The Challenge
GlobalRetail is a national retail chain operating 150 stores across 38 states, with a workforce that fluctuates between 10,000 employees during standard periods and over 14,000 during peak seasonal windows. The company sells home goods, electronics, and lifestyle products through a mix of large-format stores in suburban markets and smaller urban locations. Managing a workforce of this scale and variability had become one of the most operationally complex and financially significant challenges facing the company's leadership.
Scheduling alone was a persistent source of pain. Each of GlobalRetail's 150 store managers was responsible for building weekly schedules for teams ranging from 40 to 120 employees, accounting for varying availability, part-time and full-time status, department-specific coverage requirements, and state-mandated predictive scheduling laws in jurisdictions like Oregon, New York City, and Chicago. Most managers relied on a legacy scheduling tool that lacked demand forecasting capabilities, meaning that staffing decisions were based largely on intuition and last season's patterns rather than real-time data. The result was a chronic mismatch between labor deployment and actual customer traffic: overstaffed during slow periods, understaffed during peaks, and perpetually reactive when unexpected surges or absences disrupted the plan.
The turnover problem compounded everything. At 85% annual turnover — roughly in line with the retail industry average but devastating in absolute terms — GlobalRetail was losing and replacing approximately 8,500 employees every year. The company's talent acquisition process for store-level roles was decentralized, with each district manager managing hiring through a combination of job board postings, paper applications, and unstructured interviews. Time-to-fill for hourly positions averaged 23 days, and during seasonal ramp-ups, the process buckled under volume. In the 2025 holiday season, GlobalRetail fell 1,200 positions short of its seasonal staffing target, costing an estimated $8 million in lost sales due to understaffed stores during the highest-traffic weeks of the year.
The Solution
GlobalRetail's SVP of Operations, Patricia Mendez, sponsored a cross-functional initiative to modernize workforce operations, with the explicit goals of reducing labor cost waste, improving employee scheduling satisfaction, and building a hiring engine that could reliably scale for seasonal demands. After a four-month evaluation that included pilot deployments at six stores, GlobalRetail selected Workisy's Workforce Management and ATS modules as the foundation of its transformation.
The Workforce Management module was deployed with Workisy's AI-powered demand forecasting engine at its core. The system ingested two years of historical sales data, foot traffic patterns from GlobalRetail's in-store analytics platform, local event calendars, weather data, and promotional campaign schedules to generate store-level labor demand forecasts at 15-minute intervals. These forecasts drove automated schedule generation that matched staffing levels to predicted customer volume, ensuring that each department in each store had the right number of employees at the right times. The system also incorporated employee preferences — preferred shifts, maximum hours, availability windows, and commute constraints — into its optimization algorithm, producing schedules that balanced business needs with individual employee preferences in a way that manual scheduling never could.
The ATS module was configured to support both GlobalRetail's steady-state hiring needs and its seasonal surges. Workisy's AI screening engine was trained on profiles of GlobalRetail's highest-performing store associates to identify candidates with the strongest fit signals. The platform's automated workflow handled the full application-to-offer pipeline: candidates applied through mobile-optimized career pages, the AI ranked and screened applicants, qualified candidates self-scheduled interviews through the platform, and hiring managers received structured interview guides with candidate-specific talking points. For seasonal hiring, the system was configured with a streamlined "fast-track" workflow that reduced the application-to-start timeline to as few as five days for pre-screened candidates.
The Implementation
Given the scale of the deployment — 150 stores across 38 states — GlobalRetail and Workisy designed a rolling implementation over 14 weeks. The rollout was organized by region, with four to five regions going live each week. Each region's launch was preceded by a one-week training blitz that included virtual sessions for store managers, in-store demonstrations for assistant managers and department leads, and printed quick-reference guides distributed to all hourly employees. Workisy stationed a dedicated support team of eight customer success specialists who rotated across regions during the launch period, providing on-site support for the first three days after each region went live.
The most significant change management challenge was shifting store managers away from manual schedule building, a task that many had performed for years and considered a core part of their role. To address this, GlobalRetail's operations leadership framed the AI-generated schedules not as a replacement for manager judgment but as a data-driven starting point that managers could refine. In practice, most managers found that the AI-generated schedules required only minor adjustments, and within the first month, 83% of managers reported that they preferred the new approach. The ATS rollout was less disruptive, as district managers were already accustomed to digital hiring tools, and Workisy's interface was more intuitive than the fragmented system it replaced.
The Results
The labor cost impact was the headline result. In the first full year of operation, Workisy's demand-driven scheduling reduced total labor spending by $4.5 million compared to the prior year, while simultaneously improving in-store coverage metrics. The savings came from three primary sources: a 22% reduction in overstaffing during low-traffic periods, a 31% decrease in overtime driven by better advance planning, and a 15% reduction in the use of temporary staffing agencies during seasonal peaks. Importantly, these savings were achieved without reducing total labor hours — the same number of hours were simply deployed more effectively, with staff present when customers needed them rather than during idle periods.
Employee schedule satisfaction, measured through a quarterly pulse survey, improved by 28 points. The most-cited improvements were greater advance notice of schedules (the system published schedules 21 days in advance, compared to the previous average of 5 to 7 days), better alignment with stated availability preferences, and the ease of requesting shift swaps and time off through the Workisy mobile app. This improvement in scheduling satisfaction proved to be a powerful lever for retention. Annual employee turnover dropped from 85% to 62%, a 23-percentage-point reduction that translated to approximately 2,300 fewer separations per year. At an average replacement cost of $3,800 per hourly employee, the turnover reduction represented an additional $8.7 million in avoided costs, though GlobalRetail conservatively attributes only a portion of this improvement to the scheduling changes, recognizing that other retention initiatives were running concurrently.
On the hiring front, the ATS delivered a 40% reduction in time-to-fill for store-level positions, bringing the average from 23 days to 14 days. The 2025 holiday season — the first under the new system — was a stark contrast to the prior year's staffing shortfall. GlobalRetail met 97% of its seasonal hiring target of 4,200 temporary associates, with the AI screening engine processing over 82,000 applications in a six-week window. Store managers reported that the quality of seasonal hires was noticeably higher, with first-30-day attrition among seasonal staff dropping by 35% compared to the prior year.
What's Next
GlobalRetail is expanding its use of Workisy in two directions. First, the company plans to deploy Workisy's Payroll module across all 150 locations, replacing a legacy provider and creating a fully integrated workforce management, hiring, and payroll ecosystem. Second, GlobalRetail is working with Workisy to develop an advanced labor planning model that integrates demand forecasting with financial budgeting, giving regional directors the ability to set labor cost targets at the store level and have the scheduling engine automatically optimize within those constraints. Patricia Mendez has set an organizational goal of reducing annual turnover below 55% by the end of 2027, with continued scheduling optimization as a central pillar of that strategy.
Client Quote
"Retail is a game of margins, and labor is our single largest controllable cost. For years, we managed that cost with gut instinct and last year's spreadsheet. Workisy replaced that with actual intelligence — real-time demand signals translated into schedules that put the right people in the right place at the right time. The $4.5 million in savings got the board's attention, but what changed my perspective was watching turnover drop by 23 points. We've spent years throwing money at retention programs, and it turns out that one of the most powerful things you can do for an hourly employee is simply give them a predictable, fair schedule and a say in when they work. The seasonal hiring piece sealed the deal. After falling 1,200 people short the year before, we hit 97% of our target without breaking a sweat. That's not a marginal improvement — that's a completely different capability."
Patricia Mendez, SVP of Operations, GlobalRetail