We provide key data points that reveal hidden profit opportunities.
The result: smarter decisions, stronger accountability, and measurable improvements to your bottom line.
actual vs theoretical analysis
Summary: The owner was concerned about the efficient use of his most important ingredients. We ran an Actual vs Theoretical analysis on the previous 6 months to see where costs were slipping.
The data told a clear story. Beef, tomatoes, chicken, and avocados were bleeding efficiency as low as 22 percent. Those 4 items alone were hiding more than $90,000 a year. Cheese and liquor showed smaller but still meaningful gaps, adding another $11,000 in opportunity.
Armed with this AVT analysis, ownership put simple systems in place, including portion controls, better tracking on high-value items, and accountability on liquor. The result was tens of thousands of dollars in savings.
closing efficiency report
Summary: The owner suspected that closing shifts were dragging on too long without enough sales to justify the labor. To test the hunch, we analyzed closing-hour performance by looking at last check times, final clock outs, and revenue generated after 8:30 PM.
The data confirmed the suspicion. On Sundays and Mondays, the gap between late shift revenue and cost to close was wide. When sales don’t cover the cost to close, efficiency ratios dropped as low as 0.44. In contrast, Thursdays held strong, with later checks and a closing efficiency over 1.0.
Utilizing this new information, the owner tightened schedules on slow nights, shifted sidework earlier in the day, and coached staff on creating closing urgency specifically on Sundays. Unnecessary labor costs were reduced, efficiency ratios improved, and the restaurant maintained its guest experience without bloated closes.
server batting average
Summary: The owner wanted more than gut feeling when it came to evaluating staff. Servers were busy, checks were going out, but it was unclear who was truly driving sales, who was coasting, and where money was being left on the table. Without clear numbers, coaching felt vague and scheduling decisions were based more on intuition than performance.
We built individualized scorecards that broke down each server’s performance across key metrics. In the above, Jessica was a high performer when it came to attaching Loyalty Rewards members with their checks. Management asked for insights on what was working well for her, as she consistently outperformed the rest of the team. She also received targeted coaching around tips, as she was consistently receiving 30% less than the rest of the team on average.
These scorecards were used to pinpoint underperformers and give them specific coaching, while also recognizing and replicating the strengths of top performers. The tool turned raw sales into a clear management system, helping the team lift check averages, reduce waste, and schedule more strategically.
scheduling recommendation tool
Summary: The owner suspected that labor costs were creeping up because schedules were being built on habit, not data. We built a scheduling recommendation tool that tied staffing hours directly to projected sales using Sales Per Labor Hour (SPLH). Instead of guessing, managers could now see the average, most efficient, and least efficient staffing levels based on the five best and worst weeks from recent history.
The results were eye-opening. Weekly labor hours ranged from 1,319 at peak efficiency to 1,704 at the least efficient, all with the same level of sales. Roles like bartenders, line cooks, and prep staff showed the widest swings, proving where overstaffing was costing the most.
With the tool in hand, managers began scheduling around sales forecasts instead of last week’s template. They aligned hours to benchmarks, cut unnecessary labor on slow nights, and kept labor % inside target without sacrificing guest service.
The shift was small in process but big in impact: labor efficiency improved by several percentage points, adding thousands of dollars back to the bottom line each year.
