Queries provide a powerful way to access and filter data within BETA to generate reports and gain insights about your members, sales, staff and much more.
You can either use this BETA tool to generate custom or compound queries tailored to your gym's needs, or select from a range of our standard, ready-to-use queries available.
To understand what is a Query in BETA, as well as gain insights into the Query Types & Categories you can use, please read the full Reporting Dashboard Explainer.
🔗 The Reporting Dashboard Explainer
https://www.notion.so/beta/Reporting-Dashboard-Explainer-21116cc94ed480768b9cf8fc062a5925
No need to build queries from scratch as BETA provides a collection of pre-built standard queries. These ready-to-use queries help your team quickly access detailed insights into key performance areas.
You can generate a query with owner permissions.
To access the reporting page, please go to Insights page on your BETA dashboard and find the Go to reporting page button on the right top corner of the filters` function.
This query retrieves membership statistics for a specified time period. When you provide a start and an end date, the query checks all climbers with active passes for the gym during the selected period.
If you are using a multi-gym setup, the results are broken down by facility — for example: Gym A: 50 members, Gym B: 60 members.
You can see an example in BETA demo by filtering with:
Period: Monthly
Type: Standard
A query that generates a report on new members
Helpful to define membership growth trends and seasonal patterns (if you split by time periods).
When isChurn = false:
Calculates new member signups
Tracks membership growth
A query that generates a report on the members who left. Helpful to define membership decline trends and seasonal patterns (if you choose to split by time periods).
When isChurn = true:
Calculates members who left during the period
Identifies membership cancellations
This calculates the total duration of active memberships within a specified start and end date, grouped by entry type. It helps assess how long members are active under each entry type during a given period, providing insights into usage patterns and retention.
This query calculates the lifetime value of members i.e., total revenue generated per member.
We can calculate Member LTV over various time periods—daily, weekly, monthly, or quarterly. For each period, we identify all users who had an active membership (excluding day passes and unlimited-duration passes, which are typically used for punch cards or staff access) and retrieve their all-time transaction value total. Then we calculate the average value across the group to estimate the average lifetime value of members active during the selected period.
Note: This calculation does not include historical (imported) data, so it reflects only transactions recorded within the BETA system.
This query provides a breakdown of sales after discounts by product category and tax bracket. It calculates the total amount sold in each category and includes an overall sales total across all categories. The report accurately handles both tax-inclusive and tax-exclusive pricing, making it a reliable tool for comprehensive sales reporting.
Please note:
Some regions require inclusive tax (common in Europe)
Some require exclusive tax (common in US/Canada) - i.e. gyms whose currency is USD or CAD their amounts are tax exclusive in the report
This query provides information on total refunds after discounts per category and per tax bracket.
This data is useful for:
Monitoring refund rates by category
Financial reporting
Identifying potential issues (i.e., high refund rates in specific categories)
Tax accounting for refunded transactions
These 2 use the same function to calculate the output using a start and end period.
Gets total which is category specific and totalVal which is grand total across the categories.
Please note:
Some regions require inclusive tax (common in Europe)
Some require exclusive tax (common in US/Canada) - i.e. gyms whose currency is USD or CAD their amounts are tax exclusive in the report
This query calculates the Cost of Goods Sold (COGS) per gym, broken down by product category. It’s useful for detailed tracking of inventory costs, whether you're analysing a single location or comparing multiple gyms.
This query is similar to the above, but instead returns the quantity per product, rather than monetary values.
This query calculates the current value of inventory held at each gym. It does this by retrieving the current stock levels, multiplying the cost price by the quantity on hand, and aggregating the total by gym. This helps with tracking inventory value for accounting, audits, and stock management.
The getHoursByTag query supports the four use cases below by checking actual vs planned and monetary (money) vs hours. It checks hours worked or cost of hours worked according to employees timesheet at the gym.
Tracks planned employee working hours by tags and per gym.
Tracks planned monetary costs per tag and per gym.
planned hours worked * shift rate e.g. 8h * £10 = £80
Tracks actual hours worked per tag and per gym.
Tracks actual monetary cost per tag and per gym.
worked hours worked * shift rate e.g. 8h * £10 = £80
….Please forward any questions to the BETA team….
BETA offers two main ways to view and analyse your data: Insights and Reporting.
Each serves a different purpose, depending on how you want to use your data — whether it’s for quick checks, or for long-term strategic KPI analysis.
One of the most important things to understand is how often your data refreshes:
Insights are updated in real time. If you're using Insights to check daily sales, view activity in a product, or monitor registrations, you’re seeing the most up-to-date information available. Anywhere you see the Insights button (
️), the data is live and continuously refreshing. Av
Reporting, on the other hand, pulls from a larger, historical database (including migrated data). This makes it ideal for tracking long-term trends and KPIs. Because of this, some reporting data may be delayed — in some cases, up to 24 hours from the last time it was viewed.
Tip: If you're ever unsure where to look, just ask: Do I need this data now, or am I trying to track something over time? Insights for real-time answers, and Reporting for deeper analysis.
Climbers
These queries focus on individual profiles — they answer the question relating to “How much climbers ____?”. e.g., tracking their activity, progress, or engagement over time. Use Climbers queries to analyse behaviour like attendance patterns, membership duration, or performance improvements.
Sessions
Sessions queries look at visit data. — they answer the question relating to “How many times did climbers ____?”. They help you understand how often and when members are using the gym. Ideal for analysing peak hours, class popularity, or usage trends across different times or days.
Products
Product queries focus on what's being sold— they answer the question relating to “How much products were ____?”. e.g., memberships, class packs, merchandise, etc. Use these to track sales performance, identify best-selling products, or monitor revenue trends tied to specific offerings.
Totals
Total queries return financial data — they answer the question relating to “How much revenue ____?”. They aggregate data across different categories to give you a snapshot of overall performance such as total revenue, number of sessions, or member count within a selected time frame.
All charts in the Reporting Dashboard update only when someone opens the Reporting page and more than 24 hours have passed since the last refresh. If you view the report again within that 24-hour window, it will show the same data until the next automatic update.
For example, if you open the report at 3:00 PM on Monday, it updates then. If you check again later that same day (e.g., at 8:00 PM), you’ll still see the same data. However, if you open it again at 3:01 PM on Tuesday or later, it will automatically refresh with the latest figures.
In BETA, historical transactions are not be visible under climbers’ profiles.
Attention: Migrated data has the same limitations of your previous software - data in BETA is a lot more granular than migrated data!

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