CUSTOMER SUPPORT DASHBOARD T-MOBILE INTERNAL REPRESENTATIVES
CARE DASHBOARD | T-Mobile
Project Description
COMPANY
T-Mobile for Business
ROLE
Lead Designer
Product Designer
Workshop - Co-lead
OVERVIEW
PROCESS
User Research
Competitive Analysis
Wireframing
The Care organization relied on fragmented reporting tools that evolved without governance or alignment. Metrics were duplicated, dashboards varied by team, and effort often outweighed measurable impact.
This project focused on restructuring the analytics ecosystem into a modular, decision-driven dashboard framework that reduced subjective feedback loops and introduced scalable evaluation criteria.
CORE PROBLEM
Reporting evolved without system governance
Metrics lacked canonical definitions across teams
Visualization choices were inconsistent
Effort was not tied to measurable impact
Feedback loops were opinion-driven
The system optimized presentation — not decisions.
The goal was to understand:
Core responsibilities and day-to-day workflows
Tools currently used to support customer communication
Pain points within existing systems
Current Account Hub usage patterns
We also examined how Care interacts with adjacent roles (e.g., TEM, IT Equipment Manager, Business Owner) and how those dynamics impact issue resolution and customer outcomes.
THE GOAL
Reporting evolved without system governance
Metrics lacked canonical definitions across teams
Visualization choices were inconsistent
Effort was not tied to measurable impact
Feedback loops were opinion-driven
The system optimized presentation — not decisions.
Card sorting Effort vs. Value
Widget Audit & Gaps
As part of discovery, Care audited existing dashboard widgets to determine:
Which widgets supported their needs
Which were missing or incomplete
Opportunities for new or revised components
Special attention was given to collaboration features such as Notes:
Visibility and privacy controls
Sharing permissions
Cross-role accessibility
TEAM
UX/UI
Accessibility
Development team
3rd Party Vendor
Project Management
Business Owners
Voting on priorities
Workflow & Escalation Context
We explored:
How Care supports customers when they are blocked from achieving goals
Common issue resolution patterns
What information Care needs immediate access to during live interactions
Process
Iteration: From Exploration to Convergence
While designing the dashboards, in testing we realized that Salesforce could not handle the backend. Even though the goal was to surface 4 customizable dashboards for different users, we opted for speed. A pain point that had hurt the legacy platform.
Round 6 comp
What changed between round 5 & 6
The back end of Salesforce could not handle the data API ingestion and was lagging the site. So instead of have 4 customizable dashboards, we simplified and made individual landing pages for the widgets that lived on the dashboard.
So instead of living on the dashboard they got push from L0 to L1 landing pages.
Round 6 comp
Round 5 comp
Solutions
01 | Discovery — Exposing Structural Gaps
I conducted stakeholder interviews and workflow mapping to understand how reporting decisions were made across teams. Findings revealed:
Redundant metric definitions
Disconnected dashboard ownership
Charts selected based on familiarity, not suitability
No clear tie between reported metrics and operational decisions
High effort spent producing low-impact views
The core issue was not UI.
It was structural misalignment.
02 | Synthesis — Canonicalizing Pain Points
Rather than accumulating feedback, I consolidated findings into a canonical pain-point framework.
This reduced duplicated narratives and allowed us to isolate systemic patterns instead of isolated complaints. Key themes:
Effort vs Impact imbalance
Visualization misuse
Lack of governance standards
Reporting without accountability loops
This created alignment before design began.
03 | Framework Definition — Designing the Modular System
Instead of redesigning screens, I designed:
A modular dashboard architecture
Standardized visualization patterns
Reusable card structures
Clear metric grouping logic
Evaluation criteria for chart effectiveness
This shifted discussion from:
“What looks right?” TO “What drives decisions?”
04 | Validation — Prototype & Iteration
An internal prototype was developed to:
Test modular consistency
Validate metric grouping logic
Reduce chart variation
Demonstrate scalable governance
Stakeholder feedback showed increased clarity in:
Performance visibility
Effort allocation
Data interpretation consistence
Although the full implementation was not completed, the framework established a scalable model for analytics governance.