Home

Statistics

Statistics bookings: understand demand, bays, and peak times

Statistics bookings: understand demand, bays, and peak times

Last updated: February 3, 2026


The Bookings statistics page is for one thing: spotting booking patterns.

Use it when you want to answer questions like:

  • When do people want to play? (weekday + time of day)
  • Which bays are carrying the venue? (hours + revenue per bay)
  • Is demand shifting? (comparison vs another period)
Everything on this page is scoped to the selected location and the selected period.

1) Start with the period (and optional comparison)


Choose a period

The period selector controls all numbers and charts on the page (Today / Last 7 days / This month / custom range).
Tip: Use a custom range when you’re evaluating a specific change (new pricing, campaign, opening hours update, new bay setup).

Add a comparison (optional)

Turn on a comparison (previous period / last week / last month / last year) to get context.
  • KPI cards show change vs the comparison period
  • “Busiest day” shows the comparison period’s busiest weekday as a subtitle

2) The top KPI cards: a quick demand snapshot


Total number of bookings

What it’s for: A quick read on overall booking activity in the period.

How to use it:

  • If this goes up while “Usage per bay” stays flat, you may be getting more short bookings or more single-bay bookings.
  • If this goes down but peak periods look the same, you may have a distribution shift (fewer off-peak bookings).

Total number of users

What it’s for: A signal for “how many different people are booking”, not just how often.

How to use it:

  • If bookings rise but users don’t, demand is coming from repeat customers.
  • If users rise, you’re likely expanding reach.
Good to know: Bookings without a linked user (for example anonymous/internal cases) won’t be counted as users.

Busiest day

What it’s for: The weekday with the most bookings in the period.

How to use it:

  • Plan staffing and opening-hour focus
  • Validate that a promotion moved demand to a target weekday
  • Compare busiest weekday across periods to see if behavior is shifting

3) Usage per bay: your most practical “what should we improve?” section

This section shows one row per bay with:

  • Hours booked
  • Revenue
  • Bookings
You can sort the table and use the two bar charts to see the ranking instantly.

How to interpret it (the “so what”)

  • High hours + high revenue: your strongest bay(s). Protect these peak slots and consider smart price increases.
  • High hours + low revenue: you’re filling time but not earning much per hour (discounts, off-peak heavy mix, or pricing mismatch).
  • Low hours + decent revenue: premium bay that sells less often—good candidate for targeted promotion or better placement/visibility.
  • Low hours + low revenue: underperformer—review pricing, bay setup, booking flow, or whether it should be bookable.

How bay revenue is attributed (important for multi-bay bookings)

If a single booking uses multiple bays, the booking’s payment is split across the bays used, so each bay’s revenue reflects that shared usage (instead of counting the full payment on every bay).

4) Booking heatmap: “when do people want to play?”

The heatmap is designed for pattern recognition.

What you’re looking at

  • Rows: weekdays
  • Columns: hours (0–23)
  • Each square: a time slot
  • The number is a percentage relative to the busiest slot in the selected period:
  • 100% = the single busiest slot
  • 50% = about half as busy as your peak slot

What the heatmap is not

It is not an occupancy or utilization chart. It does not tell you:

  • coverage percentage
  • how many bays were filled
  • how “full” you were in absolute terms
It tells you where demand concentrates.

Filters

You can switch between:

  • All sims
  • One specific bay
This is useful when one bay has a different audience (e.g., premium bay vs standard bay) and you want to see if its demand peaks differ.

How to use it (real decisions)

  • Staffing: match staffing to the dark-green clusters
  • Pricing: add peak pricing where demand clusters; run off-peak offers where it’s consistently light
  • Opening hours: consider extending hours into a consistently busy edge, or trimming persistently dead hours
  • Operations: if weekends are packed but weekdays are light, plan weekday leagues/events or corporate offers

5) Activity by day of week: your weekly demand shape

This chart summarizes how bookings are distributed across weekdays.

Use it to:

  • Compare weekday vs weekend demand
  • See if demand is moving (e.g. Thursdays becoming the new peak)
  • Measure whether a campaign changed which days people book—not just the total count
Like the heatmap, you can filter by all bays or one bay to see whether different bays behave differently.