Can marketing build guest lists by frequency and check themselves?
What the answer looks like
Illustrative output on sample data
Illustrative example: how Lexi answers this for a casual dining group with 22 venues. Marketing asks for guests by visit frequency and average check at each venue, and the list is built from the group's shared definitions.
- Frequency bands: weekly, monthly, occasional and one-time, over the past 12 months
- Average check bands: above or below the venue median
- Matched across POS, reservations and online ordering
What the data shows
The list returns 210,000 guests across 22 venues. Weekly guests with an above-median check number 9,400.
What it means
Marketing can build this list in minutes, and it matches the definitions finance and the data team use.
What to do
Save the list as a standing segment, so marketing can refresh it before each campaign without a new request.
Want me to save it as a segment and push the weekly high-check group to Klaviyo?
Why it matters
Every list request that goes through the data team adds days and pulls analysts away from harder work. Letting marketing build lists by visit frequency and average check themselves, from shared definitions, speeds up campaigns and keeps every list consistent.
Frequently asked questions
Can marketing teams build guest lists without the data team?
Yes, if the definitions are agreed and the data is matched to each guest. With shared rules for frequency, check size and activity, marketing can build lists directly, and the data team only needs to maintain the definitions.
Why do shared definitions matter for self-serve lists?
Without them, each team defines a regular or a high spender differently, and lists stop matching reports. Shared definitions mean a list built by marketing uses the same rules as finance and the data team.
What lists do restaurant marketers ask for most?
Regulars by venue, lapsed guests, high spenders, guests due a birthday and guests who ordered a specific item. Saving these as standing segments removes most repeat requests and keeps them up to date automatically, so the data team can focus elsewhere.