Redesigning InvoiceCloud’s
Biller Portal Dashboard
Transforming Data confusion into confidence
↓32%
Customer support tickets on data discrepancies
↑40%
Projected dashboard engagement
↑80%
Confidence and trust in analytics data
Duration
3 months
Project Category
Team
Lead Designer (me)
Senior Data Analyst
Tools
Figma, Magic Patterns, Bolt.new, Figma Make, JIRA, Confluence
Summary
When billers lost confidence in the numbers driving their business, I led a redesign of InvoiceCloud’s Biller Portal dashboard to rebuild trust, usability, and insight. Through stakeholder research, data synthesis, and AI-assisted prototyping, we developed a modern data experience framework to guide the platform’s broader modernization effort.
01
Project Context
InvoiceCloud Billers
InvoiceCloud and The Biller Portal
InvoiceCloud provides a SaaS platform that simplifies bill payments for organizations and their customers. Billers are enterprise users who manage the delivery and payment of customer bills. They work within the Biller Portal, which lets users manage payments, metrics, and notifications and supports four primary roles: Finance, Customer Service, Operations, and IT.
Problem
After more than a decade without updates, the biller portal’s homepage dashboard had become inaccurate, confusing, and visually outdated, eroding trust across billers and internal teams.
02
Challenges
"I will be honest… and this isn't what you want to hear, but I log on and I immediately leave that page to look at other things."
— Tier 1 - Utility Biller
03
03-01
Internal Discovery
Meetings with Analytics, Customer Success, and Engineering revealed three key problems:
Data definitions were inconsistent or unclear.
Reported numbers often didn’t match internal analytics models.
Customer Success teams fielded frequent “what does this mean?” tickets.
03-02
Biller Interviews
Key insights include:
03-03
Reframing the Problem
From Personas to Functions
Prioritizing features by user role quickly broke down.
Teams varied too much across industries, and their responsibilities often overlapped depending on company size. To bring clarity, I shifted the focus from who users were to what they were trying to accomplish. Using the Jobs to Be Done (JTBD) framework, I reframed our understanding around functional impact, how users used data to get their jobs done.
This shift enabled the team to:
Identify cross-departmental needs or jobs
Prioritize features based on functional value rather than organizational hierarchy
From overlapping priorities based on roles...
Finance
Customer Service
Operations
IT
Customer and payment data (engagement and adoption)
Payment Operations
Reporting and Reconciliation
System Tasks (administration and integration)
04
Design
04-01
MVP Features Defined
Through workshops, we finalized the first release to include:
Five key graphs
Aligned with daily operational needs
Custom date filtering
ranging week, month, quarter, and year across all data sets
System and user alerts
For key actions and system status updates requiring biller action
Metric definitions
For clarity on data meanings
04-02
Prototyping with AI
I used tools like Magic Patterns, Bolt.new and Figma Make to quickly generate design variations, enabling faster internal review and alignment. These tools generated layout variations, reducing initial exploration time by 60% and inspiring several new interaction patterns.
04-03
Final Design
The redesigned dashboard emphasizes clarity, trust, and actionability.
05
Impact
↑ 40%
↓ 30%
↑ 80%
Short-term Results
Approved for development in Q1 2026, aligned with the platform-wide modernization strategy.
Increased internal stakeholder confidence in data visualization direction by 80% (survey results from Analytics team). 【METRIC OPPORTUNITY】
Long-Term Organizational Impact
Sparked investment in a biller research panel for continuous feedback.
Inspired leadership to integrate research checkpoints at every product phase.
Helped establish a model and investment for using AI-assisted design exploration across teams.
“This project helped us realize how disconnected our assumptions were from biller realities.”
— Product Leader, InvoiceCloud
06
Reflections
What I learned
This project marked a turning point in how our team approached design. Before, discovery was often intuitive rather than evidence-based, which limited our ability to validate assumptions or measure success. We went from rarely using research or metrics in discovery to embedding , data-informed research in every design cycle.
Partnering with the Data Analytics team was the catalyst. Their collaboration helped us connect quantitative data with qualitative insight, proving the value of structured discovery and evidence-based decisions. By showing what’s possible when research and data align, we helped shift our culture from reactive problem-solving to proactive, insight-driven design.
Organizational Impact
As a result, our work went beyond improving one dashboard. It helped influence the broader design culture at InvoiceCloud:
Discovery research and stakeholder interviews became timeboxed, required parts of the design process.
We secured additional support to grow the biller research panel, ensuring continuous user feedback at the feature level.
Teams across product and analytics began incorporating data-informed design into their own workflows.