Finding Top Tasks

A workshop with 12 participants (partners, stakeholders, and internal users) revealed three critical tasks:

  1. Transactions (35% priority)

    • Managing transaction data

    • Running reports

    • Filtering results

  2. Virtual Payments

    • Processing payments

    • Payment status tracking

  3. Transaction Exports

    • Downloading data

    • Generating reports

This analysis helped prioritize the transaction management system as our primary focus for redesign, specifically the filtering and export capabilities.

Usability study & User interview

Focusing deep into the transaction top task, a usability run of the existing transaction user journey was done with internal users to gather insights and creating assumptions. Which were collected into a journey map to showcase how the whole experience of exporting transactions was for the users.

Journey Map

The whole experience of the usability study was mapped in a user journey to look at the overall experience.

Pain Points

  1. Complex filters leading to user avoidance

  2. Hidden frequent tasks and inefficient navigation space

  3. Unreliable export system causing support dependency

‘’ I just export the whole data in excel, instead of messing with filters. The system (filter selection) is way too complicated to find and select. “

Assumption vs Reality

Initial Assumption:

  • Slow performance was causing transactions to be exported

Key Finding:

  • Partners avoided filters due to complex UI, and poor discoverability.

  • Users exported entire lists for manual Excel filtering.

  • Root cause: Poor filter UX, not backend performance.

Old filter design

Recap

Major Problems Discovered:

40+ second delays for data access

2,000+ monthly support tickets

5-6 hours weekly on manual tasks

Complex filter interface

Hidden frequent tasks

Key Findings
Task Priority

  • Transactions highest priority (35%)

  • Virtual Payments and Transaction Exports critical

  • Partners focused on transaction management

User Behaviour

  • Avoiding filters due to complexity

  • Exporting full data for manual filtering

  • Heavy reliance on support for basic tasks

Critical Insight

"I just export the whole data in excel, instead of messing with filters."

The core issue wasn't system performance but poor user experience, leading to inefficient workarounds and manual processing.


Design Solution

Improving data accuracy by having different ways to access merchants.
Streamlining the transaction return and export flow.

New user journey

Problem Prioritisation

Cross-functional workshop with Product Manager, Engineering Lead, and Support Team Lead to map issues on Impact vs. Effort matrix.

Quick wins identified → navigation and filter improvements prioritized for immediate development, with high-effort features planned for phased delivery.

This focused approach helped create realistic timelines and ensure stakeholder alignment.

Prioritisation Matrix

Success Metrics

To measure the impact of the solution on the final partner experience and business. Some success metrics were defined. Here are some success metrics defined before the redesign:

  1. Task completion time.

  2. Data export success rate.

  3. Support query generation rate.

  4. Overall partner satisfaction (qualitative).

  5. Feature usage rate.

Wireframes

Some Wireframes tested with users

Problem 1: Limited self-service by users

Navigation redesign

  1. Better interaction with the table. To give user more space to view larger tables.

  2. Simple navigation structure: By keeping most used features upfront and using sub-menu for items which are less frequently used.


Navigation iterations

Merchant selection redesign

Improving efficiency of the partner being able to find transactions fast include the process selecting relevant merchant (by either selecting merchant ID or site reference.

Problem 2: Users wasting 5-6 hours on manual filtering of exports.

Filter redesign

  1. Reduced export transaction time with easy to discover and apply filters.

  2. Reduced support ticket by 60% which were mostly about long export times.

  3. Overall increase in task accuracy.

New Filter Designs (Left) vs Old Filter Design (Right)

Problem 3: High export times due to poor performance.

Problem 4: 2000+ export related queries due to session timeout

Export redesign

In the initial assumption, partners were avoiding the use of filters because of its poor UX. So they were exporting whole transaction data only to use offline tools like excel to filter manually.

To tackle this issue, a 2-step export and download process was proposed.

  1. The export files are prepared in the background, even if the user logs out or the session times out. This helped partners to save time.

  2. It also reduced the support tickets, since export of the transaction data has the highest raised tickets.

Export design process

Result & Impact

  • Task completion: 40s → 12s (70% faster)

  • Support tickets: 2,000+ → 800 monthly (60% reduction)

  • Export success: 95% reliability

  • Manual work: Eliminated 5-6 hours weekly per partner

Key Lessons:

  1. Assumption vs Reality

    • Initial focus on performance was misplaced

    • User behaviour revealed UX as core issue

    • Simple solutions often outperform technical fixes

  2. Research Impact

    • User quotes revealed critical insights

    • Journey mapping exposed hidden pain points

    • Cross-functional workshops enabled better prioritization

  3. Design Process

    • Start with user pain points, not technical assumptions

    • Prioritize high-impact, low-effort solutions

    • Test solutions with real users early

  4. Next Steps

    • Apply successful patterns to other features

    • Continue monitoring via Heap Analytics

    • Regular feedback collection from partners

70%

Task Completion Time Improved

60%

Reduction in Support Tickets

95%

Data Export Success Rate

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