How franchises compare performance across 50+ locations

Fifty locations. Same brand, same menu, same operations playbook. And the data sits in Square, 7shifts, QuickBooks and DoorDash, siloed per location, so comparing performance means exporting from every system for every location and stitching it together by hand.

The problem

A multi-location business runs on four to six systems per location:

• POS: Square, Toast, Lightspeed for sales, orders and menu mix
• Labor: 7shifts, Deputy for scheduling, labor cost and overtime
• Delivery: DoorDash, UberEats for volume, commissions and ratings
• Reviews: Google Business Profile for sentiment by location
• Accounting: QuickBooks, Xero for P&L per location

Each reports on one location at a time. Any cross-location question, which is to say every question an operations lead actually has, means either a corporate BI platform costing months and real money, or somebody pulling fifty reports by hand.

The result is that most multi-location operators know their worst location is bad roughly a month after it started being bad.

The setup

1. Square + 7shifts + QuickBooks → Airbyte or Fivetran → warehouse
2. DoorDash + UberEats → same pipeline, same warehouse
3. Warehouse → Contextflo
4. Contextflo → Claude

The one thing that decides whether this works is a location identifier on every row, consistent across every system. Get that wrong and nothing downstream can be compared.

What you can ask once it is connected

Which locations have labor cost above 30% of revenue this month, and how does their overtime compare?

Compare food cost as a percentage of revenue across our top ten and bottom ten locations by sales. What's different?

Which locations have the highest DoorDash commission as a share of delivery revenue?

Show me locations where Google review ratings dropped below 4.0 this quarter alongside their sales trend.

The second one is the question worth building this for. Everyone knows which locations are underperforming. The useful part is what the good ones are doing differently, and that is a question nobody can answer from fifty separate dashboards.

Comparing locations fairly

A ranked list of locations by any metric will be dominated by things the location cannot control: a downtown store with high rent and high volume will not look like a suburban one, and a location that opened in March has no seasonal history.

Two habits fix most of this. Compare like with like, by segmenting on format, market type or age before ranking. And compare a location against its own trend as well as against the group, because a store improving fast from a low base is a different conversation than one quietly sliding from a high one.

Worth saying to the managers involved, too. Cross-location visibility lands very differently depending on whether it reads as a leaderboard or as a way to find out what is working. The data does not decide that, you do.

From weekly reports to answers

Regional managers should not wait for a Monday report to learn which locations need attention. With this setup they ask and get an answer spanning POS, labor, delivery and reviews at once, which is not a query any of those systems could have answered alone.

Here is a more in-depth look at Contextflo and how it works.