TalentReef – Bulk Export Tool
Designing a self-service bulk export workflow for enterprise HR — from a support-dependent process to a scalable, validated experience.
Senior Product Designer
Mitratech
Self-Service Enterprise Workflows
HR Managers, Recruiters & Admins
The Challenge
A routine task had become a support dependency.
TalentReef had no self-service bulk export capability.
HR Admins managing hundreds of employee records had two options: export forms individually or contact Support and wait.
Behind the scenes, Support was manually processing export requests in batches of up to 200 records, creating a repetitive operational dependency that would only grow with usage.
The challenge wasn't simply to create a bulk action. It was to design a workflow that users could discover, understand, recover from when something failed and trust at enterprise scale.
Current State:
Individual export → repeated work → Support ticket → manual processing → waiting
Why It Mattered
The goal wasn't just fewer clicks. It was self-service.
Every routine task that requires a user to contact Support creates friction for both sides.
For HR teams, exporting employee data is part of recurring operational work across compliance, payroll and reporting workflows.
For Support, manually processing those requests creates a dependency that doesn't scale.
The opportunity was to move the task back into the product — while making the workflow understandable enough that users could complete it independently.
Success Criteria
Before designing the interface, I defined what success needed to look like.
The project began with a question:
How will we know this is working?
I translated the problem into measurable design goals:
Users should be able to discover the capability without Support assistance.
Core export tasks should be completed independently.
One-time and scheduled exports should be understandable within the same workflow.
Failure and partial-success states should clearly explain what happened and what users can do next.
The workflow should remain viable for 10,000+ employee records.
The experience should address the support dependency the feature was intended to replace.
Research & Discovery
Research caught the problem before development did.
I recruited active Admins and HR Managers through Pendo segmentation, excluding test accounts, and conducted two rounds of validation.
Round 1 — Usability
8 enterprise users tested the workflow.
The critical issue appeared within the first two interviews: users weren't finding the CTA.
The CTA wasn't visually hidden. The problem was that bulk export was a completely new concept, so users had no established mental model for where to look.
Round 2 — Workflow validation
I shifted the question from usability to scalability and ran workflow validation sessions with 6–7 internal Support and Technology stakeholders.
The focus became:
Can this workflow support 10,000+ employee records — and can it realistically replace the support request it was designed to eliminate?
Key Insight
"A feature that solves a real problem but can't be found is still a failed design."
Familiarity to the designer is not familiarity to the user.
Design Thinking
The design principles that shaped the solution
Define success criteria before designing.
Validate new concepts differently from redesigns.
Treat discoverability as a design constraint.
Validate scalability separately from usability.
Design enterprise self-service around user confidence, not just efficiency.
Design Decisions
Decision 01 — Make the primary action unmistakable
Round 1 testing showed that the original CTA wasn't being discovered, so I redesigned it using stronger visual hierarchy and language aligned with the ATS design system.
Decision 02 — Design the complete lifecycle
Instead of designing only the successful path, I mapped the complete export lifecycle across seven states:
Queued → Processing → Completed → Partial Success → Failed → Canceled → Paused
Decision 03 — Make partial failure actionable
A bulk operation can succeed partially. Instead of forcing users to contact Support, the result view shows exactly what succeeded, what failed and why — including a downloadable failure report.
Decision 04 — Design for engineering clarity
I documented interaction behaviour, state-specific actions, error conditions and edge cases so Product, Engineering and Support could align before development.
Systems/States/Scalability
A bulk action is actually a system, not a button.
The workflow needed to communicate what was happening before, during and after an export.
I therefore designed explicit system states and corresponding actions, feedback and recovery paths.
The final model covered:
Queued · Processing · Completed · Partial Success · Failed · Canceled · Paused
I also validated the workflow with internal stakeholders against scenarios involving 10,000+ employee records.
Final Solution
A self-service export workflow built around how Admins actually work.
The final experience supports:
One-time exports
Scheduled exports
Filtering before selection
Additional recipients
Explicit processing states
Partial-success handling
Downloadable failure reports
Recovery actions
Scheduling patterns such as last-day-of-month exports
The result is an end-to-end workflow designed to move routine export work from Support back into the product.
Screen flow/Interactions Defined for "Data Listing" Screens + Dev Notes
Outcome/Validation
Validated before development.
8/8
Users located the tool without assistance
7/7
Gave positive overall feedback after Round 1
10k+
Employee records validated during workflow validation.
#1
Avoidable support category — confirmed eliminated
Feature status: Active development
What I'd Do Next
Instrument Pendo analytics on CTA discoverability.
Compare first-time vs returning-user behaviour.
Run follow-up research with the Admin segment.
Measure actual support-ticket reduction after launch.
Reflection
What this project reinforced
Research doesn't just validate a design. Sometimes it changes what the design needs to be.
The most important learning here was that enterprise workflows need to be designed as systems — with discoverability, state management, failure recovery and scalability considered together.
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