Why Raw Sales Mislead
Comparing stores by total sales alone hides the operational story. A high-volume location may be inefficient, while a smaller store quietly outperforms everyone on productivity. SPLH benchmarking sales by store size becomes essential—it reveals how efficiently each location converts labor into revenue, regardless of footprint.
Larger locations generate higher absolute sales
A flagship store in a downtown core will almost always post higher weekly sales than a neighborhood location, even if the neighborhood team runs a tighter operation. Raw sales figures reward scale, not efficiency — a large store can top the sales leaderboard while overstaffing every shift, while a smaller venue that matches demand precisely gets buried in the rankings.
When managers compare stores by total sales alone, they penalize smaller locations for factors outside their control: foot traffic, catchment size, and square footage. The result is a distorted performance view that can lead to the wrong resource allocation decisions.
Managers make staffing and investment decisions
Every decision about where to add headcount, which manager to promote, or which location deserves a remodel starts with comparing performance across stores. But when those comparisons rest on raw sales alone, managers reward scale instead of efficiency. A director choosing between investing in Store A (high revenue, bloated labor spend) and Store B (moderate revenue, tight execution) will pick the wrong store if the only number on the table is total sales.
True operational efficiency requires a metric that isolates performance from location size. Sales per labor hour SPLH comparison does exactly that—it shows how much revenue each scheduled hour produces, making a 2,000-square-foot downtown store directly comparable to a 6,000-square-foot suburban box. That clarity changes every staffing and capital allocation conversation.
Raw sales figures reward scale, not efficiency—a large store can top the sales leaderboard while overstaffing every shift, while a smaller venue that matches demand precisely gets buried in the rankings.
What SPLH Measures
Sales Per Labor Hour isolates how efficiently a location converts staff time into revenue. The formula is simple: divide total sales by total labor hours worked during the same period. A store that generates $60,000 in weekly sales using 400 labor hours delivers $150 SPLH. Another location posting $180,000 in sales with 1,500 labor hours also delivers $120 SPLH — lower productivity despite triple the revenue.
Consider two stores in the same chain. Location A occupies a modest footprint and schedules lean labor coverage, yet generates strong weekly sales. Location B spans a larger space, posts higher total sales volume, and requires proportionally more staff hours. Raw sales figures suggest Location B dominates in absolute terms. But sales per labor hour reveals an instructive contrast: the smaller store extracts more revenue from every hour its team works, while the larger location spreads its sales across a broader labor investment. The smaller store demonstrates superior labor efficiency despite its lower total output.
This metric strips away differences in store size, market density, and format. A compact urban store and a sprawling suburban location operate under different constraints, but SPLH reveals which team moves product more efficiently relative to their labor investment. That clarity enables fair comparable sales analysis different location sizes and shows operators exactly where staffing changes will protect margin without sacrificing coverage.
Calculating SPLH Correctly
Accurate SPLH starts with clean data. Collect your total sales and total labor hours for the same time period — typically a week, month, or four-week accounting period. Labor hours means all paid time on the clock. Managers, full-time associates, part-timers, everyone. If someone was paid, they count. Some retailers exclude salary managers from labor-hour totals; others include them. Pick a rule and apply it consistently across every location, or your comparisons break down immediately.
When calculating SPLH, make sure you have the following in place:
- Define which sales figure you're using (gross sales or net sales)
- Collect total sales for the same time period as labor hours
- Include all paid time on the clock in your labor hour calculations
- Apply the same standards consistently across every location
The math is simple: divide total sales by total labor hours. A store that rang up $45,000 in sales and scheduled 300 labor hours delivers $150 SPLH. Track this monthly or quarterly to spot trends; a single week can swing wildly due to holidays or weather.
Schedule and time-tracking systems feed this calculation. If your platform captures punches and actual hours worked — not just the schedule — you're measuring real labor deployment, not the plan. That distinction matters when a manager schedules 40 hours but the team works 44. Data quality drives benchmarking accuracy, and garbage data produces garbage targets.
Setting Benchmarks Across Sizes
A single SPLH target across every location assumes all stores trade the same way — but they don't. Your downtown flagship faces different foot traffic, customer demographics, and labor markets than a suburban strip-center store, even when both occupy 3,000 square feet. Fair store benchmarking methodology equal comparison starts by grouping stores into comparable tiers, not forcing identical performance expectations onto fundamentally different operations.
Start by clustering locations based on characteristics that drive labor efficiency: market type (urban, suburban, rural), customer transaction patterns (high-ticket low-volume versus high-volume low-ticket), local wage rates, and operating hours. Within each cluster, establish baseline SPLH ranges drawn from your historical data. Urban and suburban locations will reflect distinct operational profiles, with urban settings shaped by rent and wage pressures, and suburban operations influenced by traffic patterns and local labor availability.
The goal is identifying outliers within each tier — the stores that fall below their peer group's baseline — rather than comparing an urban location to a suburban one. A store hitting $88 SPLH in a high-cost downtown market may actually outperform a suburban location at $98 SPLH once you account for market realities. Tiered benchmarks let you ask the right question: which stores underperform their peers, and why? That's where operational attention pays off. Not in chasing a single company-wide number that ignores how your portfolio actually operates.
Interpreting Results Fairly
A location posting high SPLH may look strong on paper, but that number alone doesn't tell you whether the team is efficient or simply stretched too thin. High SPLH combined with improved turnover, rising customer complaints, or increasing injury rates often signals understaffing and burnout, not operational excellence. Conversely, a store with low SPLH might be overstaffed for its volume, but if it holds steady employees, trains them well, and maintains strong customer satisfaction, the labor investment may be protecting your four-wall profitability in ways the SPLH doesn't capture.
Use SPLH as a diagnostic tool, not a ranking system. When a location posts low SPLH, the right response isn't to label the store weak or cut hours reflexively. Instead, investigate: Are shifts misaligned with traffic patterns? Is the team undertrained on your POS or core processes? Are coverage gaps forcing inefficient task handoffs? The metric raises the question; the operational review finds the answer.
Compare trends over time rather than single-month snapshots. A store that improves SPLH by ten percent over two quarters while holding turnover flat has solved a scheduling or process problem. A store whose SPLH spikes one month then crashes the next is likely reacting to staffing volatility, not executing a plan.
SPLH becomes meaningful when you pair it with labor quality metrics: turnover rate, average tenure, customer satisfaction scores, safety incidents, and schedule adherence. Together, these measures reveal if you are running an efficient operation or simply running your people into the ground.
Taking Action on Insights
SPLH becomes useful when it drives staffing decisions. A manager reviewing peer-group benchmarks might discover that Location 12—a 3,500 sq ft store—outperforms similar-sized locations in labor productivity. That data justifies protecting its staffing levels during peak hours, even if corporate pressure mounts to trim labor budgets uniformly. The store earns its payroll through documented efficiency.
The same analysis exposes underperforming locations within their tier. When a suburban store posts $54 SPLH while comparable peers average $71, the gap signals an operational issue worth investigating: training gaps, scheduling mismatches with traffic patterns, or process bottlenecks that waste labor hours. Directing training resources and scheduling reviews to that location addresses the root cause rather than punishing smaller stores for lower absolute sales.
Implementing these decisions requires connecting SPLH targets to shift schedules. PlannerPuffin's workforce planning tools translate demand forecasts and SPLH benchmarks into daily coverage plans. So managers move from insight to adjusted schedules without rebuilding spreadsheets. The platform cascades location-specific targets into shift assignments that protect both margin and service levels, closing the loop between analysis and execution.