Visualizing visitor/check-in data from a digital membership platform in Looker

Looker Q&A Looker
1

Hi all,

I’m working on a membership-based platform that issues digital membership cards (used across multiple locations/venues essentially a “Membership Anywhere” ), and I want to build out visitor analytics using Looker.

The core use case: every time a member checks in or scans their digital card at any location, we capture an event (timestamp, location, member ID, etc.). I’d like to visualize this data to track things like visitor footfall trends, peak usage times across locations, and member engagement over time.

A few questions for the community:

  1. What’s the recommended LookML modeling approach for this kind of multi-location check-in/event data (one fact table vs. location-specific tables)?
  2. Any tips on building drill-down dashboards so we can go from an overall visitor trend down to a specific location or member segment?
  3. Has anyone built something similar for membership/loyalty platforms and can share lessons learned on dashboard structure or performance optimization?

Would appreciate any guidance, examples, or links to relevant docs/articles from the community.

Thanks!

2

For this kind of check-in data, I’d start with one event/fact table, not location-specific tables. Something like one row per scan/check-in with member_id, location_id, timestamp, source/card type, and any event metadata. Then join out to member and location dimension tables.

That gives you much cleaner LookML:

  • measures like check-ins, unique members, visits per member
  • time dimensions off the event timestamp for hour/day/week/month trends
  • location dimensions for venue, region, city, etc.
  • drill fields from overall trend → location → member segment → raw event rows

For performance, pre-aggregate only after the raw model is working. A daily_location_member summary table can help a lot once volume grows, but I would still keep the raw events as the source of truth.