Aug - Sep 2022
Complaints are often treated as isolated incidents—but in reality, they unfold as multi-stage journeys shaped by interactions, systems, and emotions. This project reframed complaints as a service journey, uncovering systemic breakdowns and transforming a fragmented, ticket-based model into a contextual, SLA-driven system—reducing resolution time and improving efficiency, clarity, and overall experience.
The existing complaints system was built on vague structures, making it difficult for users to find relevant support options and causing inefficiencies in agent workflows. This led to delays in resolution and lower satisfaction for both customers and couriers. These issues are emerging due to:
MRSOOL Customers and Couriers using the mobile app during their order journey.
How might we surface the right complaint reasons at the right moment in the user’s journey, making support easier to access and more effective — while aligning with the new support structure and policies?
Transformed complaints from a fragmented, ticket-based model into a contextual, SLA-driven system, reducing resolution time to minutes and improving efficiency, clarity, and user experience.
User research revealed that complaint reasons were fragmented, duplicated, and difficult to navigate, leading to confusion and inefficient complaint submission. These issues were further amplified by evolving support policies and SLAs, highlighting the need for a more structured, context-aware complaint system.
Analyzed historical support data to understand how users interacted with complaint reasons in the app support. By clustering and filtering data based on frequency and usage patterns, key issues were identified:
These insights informed the restructuring of the Complaint Tree.
Treating complaints as a journey—not a single touchpoint.
Gathered the most frequently used complaint reasons from historical data to identify outdated, duplicate, or irrelevant entries.
“Contextual complaints” allow us to understand complaints within their full service context—across interactions, systems, and user expectations—rather than treating them as isolated tickets.
Defined a contextual complaint framework where complaint options adapt based on:
This ensures users only see relevant complaint reasons at the right moment, reducing friction and improving clarity.
Collaborated with Product to define a priority matrix and align complaint types with SLA expectations based on:
This enabled more consistent and efficient ticket handling.
Used Notion to document and organize the filtered complaint reasons within the new tree structure, assigning conditions and context for each reason to support configuration and implementation.
Rebuilt the Complaint Tree by:
The result is a clean, scalable system aligned with real user scenarios.
Mapped complaint reasons across the order journey using status indicators, ensuring each complaint appears:
This connected complaints directly to real user flows, rather than static categories.
Structured the Complaint Tree into a development-ready configuration, including:
Enabling smooth handoff to engineering and scalable implementation.
Customer support operations were fragmented across workflows, tools, and teams—leading to inconsistent resolution, operational inefficiencies, and poor customer experience. This project focused on defining a structured roadmap to align support as a scalable, high-impact service.
Facilitated cross-functional workshops to deconstruct how support operates across user journeys. Transformed fragmented, tribal knowledge into a structured system of root causes, decision logic, and resolution flows, revealing gaps between product experience and operational reality.
Let’s talk and design whats next.
