Spend up to 13% less on labour.
Right people, right shifts, no compromise on service.
Our software turns your existing store data, from POS to footfall to conversion, into hour-by-hour AI demand forecasts, then builds the lowest-cost roster to cover them, matched to the right skills. We implement it, train your team, and prove the saving.
Every unmeasured week costs a mid-size chain roughly $48k in lost margin. The scan takes one conversation.
Every hour the roster misses demand, you overspend on labour.
Retail labour mismatch is the gap between when customers actually arrive and when staff are scheduled. Over-staffed quiet hours burn margin; under-staffed peaks cost sales and service. Most chains lose 4-13% of labour spend to this gap. It happens invisibly, every week.
38% of quiet hours, paid in full
Most stores run the same template every day, whether 5 or 50 customers walk in. Quiet mornings are overstaffed by design, and you pay for hours with no demand to serve.
1 out of 4 peaks are understaffed
Roughly a quarter of peak trading hours run short-staffed. Queues lengthen, conversion drops, and the busiest, most valuable hour is the one you under-resource.
Spent building the wrong roster
Your planners know the patterns. They just don’t have the tools or the time to encode them, so the roster gets rebuilt by hand each week, never quite matching demand.
Staff stuck in one store
Neighbouring stores can cover each other’s peaks: sharing staff across sites is one of the cheapest efficiency gains in the roster. Yet most chains plan each store in isolation, so one site runs short while the store next door sits over-covered.
Figures: StoreCadence Workforce Efficiency Benchmark. See the dataset →
The StoreCadence Workforce Efficiency Model
A repeatable, data-first system that reads how your stores actually trade, forecasts the demand, and builds the rosters to match, with the proof to put the savings in your P&L.
Connect store data
We prepare your data and connect the systems that drive demand: POS, revenue-management, footfall counters and order systems, so the platform reads your business as it actually trades.
Forecast demand
Machine-learning models turn your sales, footfall and transaction data into an hour-by-hour demand and manpower forecast for every store and daypart, learning the patterns, seasonality and weather effects your planners know but have never had time to encode.
Build the rosters
The platform turns the forecast into skills-matched roster templates built around your contract hours, budgets, revenue goals and service standards, so the right skills are on the floor in every hour.
Adapt & measure
Rosters flex as conditions change, KPIs are tracked against an agreed baseline, and each week of actuals sharpens the next forecast, so your managers’ knowledge is captured once and compounds.
Six ways we take cost out of the roster and keep service in.
Staff Scheduling Optimisation
Skills-matched rostering that puts the right people, with the right skills, in every hour that sells.
Learn more →Labour-Cost Optimisation
A finance-grade business case that treats labour as the controllable cost it is.
Learn more →Demand, Footfall & Manpower Forecasting
Machine-learning forecasts of when customers arrive and the manpower each store needs, hour by hour.
Learn more →Retail Data & Analytics
Turning the data your systems already capture into decisions managers can act on.
Learn more →Implementation & Migration
Integration, rollout and training that move you from your current tool, or spreadsheets, to StoreCadence without disruption.
Learn more →Operational Efficiency
Store-process improvement that decides how far each labour hour goes.
Learn more →We don’t pitch percentages.
We prove them.
Every rollout is measured against a baseline of labour cost-to-sales and service. A sample of what that looks like in practice:
Top-5 Asia-Pacific grocery chainLabour cost-to-sales cut in 9 months across 180 stores, with service levels held flat.
200-store fashion retailerImprovement in roster-to-demand fit, while planner scheduling time was cut roughly in half.
National QSR brandLabour spend reduced across 340 outlets with no increase in average customer wait time.
“We’d rolled out WFM software and still couldn’t explain our labour line. StoreCadence found the gap in three weeks, and we could finally put a number on it, and on the fix.”
Start with the benchmark instead.
See how chains like yours compare on labour cost-to-sales, roster-to-demand fit and forecast accuracy. We email you the annual report; the headline figures are published openly here.
Free report · No sales follow-up unless you ask
A specialist who has seen your exact problem before.
Generalists explain the problem. We’ve already solved it, in chains that look like yours.
Retail-only focus
We work exclusively in retail and food service. No detours, no learning your business on your budget. The method is built for the store floor.
A proprietary method
The StoreCadence Workforce Efficiency Method™ is a repeatable, documented system for rolling out the platform, not a project reinvented each time.
A benchmark database
Our owned benchmark data lets you see exactly how your labour efficiency compares to chains of a similar format and size.
Start with proof, not a sales call.
See where you stand before you ever speak to us. Each tool gives you something useful to keep, and a clearer picture of the opportunity.
Retail Labour Efficiency Scan
A short self-assessment that returns a workforce-efficiency maturity score and tailored insight for your format.
Labour-Cost / ROI Calculator
Enter revenue, labour percentage and store count to see an indicative savings range against your total spend.
Annual Workforce Benchmark
Our signature dataset on retail labour efficiency across formats: the numbers operators benchmark against.
What retail operators ask us first.
Straight answers to the questions we hear most often.
Find your labour-cost gap in one conversation.
A no-obligation efficiency scan shows what the StoreCadence software would save you, on your own data: where your roster is leaking margin and what it's worth to fix, with a clear business case before you commit.