Store-Level Four-Wall P&L Prompt
Prompt
You are a retail finance manager preparing the four-wall P&L for individual store profitability. Store data: [PASTE: Store | Revenue | COGS | Gross margin | Direct store labor | Store occupancy (rent + utilities) | Direct store expenses (supplies/maintenance/security) | Four-wall EBITDA | % of corporate overhead allocated (if any)] Four-wall EBITDA = Revenue − COGS − Direct store labor − Occupancy − Direct store expenses (before corporate overhead allocation) Analyze: 1) Four-wall EBITDA by store — rank from most to least profitable; flag negative four-wall stores 2) Occupancy leverage — rent as % of revenue; stores above 15% rent-to-revenue are at risk 3) Labor efficiency — labor as % of revenue by store; outlier stores with excessive labor ratios 4) Break-even revenue — for each store, the revenue required to achieve four-wall breakeven 5) Underperforming store plan — for stores with negative or very low four-wall EBITDA: turnaround / renegotiate lease / close Output: Four-wall P&L by store. Profitability ranking. Occupancy and labor ratios. Break-even revenue. Underperforming store action plan.
Why it works
Four-wall P&L analysis is the right tool for store-level performance decisions because it isolates costs the store manager can control from corporate overhead they cannot. Separating four-wall EBITDA from contribution margin (before rent) is particularly valuable for lease renewal decisions — a store that is four-wall negative after rent may still be worth keeping if the contribution margin is positive and the lease is near expiry. The traffic chart format makes relative performance visible across the portfolio.
Watch out for
Four-wall P&L comparisons across stores require consistent allocation methodology — if some stores have different lease structures, labor models, or corporate service levels, direct comparison will be misleading. Document the methodology clearly in the dashboard so management interprets variances in context. Also be careful about using four-wall EBITDA as the sole basis for store closure decisions, as customer data often shows that closing a store reduces online sales in that market as well.
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