Store Revenue Per Square Meter: Formula, Examples, and 3-Step Calculation
Revenue per square meter (坪效) is the core metric for store operational efficiency. This guide covers the formula, influencing factors, industry benchmarks, and how to use AI tools for multi-store efficiency analysis, with F&B and retail examples.
What Is Revenue Per Square Meter and Why Does It Matter?
Revenue Per Square Meter (坪效) = Total Revenue ÷ Store Area (sqm). It's the core metric for measuring store space utilization efficiency. Two stores with ¥300K monthly revenue but different sizes — 80 sqm vs 120 sqm — have very different efficiency: ¥3,750/sqm vs ¥2,500/sqm. The former is 1.5x more efficient. This metric answers a key question: is your store space being fully utilized?
Formulas and Variants
Basic: Monthly Revenue ÷ Store Area (CNY/sqm/month). Daily average: Daily Revenue ÷ Store Area (CNY/sqm/day) — better for comparing months with different day counts. Growth rate: (Current - Previous) ÷ Previous × 100% — tracks efficiency improvement over time. For beverage chains, add cups per sqm: Monthly Cups Sold ÷ Store Area (cups/sqm/month) to measure production density.
4 Factors That Influence Efficiency
First, foot traffic — directly sets the revenue ceiling. Second, average ticket — spending per customer. Third, table turnover (F&B) or conversion rate (retail). Fourth, space utilization ratio — non-revenue area (kitchen, storage) percentage. Improving efficiency isn't just about increasing revenue — it's finding the optimal balance across all 4 factors.
Industry Benchmarks
F&B: Fast casual ¥2,000-4,000/sqm/month, Full service ¥1,500-3,000/sqm/month, Fine dining ¥3,000-6,000/sqm/month. Retail: Convenience stores ¥3,000-5,000/sqm/month, Supermarkets ¥1,500-2,500/sqm/month, Specialty stores ¥2,000-4,000/sqm/month. Beverage: Top brands ¥4,000-8,000/sqm/month, Median ¥2,000-4,000/sqm/month. These are references — what matters is comparing against your own benchmarks and history.
3 Steps for Multi-Store Efficiency Analysis
Step 1: Prepare data — monthly revenue and store area for each location. For F&B, add foot traffic and average ticket. Organize in Excel with columns: store name, month, revenue, area, traffic, ticket. Step 2: Calculate — upload to an AI tool like DataFish, which auto-calculates efficiency and rankings. No formulas needed. Step 3: Compare — identify top and bottom performers, analyze why. Traffic issue or size issue? Low ticket or low turnover? AI provides the conclusions automatically.
Case Study: 5 F&B Stores
A chain with 5 stores, ¥200-400K monthly revenue, 60-120 sqm. DataFish analysis showed: highest efficiency at Store A (80 sqm, ¥350K = ¥4,375/sqm), lowest at Store C (120 sqm, ¥280K = ¥2,333/sqm). Further analysis revealed Store C had good traffic but 30% lower average ticket — located in a commercial area with quick-service customers. Recommendation: adjust menu to add higher-ticket combo meals.
How to Improve Store Efficiency
Five directions: First, optimize category mix — cut low-efficiency categories, add high-efficiency ones. Second, increase average ticket — combos, upselling, upgrade prompts. Third, improve turnover/conversion — streamline service, reduce wait times. Fourth, adjust layout — shrink non-revenue space (kitchen/storage), increase selling area. Fifth, data-driven site selection — analyze target area traffic and spending power before opening. Use DataFish to track efficiency changes and quantify each improvement's actual impact.