Multi-location restaurant analytics: every venue in one view
What the answer looks like
Illustrative output on sample data
Illustrative example: how Lexi answers this for a taqueria chain with 16 locations. It matches POS, online ordering and loyalty records to each guest for the last 90 days.
- Downtown: 9,400 guests, 2.6 visits each, $14.20 average check
- University: 11,800 guests, 1.9 visits, $11.60 average check
- Suburban mall: 6,100 guests, 1.4 visits, $19.80 average check
What the data shows
University has the most guests, Downtown the most frequent ones, and Suburban mall the biggest checks with the fewest repeat visits.
What it means
Each location has a different job. Suburban mall wins family meals but rarely becomes a habit.
What to do
Test a weekday family deal at Suburban mall to lift frequency, and a combo upgrade at University to lift the check.
Want me to add the other 13 locations, ranked on each measure?
Why it matters
Location reports built one at a time make it hard to compare venues fairly. Seeing guests, visit frequency and average check side by side shows which venues lead on which measure, so managers can learn from each other and head office knows where to focus.
Frequently asked questions
What should multi-location restaurant analytics include?
At minimum, guests, visit frequency, average check and revenue for each location, measured the same way and over one shared time window. Matching guests across locations matters too, since many people visit more than one, and a guest counted twice inflates every total.
How do you compare restaurant locations fairly?
Compare like with like. Group locations by format and trade area, use the same time window, and look at each measure separately before combining them. A location with fewer guests may still lead on frequency or check, which is worth learning from.
How often should multi-location reports be reviewed?
Weekly for sales and covers, and monthly for guest measures such as frequency, retention and average check, which need more time to move. Quarterly reviews against last year show the trends that weekly noise can hide.