Chart Builder: Metric Types Explained

The Chart Builder lets you plot one or more metrics over time, each built from a metric type (what to measure) plus a set of filters (how to narrow it down). This article walks through all 12 available metric types, what each one measures, its key filters, and a few example use cases — plus composite metrics, which combine other metrics on the chart arithmetically.

A couple of concepts that apply across metric types:

• Additive vs. non-additive. Most metrics (revenue, counts of things that happened) can be added together across periods — daily values sum to a monthly total. A few metrics are point-in-time snapshots or distinct counts (like Membership Count or Unique Check-Ins) and can't be summed this way, since doing so would double-count. We call this out for each metric type below.
• Rates that show no value yet. Retention, Conversion, and Churn all measure an outcome that takes time to know — a customer isn't "retained" or "converted" until enough time has passed. Recent periods that haven't had time to settle show no value yet, rather than a misleading 0%.
• Selecting products by attribute. Metrics that can be narrowed to specific products can also select them by what they are — all prepaid memberships, all single-entry passes, all rolling events — using the shared filters described in Selecting Products by Attribute below.

At a Glance

Selecting Products by Attribute

Filters like Product Type and Membership Types let you narrow a metric to specific products by picking them off a list. The product attribute filters offer another way: select products by what they are, instead of checking each one by hand.

Each attribute filter is only offered when the metric is looking at products of the matching type: through its Product Type filter (Revenue, Products Sold, Product → Membership Conversion), through the product implied by its Entry Method (Check-In Count and First Check-In → Membership Conversion — guest pass entry is granted by a membership, so it takes the membership filters), or always, for the membership-focused metrics (Membership Count, Member Churn, Membership Referrals).

Within one filter, checking several values includes products matching any of them. Adding several attribute filters narrows to products matching all of them — and they combine the same way with a specific-products selection, so the chart includes only the products that satisfy everything. To include everything again, remove the filter rather than checking every box.

The criteria are re-applied every time the chart runs. That's the real power of these filters: a chart of "all prepaid memberships" picks up a newly created prepaid membership automatically, and a retired product stops accruing new values (unless you use Product Status to chart retired products deliberately) — no need to revisit the chart's configuration as your catalog changes.

One thing to keep in mind: attributes reflect each product's configuration today, applied to the chart's whole history. For example, if a membership type is switched from Prepaid to Recurring, all of its values — including past periods — count under Recurring from then on.

Revenue

Money collected from completed payments — by default net of refunds, with a Revenue Calculation filter to change that definition. This is your core financial metric — additive, so daily or weekly values can be summed into a longer-range total.

Key filters:

• Product Type: Restrict to Memberships, Entry Passes, Events, Voucher Packs, Gift Cards, or Other, or leave it as "All" to include everything. Choosing a type with individual products lets you narrow further to specific products.
• Revenue Source: Restrict to revenue collected through Capitan (Direct), your Point of Sale (POS), or Offline. Leave this filter off to include every source.
• Revenue Calculation: How revenue is calculated. Net of Refunds (the default) subtracts refunds from sales. Gross Sales ignores refunds — the right choice when you're measuring what products actually sold for, such as average sale price or discount utilization. Net of Refunds & Processor Fees additionally subtracts payment processor fees, for take-home revenue. Because processor fees are only tracked for payments collected by Capitan, this option is only available when the Revenue Source is "All" or "Capitan (Direct)". Leaving this filter off is the same as choosing Net of Refunds.
• Locations: Restrict to one or more locations.
• Product attribute filters: When the Product Type is Memberships, Entry Passes, Events, or Voucher Packs, you can also select products by attribute — for example, all prepaid memberships or all rolling events. See Selecting Products by Attribute.

Note on refund timing: refunds are counted on the day the refund was issued, not the day of the original sale. A net series can therefore dip — or even go negative — in a period containing large refunds of earlier sales, while a Gross Sales series always reflects clean sale-day amounts.

Example use cases: Compare membership revenue collected through Capitan vs. your POS this quarter. Chart total gift card revenue by month. Track revenue for a single high-value event type. Chart gross event sales to measure average booking price without refund noise.

Check-In Count

The number of customer check-ins.

Key filters:

• Check-In Type: Leave off to count every check-in. Choose "Unique Customers" to count each distinct customer once per period instead of every visit (this makes the series non-additive, since the same person can appear in more than one period). Choose "First-Time Check-Ins" to count only brand-new visitors.
• Entry Method: Restrict to how customers entered: Membership, Entry Pass, Event, Guest Pass, Free Entry, or Migrated. Methods that involve a product (Membership, Entry Pass, Event, Guest Pass) let you narrow further to specific products used for entry.
• Locations
• Time of Day: Restrict each day's count to a window of hours, in your organization's local time. Only available with the Daily interval.
• Product attribute filters: With an Entry Method whose entries come from a product, you can select those products by attribute — for example, check-ins using single-entry passes. See Selecting Products by Attribute.

Example use cases: Chart unique weekly visits per location. Compare traffic by entry method to gauge day-pass vs. member visits. See how many members check in during early-morning hours to inform staffing.

Products Sold

The number of products sold — memberships, entry passes, event bookings, voucher packs, or gift cards — counted by when they were sold. Every sale counts by default, regardless of price or how it was acquired; use Exclusions to narrow that. Additive.

Key filters:

• Product Type (required): Which category to count, and optionally specific products of that type. Not available per-product for gift cards.
• Locations: Restrict to sales at specific purchase locations.
• Exclusions: Opt-out toggles to drop sales you don't want counted: exclude gift redemptions, exclude redemption code redemptions, exclude purchases with $0 paid, or exclude entry passes created manually by staff (rather than purchased). Nothing is excluded by default, and only the exclusions relevant to your chosen Product Type appear.
• Product attribute filters: Select products of the chosen Product Type by attribute instead of picking them individually — for example, only retired memberships or only rolling events. See Selecting Products by Attribute.

Example use cases: Track new membership sales per month. Exclude $0 purchases to see only paid entry pass sales. Exclude manually created entry passes to isolate self-service purchases from staff-issued ones.

New Customers

The number of new customers, counted by the period of their first-ever check-in — not their signup date — and attributed to their home location. Additive. Because this is based on the first check-in timestamp rather than tallying check-in records, it correctly includes customers whose check-in history was imported into Capitan.

Key filters:

• Locations: Restrict to customers whose home location is one of these.

Example use cases: Track new-customer growth over time. Compare new-customer acquisition across locations.

Membership Count

How many memberships, individual members, or add-ons you have at a given point in time. This is a snapshot (a stock count), not a total sold — so it is not additive across periods; you can't sum weekly counts into a total, since the same memberships would be counted repeatedly.

Key filters:

• Count By (required): What to count: Memberships (whole membership records, default), Individual Members (a headcount of distinct people — a group membership counts each of its members, while a customer with more than one membership is counted once), or Add-Ons.
• Status: Active (default), Frozen, or Active + Frozen.
• Value in Period: Which point in each period to read the count at: End of Period (default), Start of Period, Minimum, or Maximum. Minimum is useful for catching dips from freezes within a period.
• Membership Types: Restrict to specific membership types.
• Add-On Names (only when Count By is Add-Ons): Restrict to specific add-ons by name.
• Discount Association (only when Count By is Memberships or Members): Only count memberships receiving a discount through a chosen association.
• Membership Purchase Type, Membership Billing Interval, and Product Status: Select membership types by attribute instead of picking them individually — for example, count only prepaid memberships. See Selecting Products by Attribute.

Example use cases: Chart active membership count over the past year. See how many individual members you have per location. Track total add-ons (e.g., bouldering or swim access) over time. Chart the minimum membership count each month to spot the impact of freezes.

Customer Retention

How well the customers who first checked in during each period were retained. A customer is considered retained if they checked in again during a 10-day window that begins 90 days after their first-ever check-in.

Key filters:

• Component (required): Which value to plot: Retention Rate (the default, shown as a percentage), Customers Retained, or Customers Lost.
• Locations: Restrict to customers whose home location is one of these.

Because retention isn't knowable until roughly 100 days have passed, recent periods won't show a value yet; this avoids reporting a misleadingly low retention rate for customers who simply haven't had time to come back.

Example use cases: Track your retention rate trend by quarter. Compare retained vs. lost counts across locations to spot where onboarding needs work.

Product → Membership Conversion

Of the customers who used a product (a membership, entry pass, or event) for the first time while not already a member, how many became members within a chosen conversion window.

Key filters:

• Component (required): Conversion Rate (default), Converted, or Not Converted.
• Conversion Window (required): How long after the first product use to check for membership: 30, 60, 90, 120, or 180 days, or 1 year.
• Product Type (required): Memberships, Entry Passes, or Events, and optionally specific products of that type.
• Product attribute filters: Select products of the chosen Product Type by attribute — for example, conversion from all single-entry passes. See Selecting Products by Attribute.

A note on Product Type: choosing a single specific product counts each customer once. Leaving it on "all products" of a type instead counts each first-time use, so a customer who first used two different products of that type is counted twice.

Example use cases: See what percentage of entry-pass first-timers convert to membership within 60 days. Compare conversion rates between event bookings and entry passes to see which drives more memberships.

First Check-In → Membership Conversion

Of the customers whose first-ever check-in falls in each period, how many became members within a chosen conversion window. Uses the same Component and Conversion Window options as Product → Membership Conversion above.

Key filters:

• Component (required): Conversion Rate (default), Converted, or Not Converted.
• Conversion Window (required): 30, 60, 90, 120, or 180 days, or 1 year.
• Entry Method: Restrict to first check-ins made with a specific entry method, and optionally specific products used for entry.
• Locations
• Product attribute filters: With an Entry Method whose entries come from a product, you can select those products by attribute. See Selecting Products by Attribute.

Example use cases: Chart your overall first-visit-to-member conversion rate. Compare whether guest-pass first-timers convert to membership better than free-entry first-timers.

Member Churn

Of the members you had at each period boundary, how many had churned (were no longer members at all) by the end of a chosen churn window. A member is still counted as a member if they're active, frozen, or in a failed-payment state — so a temporary freeze isn't mistaken for churn. Switching to a different membership type also doesn't count as churn.

Key filters:

• Component (required): Churn Rate (default), Members, Retained, or Lost.
• Churn Window: How many days after each period a member must remain a member to count as retained. Defaults to 30, up to a maximum of 366.
• Value in Period: Which period boundary to measure the member set at: Start of Period (default) or End of Period.
• Membership Types: Restrict which members count toward the starting set (the churn check itself always looks at the whole organization, so switching to an excluded type still counts as retained, not churned).
• Billing Locations: Restrict by the location the membership bills to, which can differ from a member's check-in/home location.
• Membership Purchase Type, Membership Billing Interval, and Product Status: Select the membership types that count toward the starting member set by attribute — for example, churn among recurring members only. See Selecting Products by Attribute.

Note that Member Churn isn't available on the Daily interval — a churn window can't be meaningfully measured against a single day.

Example use cases: Track monthly churn rate over the past year. Break out churn by billing location to spot a location with a retention problem. Measure churn specifically for a trial or intro membership type.

Member Ancillary Revenue

Money members spent on purchases outside their membership (e.g., retail, guest fees, day-pass add-ons) while they were an active member, summed by the period the purchase completed in and attributed to the purchase location. Additive.

Key filters:

• Locations

Example use cases: See how much extra revenue your members generate beyond membership dues, and how that breaks down by location.

Member Ancillary Spend Count

The number of distinct members who made an ancillary purchase in each period, over the same purchases as Member Ancillary Revenue above. Because the same member can appear in more than one period, this is a distinct count and is not additive across periods.

Key filters:

• Locations

Example use cases: Track how many members are engaging in non-membership purchases each month, as a gauge of member engagement beyond dues.

Membership Referrals

The number of membership referrals — memberships purchased that credit a referring customer — counted by the period the purchase completed in. Additive.

Key filters:

• Billing Locations: Restrict to memberships billing to specific locations.
• Membership Types: Restrict to specific membership types.
• Membership Purchase Type, Membership Billing Interval, and Product Status: Select membership types by attribute instead of picking them individually. See Selecting Products by Attribute.

Example use cases: Track referral-driven signups over time to measure the impact of a referral program. See which membership types are most often referred.

Composite Metrics

Beyond the 12 metric types above, the Chart Builder can also plot a composite metric: a metric computed from the other metrics on your chart, using one of four operations — Sum, Subtract, Multiply, or Divide. This is how you build ratios and derived values like revenue per check-in or average sale price. Use the Add Composite Metric button to create one.

A composite is built from operands: other metrics on the chart — including other composites, so an expression like (A + B) ÷ C is possible — and, optionally, numeric constants (for example, a 0.9 margin factor). At least one operand must be a metric. Sum and Multiply accept two or more operands; Subtract and Divide take exactly two, applied in order (A − B, A ÷ B). The composite is computed period by period, combining its operands' values within each period.

A few things to know:

• Plotting only the result. A composite's source metrics have to be on the chart, but they don't have to be drawn — hide a source metric with its visibility checkbox to plot only the composite.
• Unit. By default the composite's unit (how its values are formatted) is inferred automatically — for example, dividing a currency metric by a count metric gives currency, as in revenue per check-in. You can also set it explicitly to Currency, Count, or Percentage. Dividing two counts produces a 0–1 fraction, so choose Percentage to display it as a percent.
• Gaps, not zeros. A period where any operand has no value — or where a Divide's denominator is zero — shows no value, rather than a misleading zero.
• Additive? A Sum or Subtract of additive metrics is itself additive. Multiply and Divide composites — and any composite that includes a constant — are not additive, since a ratio or scaled series can't be meaningfully summed across periods.

Example use cases: Revenue per check-in (Revenue ÷ Check-In Count). Average sale price (Revenue with Gross Sales ÷ Products Sold). Ancillary revenue per spending member (Member Ancillary Revenue ÷ Member Ancillary Spend Count). Total revenue across two separately-plotted product types (Sum of two Revenue metrics).