Loyalty
use case
Grow revenue

Do loyalty members visit more venues in the group than non-members?

Last updated
October 21, 2026
Do members visit more venues in the group than non-members?
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What the answer looks like

Illustrative output on sample data

Illustrative example: how Lexi answers this for a bar and restaurant group with eight venues. It matches loyalty members and non-members across POS tabs and reservations for the past 12 months.

  • Members: 1.9 venues on average, 34% visited three or more
  • Non-members: 1.2 venues, 6% visited three or more
  • Members before joining: 1.3 venues

What the data shows

Members visit more venues than non-members, and more than they did before joining.

What it means

The program is encouraging exploration, mostly through its welcome rewards that can be used at any venue.

What to do

Add a reward for a first visit to a new venue in the group, and show members which venues they have not tried yet.

Want me to show which venue pairs members combine most often?

Why it matters

A group loyalty program should encourage guests to try more than one venue. Comparing how many venues members and non-members visit shows whether the program is doing that job, or whether members simply keep using the venue where they joined.

Frequently asked questions

Does a loyalty program encourage guests to try other venues?

It can, if rewards work across the group. Compare the number of venues members visit with non-members, and with their own behavior before joining. If members explore more after joining, the program is encouraging it.

How can a loyalty program get members to try new venues?

Make rewards usable anywhere in the group, add a bonus for a first visit to a new venue, and show members which venues they have not tried. Personal suggestions based on similar members work better than listing every venue.

Why compare members with their own behavior before joining?

Guests who already explore are more likely to join, so a simple comparison with non-members overstates the program's effect. Comparing members before and after joining shows how much of the change the program itself caused.

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