The Booking & Demand insight helps you analyze bookings from a demand and revenue perspective.
Unlike Scheduling Performance, which focuses on session capacity and fill rate, this page focuses on what happens at the booking level: how many bookings are generated, where they come from, how clients behave, and where demand may be higher than your available capacity.
This insight helps you answer questions like:
How is booking demand evolving over time?
Which sources or passes drive the most bookings?
How far in advance do clients book or cancel?
Which clients have the most cancellations or no-shows?
Which sessions, categories, or teachers generate the most waitlist demand?
Get started: Select your filters
The main date filter is based on the session date, not the booking date. Bookings are attributed to when the session takes place.
You can refine your analysis using these filters:
Class name
Class category
Pass name
Booking Source
Teacher
1. Overview
This tab gives you a high-level view of booking performance for the selected period.
Key metrics
Confirmed bookings: Bookings with a confirmed status for sessions in the selected period
Charged cancellations: Cancelled bookings that still generated revenue (e.g. late cancellations or no refund)
Bookings per customerAverage number of bookings per client → Total bookings ÷ distinct customers
Earned revenue per booking: Average revenue per revenue-generating booking → Total earned revenue ÷ charged bookings with revenue Bookings with no tracked revenue are excluded
Booking performance
Analyze how bookings evolve over time and what drives them.
This section includes 4 views:
Bookings by source
Bookings by pass
Bookings by booking type
Charged vs free bookings
Each view includes:
a time series chart (evolution over time)
a donut chart (distribution over the selected period)
Bookings included
All charts include:
Confirmed bookings
Charged cancellations
→ This represents charged bookings, based on the session date
Bookings by source
Use this view to understand where your bookings come from.
How to use it
Identify your main booking channels
Monitor changes in channel mix over time
Spot over-reliance on one source
Measure the contribution of aggregators vs owned channels
Source | Definition |
Back office | Booking created by a staff member from the back office |
Member area | Booking made by a client via the web member area |
Aggregators | Booking made via an external aggregator integration |
Branded app (company) | Booking made via the company’s branded mobile app |
Branded app (franchise) | Booking made via a franchise-branded mobile app |
bsport app | Booking made via the bsport mobile app |
App | Fallback category when the app source cannot be precisely identified (e.g. older data) |
Migration | Booking created during data migration |
Bookings by pass
Use this view to understand which passes generate the most bookings.
Use the control “Top N” selection to keep the view focused on your top passes.
How to use it
Identify your most-used products
Spot under-used passes
Understand whether bookings are concentrated on a small number of products
Compare product mix over time
Charged vs free bookings
Compare revenue-generating bookings with bookings made without using credits.
Charged bookings → confirmed bookings + charged cancellations
Free bookings → bookings made without using credits (excluding aggregator bookings)
💡 How to use it
Monitor the share of revenue-generating bookings
Identify growth driven by non-paid usage
Track the impact of trials or non-monetized bookings
Bookings by booking type
Use this view to compare bookings by service type: activity vs appointment
How to use it
Compare demand across different service formats
Understand whether bookings are driven more by classes or appointments
Monitor how your business mix evolves over time
2. Client behavior
This tab focuses on how clients behave across the booking journey.
It helps you understand acquisition, cancellation behavior, booking habits, and client patterns that may affect performance.
First visits
How many upcoming first visits?
Track first-time clients scheduled in the coming days. These are clients who have their first confirmed session booking scheduled in the selected future period.
💡 Use it to anticipate onboarding and monitor acquisition trends
Cancellation patterns
This section helps you understand how cancellations evolve and what drives them.
It includes:
a time series chart of cancellations
a donut chart summarizing the full period
You can analyze cancellations by:
cancellation reason
booking source
service type
pass name
Cancellations included
This section focuses on canceled bookings. It is designed to help you understand cancellation behavior, whether or not those cancellations generated revenue.
How to use it
Identify periods with rising cancellations
Compare cancellation behavior across products or channels
Spot whether certain passes, service types, or sources lead to more cancellations
Review cancellation reasons to improve policy or communication
Booking and cancellation lead time
This section shows how far in advance clients usually book or cancel.
It uses buckets such as:
21+ days before
11–20 days before
6–10 days before
3–5 days before
1–2 days before
same day
after session
Definitions
Booking lead time: Number of days between booking date and session start date
Calculation: Session date - booking date
Cancellation lead time: Number of days between cancellation date and session start date
Calculation: Session date - cancellation date
How to use it
Understand whether clients tend to book early or last minute
Detect same-day or late cancellations
Improve reminder timing
Adapt promotional timing based on booking habits
Review cancellation policies if cancellations cluster close to session time
Clients impacting performance
This section helps you identify clients whose behavior contributes most to lost spots and lower performance.
It includes 2 rankings:
Clients with most cancellations
Clients with most no-shows
Clients with most cancellations
Ranks clients with the highest number of cancellations. Tooltip includes:
Count of cancellations
Share of Bookings Cancelled: Cancellations ÷ total bookings
Clients with most no-shows
Ranks clients with the highest number of no-shows. No-shows are confirmed bookings where the client did not attend the session and was registered as “absent”. Tooltip includes:
Count of no-shows
Share of no-shows: No-shows ÷ total bookings
How to use it
Spot repeated patterns that affect attendance
Identify clients with frequent late changes
Review whether reminder workflows or policies need adjustment
Use this analysis carefully as an operational signal, not as a standalone decision-maker
3. Waiting list
This tab helps you understand unmet demand.
It focuses on waitlist registrations and what happened to them.
A waitlist registration can have 3 statuses:
Waiting: the client is still on the waiting list. If the session is already in the past, this usually means the registration never converted
canceled: the waitlist registration was canceled
Converted: the waitlist registration became a confirmed booking
Waiting list performance
This section shows how waitlist registrations evolve and convert into bookings.
The waiting list conversion rate is calculated as:
Converted waitlist registrations ÷ total waitlist registrations
How to use it
Measure how effectively waitlists turn into bookings
Identify whether demand is being captured or lost
Spot periods where demand exceeds available capacity
Status split
This donut chart shows the overall distribution of waitlist registrations by status:
waiting
canceled
converted
How to use it
Quickly assess whether your waitlist is converting well
Identify if too many registrations remain unconverted
Compare status balance across time periods
Demand breakdown
This chart shows waitlist registrations by status across one selected dimension.
You can view by:
session category
session
teacher
This means the chart always shows the status breakdown, while the control changes the analysis dimension.
How to use it
Identify sessions with the highest unmet demand
Spot teachers or categories with recurring capacity pressure
Decide where to add more sessions
Detect areas where conversion is low despite strong interest
Key definitions
Metric | Definition |
Confirmed bookings | Bookings with confirmed status |
Charged cancellations | Cancelled bookings that still generated revenue |
Charged bookings | Confirmed bookings + charged cancellations |
Earned revenue | Revenue from confirmed bookings and charged cancellations (aggregator revenue is not be tracked) |
Earned revenue per booking | Average revenue per charged booking with revenue |
Bookings per customer | Total bookings ÷ distinct customers |
Booking lead time | Session date − booking date |
Cancellation lead time | Session date − cancellation date |
First visit | Client’s first confirmed booking |
No-show | Confirmed booking not attended |
Waitlist registration | Client registered on a waiting list |
Converted | Waitlist registration turned into a booking |
Best practices
Use this insight regularly to:
Track demand trends and booking sources
Reduce cancellations and no-shows
Improve client engagement and frequency
Identify unmet demand and add capacity where needed
Data freshness
Data on this insight page is refreshed hourly.
Permissions
Access to the Booking & Demand insight depends on your permissions for the relevant bookings reporting data.
If you can view, edit, or export the related bookings report, this insight page will be available to you