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Statistics customers: understand who’s booking, who’s growing, and who might churn

Statistics customers: understand who’s booking, who’s growing, and who might churn

Last updated: February 3, 2026

The Customers statistics page gives you a customer-first view of your venue. Instead of starting with hours or revenue, it helps you understand who your customers are, how they behave, and where you have growth opportunities.

Use it to answer:

  • Are we growing unique customers?
  • Are we getting new customers, or mostly returning ones?
  • How big is our member base (and how strong is member share)?
  • Who are our most valuable customers?
  • Which customers look at risk of not coming back?

All data is scoped to the selected location and selected period, and dates are interpreted in the location’s timezone.

1) Choose the period (and understand comparisons on this page)


Period selector

The period controls which bookings/customers are included (Today, Last 7 days, This month, custom range, etc.).

Comparison works differently here

On the Customers page, KPI comparisons are always calculated against the immediately previous period of the same length.
  • If you select Jan 1–31, the comparison becomes Dec 1–31
  • If you select Last 7 days, the comparison becomes the 7 days before that
This keeps customer trend tracking consistent, even if you’re using a custom date range.

2) KPI cards: your “are we growing?” snapshot


Total customers

What it tells you: how many unique customers booked at least once in the period.

How to use it:

  • Track overall customer reach. If this is rising, you’re bringing more people through the door (not just the same people booking more).

New customers

What it tells you: customers whose first-ever booking at this location happened in the selected period.

How to use it:

  • This is your acquisition signal. Use it to measure campaigns, partnerships, visibility, and onboarding improvements.

Returning customers

What it tells you: customers who booked in the period but had their first booking earlier.

How to use it:

  • This is your retention signal. If returning drops while new stays stable, you may have a repeat-experience problem (availability, price perception, membership fit, service quality).

Members (with member share)

What it tells you: customers who had an active membership in the period.
The subtitle shows member share as a % of total customers.

How to use it:

  • Member share is a great “business model mix” indicator. A rising share often means more predictable demand—but it can also change how revenue shows up elsewhere (members may pay differently than drop-ins).

3) Secondary KPIs: what the “average customer” looks like

These are averages across customers who booked in the period:
  • Avg. bookings per customer: how frequently customers book.
  • Avg. revenue per customer: how much revenue the average customer generated.
  • Avg. hours per customer: how much time the average customer books.
  • Avg. lead time: how early customers book (booking start time minus when the booking was made).

How to use lead time:

  • Short lead time (last-minute behavior): consider flexible staffing, more same-day availability, and simple checkout.
  • Long lead time (planned behavior): focus on peak-slot pricing and early-week filling strategies.

4) Customer Lists: the most actionable part of the page

This section groups customers into practical lists. On desktop you’ll switch lists via tabs; on mobile via a dropdown. Each list is sortable and can be exported to CSV.

Most active

What it shows: customers with the most bookings in the period.
Use it to: identify your core regulars and protect their experience (availability, membership fit, loyalty offers).

Highest spenders

What it shows: customers ranked by revenue in the period.
Use it to: spot VIP-like behavior early and understand what “high value” looks like at your venue.
Important: this revenue is broader than “just booking payments”—it includes captured payments tied to the customer (for example memberships and event participation).

New customers

What it shows: customers whose first-ever booking was in the period, plus their early activity (bookings + revenue).
Use it to: evaluate onboarding (“are they coming back quickly?”) and follow up with targeted messaging.

At risk

What it shows: returning customers who haven’t booked again recently and may churn.

How it’s defined in practice:

  • At least 2 historical bookings
  • No booking in the last 30 days
Use it to: run reactivation campaigns (email/SMS offers, “we miss you” messages, member win-back, off-peak incentives).

VIP

What it shows: customers ranked by lifetime revenue at this location.
Use it to: identify who you should treat like gold—these are your long-term business anchors.

Lapsed members

What it shows: former members whose membership has ended.
Use it to: win-back outreach (“come back as a member”), or learn why memberships are being cancelled.

Weekend warriors

What it shows: customers who mostly make bookings on weekends (weekend vs weekday split).
Use it to: tailor offers—weekend-heavy customers may respond best to premium/experience offers, while weekday shifting offers can help smooth demand.

5) Exporting lists (fast way to activate insights)

Each list can be exported with an Export button. The export includes:

  • customer name + email
  • the key metrics for the selected list

Use exports when you want to:

  • contact at-risk customers
  • build a VIP list for special events
  • analyze behavior in a spreadsheet

6) Age distribution (only shows when data quality is good)

The age distribution section appears only when enough customers have birthdate data on file (at least 50% coverage).
  • It shows a breakdown of age groups for customers in the period
  • Customers without a birthdate are excluded from the chart

Use this to align:

  • product offerings
  • messaging tone
  • peak-time strategy (different segments often book differently)