Data Decisions Lab  ·  Multi-location restaurants

Daypart-level P&L across every location, in one view.

Custom BI dashboards for restaurant groups of 3–15 locations. Prime cost variance, labour as % of sales, comp and void leakage, top SKU profitability, weather-adjusted demand. Reads Toast, Square or Clover exports. Pay once. No per-location SaaS bills.

BI / data analytics hello@datadecisionslab.com
2–3wk working prototype
across all locations
1× build fee,
not per-location
0 monthly seat
licences
100% data stays
in your perimeter

Founder pricing — $3,000 flat for audit + single-panel build (3 slots open). Standard rates: audit from $1,500, custom dashboard build from $5,000. Full pricing →

01

What a multi-location dashboard answers

  1. True prime cost by location, by daypart

    Prime cost (food + labour) as a percentage of net sales, broken out by location and by daypart. Where the lunch shift is leaking 4 points of margin that the weekly P&L hides because dinner masks it.

  2. Labour productivity beyond “hours scheduled”

    Sales per labour hour, sales per cover, by station and by manager. The shift patterns where you’re overstaffed during slow hours and understaffed during the rush. Comparable across all locations.

  3. Comp, void and discount leakage

    Comps and voids per server, per manager, per location. The patterns that indicate over-comping versus genuine service recovery. Discount stack abuse, duplicate voids, suspicious refund timing.

  4. Menu engineering, properly done

    Item profitability after true food cost, prep labour and waste. The classic stars / dogs / puzzles / plowhorses matrix, but at the location and daypart level, not lumped together.

  5. Same-store sales, weather-adjusted

    Year-over-year same-store comparisons that account for weather, day-of-week shifts and local events. The honest growth number, not the noisy one.

  6. Delivery vs dine-in profitability

    Net contribution per delivery order after platform commissions, packaging, and the labour drag on the kitchen. Whether DoorDash and Uber Eats are actually adding margin or just topping the line.

02

Why POS-built reports aren’t enough

Toast / Square / Clover built-ins

  • One location at a time — comparing across locations means manual exports.
  • Sales reports, but no daypart-level prime cost roll-up.
  • Labour reports, but no productivity-vs-cover or sales-per-hour.
  • Locations on different POS systems don’t consolidate at all.

Custom group dashboard

  • All locations, all dayparts, in one view.
  • True prime cost, labour productivity, leakage and menu engineering together.
  • Different POS at different locations — consolidated.
  • Pay once. No new monthly bill on top of your POS subscription.
03

Frequently asked

Some locations are on Toast, others on Square. Does that work?
Yes — mixed POS estates are common after acquisitions. The dashboard sits on a consolidated data layer that normalises menu items, modifiers and labour codes across both systems.
How long to build?
Audit 3–5 days. Working prototype on 2–3 locations in 2–3 weeks. Full group rollout with all locations in 4–6 weeks total.
Can it pull data automatically each night?
Yes — Toast, Square and Clover all support automated nightly exports, either via API or scheduled CSV drops. The dashboard refreshes overnight so morning managers see yesterday’s numbers when they walk in.
What about R365 or similar restaurant accounting platforms?
R365 is a strong product if you need full back-of-house accounting. R365 Core Operations starts around $250 per location per month; the full ops+accounting bundle runs roughly $450–700 per location per month, more with add-ons. For groups that already have accounting handled and just want the operations dashboard, custom is usually a fraction of the TCO over 24 months.

Want a single view of prime cost across all your locations?

Tell me how many locations, what POS you use at each, and what you wish you could see but can’t today. I reply within a day.

Book a free 30-min call →    or email me directly — hello@datadecisionslab.com