented workflows into a unified Case Management system. My objective was to transform how the organisation tracked customer issues, ensuring that agents had immediate, holistic visibility into case history to improve resolution speed and quality.
MY APPROACH
My approach focused on systemic design rather than isolated task management
Discovery: I conducted ethnographic shadowing of support teams across various business units, understanding the user profiles that would be using the solution day to day. This allowed me to map constraints, identify pain points, and distinguish between processes that were standardised and those requiring bespoke handling.
Framework Development: Rather than overwhelming the team with a completely new process, I developed a case entity model. This framework linked disparate elements, such as customer dialogues, transaction logs, and external files, into a single, navigable source of truth
Iterative Design: I worked closely with engineering to maintain a tight feedback loop. We prioritised a low-friction rollout, starting with a minimum viable product that focused on adoption before layering in automation. This allowed agents to become comfortable with the case structure before we introduced advanced AI-driven tools.
Defining the solution
We developed a case-building system which allows users to connect all moving parts to one entity, providing full context and understanding of a categorised customer problem. This platform provides analytics on problem areas, helping us identify which workflows cause friction. We can now track metrics such as agent touchpoints, required sub-tasks, and the duration of cases before resolution. The system centralises artefacts from internal tools, regardless of where they originated, including chat messages, transactions, and file attachments.Testing & Iterations
During the defining phase, I built operational workflows on what it would look like on production, in addition to writing a PRD to sync with my engineering team to ensure we were all aligned when it came to implementation. The PRD included key information around the background of the project, user personas, product requirements and entity relationships we would need to successfully build the solution.
OPERATION WORKFLOW & WIREFRAMES
TESTING, FEEDBACK & ITERATIONS
Agents were accustomed to managing dialogues, so the new system introduced a slight change to their workflow. I build a quick prototype using claude code and my design system that I was able to use and test with a small section of representatives within the team with dedicated process flows to get them familiar with the case interface and how to connect artefacts.
We then released the initial version to production, so agents could get some familiarity with the concept of case creation without imposing immediate operational and process changes to specific roles. In subsequent releases, we introduced an LLM-powered summary feature, providing agents with instant context when they were new to an existing case in addition to automated workflows and case actions.
Customer feedback
Agents were accustomed to managing dialogues, so the new system introduced a slight change to their workflow. I build a quick prototype that I was able to use and test with a small section of representatives within the team with dedicated process flows to get them familiar with the case interface and how to connect artefacts.
RESULTS
The implementation delivered significant improvements in operational efficiency:
Operational Scale: The workflow successfully managed over 47,000 cases with a 77% resolution rate since its release.
Enhanced Resolution Speed: We achieved a 5x improvement in resolution time, reducing the median time-to-resolve from approximately 39 days to 7 days over a one-year period - which gave our customers a more reliable customer experience.
Workflow Efficiency: By centralising artefacts, such as communications, transaction history, and associated files, the CX support team reduced the need for fragmented manual follow-ups, unnecessary time to dig around to understand the case context and provided agents with immediate, actionable context for complex issues.
RETRO
Following the implementation, I lead a small focus group to facilitate a retro where we discussed what went well, whats not going so well, any feedback or improvements we can make to the solution and what new features they'd like to see next.
