Goodwill
Relay: reimagining the donation processing workflow with voice
Product Designer
6 months
UX Research · Interaction Design · UI Design · Prototyping · Voice Interfaces · Usability Testing · Service & Workflow Design
Brittany Jain, Will Pan, Casey Potrebic, Yusen Zhang, Holly Zhu
Goodwill of Southwestern Pennsylvania
Project Overview
Problem
Goodwill processors inspect, categorize, price, and tag millions of donated items each year. But repetitive software interactions interrupt this physical workflow, requiring processors to move between clothing racks and shared computers and repeatedly enter information into a legacy POS system.
These inefficiencies slow processing and limit how quickly donated clothing can reach the sales floor.
Goal
Design a faster processing experience that reduces cognitive load and physical effort while helping processors tag clothing quickly and accurately.
User Research
Understanding processing from the inside
We started by immersing ourselves in processors’ day-to-day work. Across 4 store visits, 8 processor interviews, 8 think-aloud sessions, and 36+ items processed ourselves, we observed not only what processors did, but where their workflow created unnecessary effort.
- 4
- store visits
- 8
- processor interviews
- 8
- think-aloud sessions
- 36+
- items processed ourselves
Five points of friction emerged
Processors often decide how an item should be categorized and priced while initially handling it, but have to remember those decisions until they reach the POS later.
Multiple processors may be working at once with only a limited number of softlines desktops, forcing processors to wait for a station.
After filling a rack, processors push it across the production floor to enter items into the POS, adding travel time and congestion.
Logging a single item requires processors to navigate multiple screens and make many taps.
By the time an item is ready for the sales floor, a processor may have handled it three separate times: sorting, logging, and tagging.
The opportunity wasn’t simply to make data entry faster. It was to eliminate the separation between making a decision and recording it.
Problem Statement
“How might we reduce cognitive load and physical effort while helping processors tag clothing faster and more accurately?”
Our research led us to one guiding principle:
Let processors enter information at the moment they make a decision—without leaving the rack.
Strategy
What if processors didn’t need their hands to use the POS?
Because processors’ hands are constantly occupied inspecting, hanging, and tagging garments, we explored ways of capturing information without interrupting their physical work.
We compared three input modalities with processors using storyboards: smart glasses, voice, and tapping on a tablet.
| Modality | Mental effort | Physical effort | Preference |
|---|---|---|---|
| Voice | Lower | Lower | Strongest |
| Tablet tapping | Higher than voice | Higher than voice | Middle |
| Smart glasses | Low | Low | Lowest |
Voice emerged as the strongest direction. Processors perceived it as requiring less mental and physical effort than tapping, while smart glasses received the lowest preference ratings.
Could voice actually work in a noisy Goodwill backroom?
We tested microphone types and transcription tools in noisy, warehouse-like environments. Even in a large, echoing basketball court with people playing, our strongest transcription & microphone combination achieved approximately 98% accuracy, demonstrating that voice was technically viable.
Later, we evaluated cloud and local speech-to-text models using Goodwill’s actual vocabulary. Deepgram achieved 97.6% accuracy, while Vosk provided a 90.8% accurate local fallback, giving us a path toward both performance and reliability.
So we determined voice could capture the processor’s decision. Now we had to figure out how to get that information into Goodwill’s existing system.
Explorations
Exploration 1 — Replace the legacy POS
Our first instinct was to redesign the experience entirely.
The existing Solutions interface required processors to navigate multiple screens and repeatedly tap to enter a single item. We explored a simpler, voice-first interface that could replace those interactions.
But Solutions wasn’t just an interface. It was connected to inventory data, production tracking, checkout, and other backend systems.
Replacing it would mean replacing critical infrastructure—not just redesigning a screen. So we decided to work with the existing system instead of replacing it.
Exploration 2 — Integrate directly with Solutions
If we couldn’t replace Solutions, we wanted to connect our interface directly to it. That path quickly hit another constraint: Solutions provided no direct or public API access.
No public or direct API connectivity available
Rather than treating that constraint as a dead end, we reframed the problem:
If processors can operate the POS by tapping buttons, could software tap those buttons for them?
We built a browser proof of concept using UI automation. A processor could speak an item’s information, and the system translated that input into the interactions required by Solutions. The prototype proved that we could automate the legacy workflow without requiring API access.
Exploration 3 — A voice-first replacement interface
Next, we combined voice and automation into a dedicated fullscreen interface. Conceptually, it worked. Technically, it didn’t.
For UI automation to reliably interact with Solutions, the legacy application needed to remain in the foreground. Our fullscreen interface competed with Solutions for focus, breaking the automation.
Only one application can hold focus, so the automation stopped.
Pivot — From replacement interface to companion
Instead of covering Solutions, we designed a lightweight widget that sits directly on top of it.
The widget gives processors access to voice controls and feedback while allowing Solutions to remain visible and operable underneath. Processors no longer have to switch between applications, and the UI automation can continue working in the background.
Redesigning the Workflow
Solving the interface was only part of the problem. Our original research showed that processors interacted with the same garment three separate times: sort → log → tag.
Inspect, decide, hang on the rack
Handle again at the POS to enter it
Handle a third time to attach the tag
We asked whether Relay could eliminate that backtracking entirely. Instead of processing items in batches, we tested a tag-as-you-go workflow, where processors completed each item from beginning to end before moving on to the next.
The result surprised us.
That finding changed Relay from an alternative input method into a new processing workflow.
Final Designs
Introducing Relay
Relay is a voice-operated tagging assistant that allows Goodwill processors to log and tag clothing while continuing to physically process it.
Rather than asking processors to adapt to a new system, Relay sits alongside Goodwill’s existing POS and automates the interactions that previously required their attention.
Process directly from the rack
A Windows tablet sits beside the processor's station, eliminating repeated trips to a shared desktop.
Speak while handling garments
Processors can say item details aloud while inspecting and hanging clothing, capturing decisions at the moment they're made instead of remembering them for later.
Automate the legacy POS
Relay translates spoken information into UI actions inside Solutions, allowing processors to continue handling garments while the system performs repetitive POS interactions on their behalf.
Tag as you go
Once an item is logged, its tag can immediately be printed and attached. The processor completes the garment before moving to the next one, reducing touchpoints from three to one.
Together, these features allow a processor to complete an entire rack without leaving their processing station.
Retrospect
Impact
We tested Relay in-store for 23 hours as part of a summer that included 9 store visits, 15 research experiments, and 18 prototype iterations.
- 23
- hours of in-store testing
- 9
- store visits
- 15
- research experiments
- 18
- prototype iterations
increase in production speed — a 112% increase in items processed per hour.
items processed per hour, single-processor pilot
More importantly, the processor using Relay immediately understood the value of removing those repetitive interactions:
“I love it. This is great, this is going to make us faster.”
— Pilot test processor
Lessons Learned
No API access and Windows application limitations initially felt like barriers. Ultimately, they pushed us toward the widget model that made Relay compatible with Goodwill’s existing technology rather than dependent on replacing it.
Some of our largest efficiency gains came from questioning the workflow around the interface. Moving from batch processing to tag-as-you-go reduced both time and physical handling.
Processors were already categorizing and pricing garments while handling them. Relay became valuable when we stopped asking them to translate that thinking into a separate digital workflow later.
With more time, I’d like to
I would expand the pilot across multiple processors and stores to understand whether the production improvements hold across different working styles and environments.
I’d also test Relay with a broader range of accents, explore multilingual voice input and automatic translation, and make the system configurable across additional product categories and pricing structures.
Finally, as processors become more comfortable trusting automation, I’d explore stronger feedback and error-prevention mechanisms to ensure that increased speed doesn’t come at the expense of tagging accuracy.