HFGo engagement had been declining for years, but the business primarily monitored downstream operational metrics such as coverage and active staff.

I connected several years of product, workforce and marketplace data and found a much deeper structural problem:

• weekly HFGo users had fallen from ~15,000 → 4,900 (-67%)

• the active workforce had contracted from ~9,800 → 3,800 (-61%)

• registered workers had actually increased ~10%

• the proportion of registered workers who were genuinely active had collapsed

​​

This reframed the problem from “How do we get more people to open the app?” to “How do we protect and rebuild the active workforce that powers the marketplace?”

I then led a broader discovery combining workforce segmentation, behavioral analysis, 7 worker interviews, 7 Operations interviews, a Recruitment Director interview and staff surveys, turning the findings into a seven-goal workforce strategy and sequenced delivery plan. The work secured Company Director backing for a dedicated cross-functional programme.

​​

Impact

• subsequent interventions contributed to ~3–6% WoW weekly-active growth for several weeks

• Find Shifts weekly users increased ~15–20%

• weekday DAU increased ~7–8%

• shifted leadership attention from lagging workforce metrics toward engagement as a leading indicator of marketplace capacity

Automated matching was one of HFGo's key differentiators, but its performance and user experience had not been investigated deeply enough.

I led a focused mixed-method discovery combining:

• behavioral analysis of how workers entered, configured and left the experience

• operational analysis of short-notice shifts and pull-outs

• qualitative research with workers

• a 60+ item RICE prioritization across matching, communication, reminders and settings​

​​

The discovery challenged the assumption that the matching algorithm itself needed the biggest redesign. A major issue was legibility and control: workers didn't always understand what automated matching would do, how their availability affected it, or the consequences of their settings.

I prioritized lower-effort, higher-confidence improvements around notifications, pull-out warnings, distance settings, availability and clearer matching behavior rather than immediately rebuilding the underlying matching system.​

​

Impact

• automated matching fulfillment increased from ~55% → ~68% within four months

• addressed key friction around configuration, visibility and shift commitment

• established a more evidence-based roadmap for subsequent matching and shift-discovery improvements

HFGo's Inbox was the 4th most-visited area of the app, receiving ~100K visits per week, but engagement was shallow: only ~13% of users interacted with notification settings and 7–10% left the app immediately afterward.

Rather than simply reskinning the existing interface during the platform modernization, I helped run a full product discovery:

• behavioral analysis through Pendo

• a complete notification taxonomy

• benchmarking across ~19 consumer and healthcare-staffing apps

• two rounds of user research

• journey and persona mapping

• cross-functional ideation

• usability testing

• RICE prioritization

​​

Research showed that users didn't understand the existing Alerts/Messages split and wanted richer shift context and simpler filtering. In validation, 81% preferred the more detailed shift-invite card and all participants preferred the simplified filter-header concept.

​

Outcome

• shipped Inbox V4 as part of HFGo's modernized platform

• introduced a clearer chronological notification experience, richer shift cards, threads and filtering

• moved the product from an inherited UI structure toward a design grounded in behavioral, competitive and qualitative evidence

The workforce research showed that retention was influenced by more than available shifts. Workers needed clearer communication, local support, training visibility, recognition and better awareness of their own active/inactive status.

I translated those findings into a broader engagement layer across HFGo:

introduced HFGo's first Home screen, bringing upcoming shifts, pending invitations and workforce status into one place

created territory-specific communications for training, local updates and worker recognition

redesigned Help & Support around the worker's local team

made inactive status and approaching inactivity visible rather than silently changing in the background

connected training more directly to shift eligibility through FuturU and “skills to unlock” journeys

​

The Home and territory-communications experience shipped in June 2026 as the final major enhancement to the legacy HFGo platform.

​

Outcome

• created a clearer relationship between work availability, training, status, local support and engagement

• gave internal teams a structured way to communicate with workers by territory

• moved HFGo closer to the lifecycle strategy identified through the workforce-engagement discovery

HFGo was running on an ageing React stack, but the product was too commercially important to stop feature delivery for a large rewrite.

​

I oversaw the product side of an incremental React modernization, working with Engineering to rebuild the app section by section while the existing product remained live.

The programme included major areas such as authentication, Profile, Inbox, Help & Support, notifications, onboarding, pay documents and automated-matching settings. I prioritized modernization alongside customer-facing improvements and managed release risk as old and new components coexisted.

​

Outcome

• progressively modernized the core mobile experience while continuing to ship product improvements

• avoided a high-risk big-bang migration

• created the foundation used by subsequent workforce-engagement and marketplace improvements

• Onboarding — ran a scoped discovery focused on communicating existing product value; shipped first-login carousel and section-specific guidance.

• Same-Day Pay — redesigned the instant-pay experience around per-booking withdrawal and a clearer fee model.

• Navigation / IA — benchmarked consumer and staffing products, created two future navigation models and usability-tested the first.

• Learning & eligibility — migrated to FuturU and progressively linked training gaps to the shifts workers could unlock.

• Diagnosed a ~67% decline in weekly HFGo usage and ~61% contraction in the active workforce, securing Company Director backing for a cross-functional recovery programme

• Helped reverse the engagement trend, with subsequent interventions contributing to several weeks of ~3–6% weekly-active growth and ~15–20% growth in Find Shifts usage

• Increased automated staff-matching fulfillment from ~55% to ~68% within four months

• Led end-to-end discovery and delivery across major workforce journeys, including Inbox V4, onboarding, shift discovery, communications, training and workforce status

• Expanded and modernized HFGo from a shift-booking app toward a broader workforce-engagement platform, while progressively rebuilding the mobile experience without a high-risk big-bang migration