Gigpro/Qwick
Rebuilding trust in a staffing marketplace
From manual selection to data-backed matching
- Marketplace
- Platform strategy
- trust & reliability
In 2022, Gigpro, now Qwick, was losing business due to no-shows, late cancellations, and inconsistent Pro quality.
I led strategy and design across qualification, performance, and hiring to rebuild trust and lay the foundation for data-backed matching and platform-led fulfillment.
Results
-
79%
Fewer no-shows
-
28%
Fewer late cancellations
-
13%
of paid gigs from Direct Offers within 2 months
Before: Businesses review Pros and send multiple offers to fill shifts. Without selections, shifts go unfilled.
After: Match guarantees coverage by selecting best-fit Pros using relationship and performance data. Businesses stay in control, with the option to review Pros, send offers, or remove selections.
The problem
Nearly half of churned businesses cited concerns about Pro quality.
“The last pro showed up 20 minutes late and then just disappeared in the middle of service.”
In research with churned businesses, 10 of 22 respondents cited insufficient training or professionalism as a reason they stopped using the platform. As the marketplace grew, no-shows, late cancellations, and poor experiences with Pros made businesses less confident in the Pros they were choosing.
My approach
I focused the work around being able to answer the questions that businesses were asking themselves.
| Question | Response |
|---|---|
| Can this Pro do the job? | Pro Onboarding · Orientation · Credentialing |
| Will they show up and do it well? | Ratings by Role · Review Facets · Medals |
| Will they arrive prepared? | Uniform Requirements · Accessories · Location Details |
| Who do I want to hire again? | Direct Offers · Preferred Network |
| Who’s the best available fit for this shift? | Recommendations · Automatch · Match |
qualification • 2022,2025
Building a trusted path into the marketplace.
Before Gigpro could help businesses make better hiring decisions, it needed to set a standard for being a Pro on the platform.
The call: Raise the bar for eligibility
I expanded the onboarding requirements Pros had to complete before applying. New steps captured richer profile data, while orientation and screening helped prepare Pros and set expectations. Pros could browse gigs but couldn’t apply until they completed these requirements.
Onboarding collected structured inputs that became dedicated, scannable sections which made profiles richer and easier to assess.
W2 workers completed a pre-screen and live interview. Pro Success credentialed skills and managed approvals from the Admin dashboard.
02.visibility • 2023
Making performance visible.
Qualification efforts helped to create the signals. This work put it in front of businesses at the point of selection.
The call: Surface meaningful data and organize ratings by role
Updated business-facing Pro profiles to show preferences, availability, experience, skills, and credentials collected during onboarding, with ratings filterable by role.
Skill certificates, industry certifications, and badges helped to visually communicate qualifications at a glance, alongside richer profile data.
Added review facets for businesses to complete when rating Pros, then surfaced that feedback on profiles with ratings filterable by role.
03.readiness • 2025-2026
Getting Pros ready to work.
Before, important shift details lived in a freeform notes field. Pros arrived late because they didn’t know where to park, or were sent home because they weren’t in the proper uniform.
The call: Make arrival details explicit
Field enhancements to the posting flow made shift expectations clearer and helped Pros arrive in the right location in uniform ready to work.
On the business side, I expanded the range of uniform choices and added additional fields and attachments support for parking and arrival information.
An updated shift-detail view for Pros surfaced uniform and arrival details more explicitly while reminders reinforced expectations.
04.hiring • 2024–25
Helping businesses rehire proven Pros.
Once a shift ended, businesses had no way to reach Pros they had worked with and wanted to book again.
The call: Enable businesses to build their own networks
Businesses wanted to book Pros that they had success with before. Connect was a dedicated space for them to see all the past Pros they worked with. Here they could create groups and build their talent pipeline.
Allowed businesses to save and create groups from Pros they’ve worked with to build their trusted network of Pro talent.
Saving groups enabled businesses to send direct offers specifically to groups or individual Pros instead of posting to the marketplace.
05.match • 2024–26
Earning the right to match.
By 2025, stronger qualification, performance, and hiring signals made platform-led fulfillment possible. By 2026, Match became the default for new businesses, filling shifts with the best-fit available Pros that businesses could review and remove at any time.
The call: Pace the shift
We introduced platform support in 3 phases. Each phase took more of the selection burden off the business.
01 Recommendations
Recommended Pros was a platform curated shortlist that appeared above the standard applicant pool. Businesses had the choice to send offers to individual Pros, send to all, or select from the standard applicant pool. At this phase there was no automatic fulfillment happening.
02 Automatch
Automatch was the first step toward automating shift fulfillment. Businesses received a reminder to review selections and could remove any Pro they didn’t want. If they took no action, offers went out to the best available Pros on their behalf as a means to fill the shift.
03 Match
Match became the default for new businesses and an opt-in choice for existing customers. It sent offers and filled shifts with the best-fit available Pros, with no action needed from the business. Businesses still had access to the roster and held the right to remove Pros.
Reflection
Trust is a long game.
Businesses wanted Pros they could rely on, but early on, we had largely left both sides to establish that trust themselves. Auditing their experiences raised questions that were as much about our brand as our product: What standard were we setting? What promises were we making—or not making? And were we doing enough?
Closing that gap meant asking more of everyone, including ourselves. In this case, friction was a feature, not a bug. Pros had to meet additional requirements to become and remain eligible for work. Businesses had to provide more shift details and share feedback after each gig so we could better understand performance and improve future matches. Our responsibility was to make that effort worthwhile by using that data to help businesses make more informed hiring decisions.
Together, these efforts laid the groundwork for a more fundamental change in how the platform worked: moving from manual selection to a matching model in which businesses could rely on our judgment as a trusted partner.