A ticket sale and a visit are two different facts. Altru is very good at recording the first one: who bought, what they bought, for which date, and how they paid. The second one, whether that person actually walked through the door, is much harder to see when you open a constituent record. A patron who bought four tickets to a Saturday program and never showed up can look exactly like one who came, stayed all afternoon, and bought a membership on the way out.
That gap matters. No-show rates shape how you oversell capacity, who gets a “sorry we missed you” email instead of a “thanks for coming” survey, and whether a donor prospect has actually been on site. This article covers where attendance data can live in Altru, how to get it onto the constituent record in a form people can use, and where you’ll hit limits.
Start by defining “attended”
Before touching any configuration, agree on what counts. Teams that skip this step build a field nobody trusts. Common definitions:
- Ticket scanned at entry. The most objective signal, but only as good as your scanning discipline. If staff wave people through when the line backs up, unscanned tickets will look like no-shows.
- Checked in on a roster. Typical for registered programs, classes, camps, and events where someone works from a list.
- Group arrived. For group sales, attendance is usually recorded at the group level, and the individual people on the bus are rarely constituents at all.
- Member visited. A membership card scan or lookup at the desk, with no ticket involved.
You may end up with more than one of these. That’s fine, as long as each one is labeled for what it is.
Where the raw data comes from
Ticket scanning
If you scan barcoded tickets at the door, the scan is the attendance record. The question is whether that scan status is stored in a way you can query and tie back to the purchaser. Behavior can differ depending on how and where tickets are scanned and how your organization has Altru set up, so confirm it in your own database: buy a test ticket, scan it, leave a second test ticket unscanned, then see whether a query on sales or tickets can tell the two apart. If you have a sandbox, do it there. If you can’t find a field that separates them, ask Blackbaud support directly whether scan status is exposed to queries in your configuration.
Event and program registrations
For registered events, the registrant list is the natural place to mark attendance. Altru’s event tools have historically supported recording who attended versus who only registered, but check how your team is actually using it. In many databases the attendance marking was never done consistently, so the data exists for some events and not others. Pick one recent event, look at the registrants, and see whether anyone was ever marked as attended.
Daily sales with no name attached
Walk-up admissions sold at the window often go to a generic or anonymous constituent. Nothing you do downstream will create attendance history for a person who was never identified. If attendance by person matters to you, the fix is upstream: capture a name or member lookup at the point of sale, or accept that walk-up attendance will only ever be a count.
Getting it onto the constituent record
Once you know where the scan or check-in data lives, you have a few ways to make it visible to the people who open a constituent record.
- Queries first. Build a query of ticketed or registered constituents for a date range, with the attendance indicator as a column. Save one version filtered to “purchased, not attended.” This is the foundation for everything else and doesn’t change any records.
- Smart fields for summaries. If the attendance data is queryable, a smart field can roll it up into something like a count of attended visits or a most recent attended date. Test whether the smart field types available to you can filter on the attendance indicator before promising this to anyone.
- Constituent attributes as a fallback. When the data can’t be rolled up automatically, some teams add an attribute such as “Attended: [program name]” and update it in bulk from a query export after each event. It’s manual, but it’s explicit and easy to report on. Keep the attribute list short or it turns into clutter.
- Interactions or notes for one-off cases. For a major donor who came to a cultivation event, a contact record or note is often more useful to development staff than any flag.
Whichever route you choose, name things precisely. “Ticket purchased” and “Attended” should never share a label, and nobody should have to guess which one a report is counting.
Reporting no-shows
With a “purchased, not attended” query in place, you can answer the questions that usually prompted this in the first place:
- No-show rate by program, day of week, or ticket type, which helps you decide how far to oversell timed entry.
- Members who renew but rarely visit, a group that tends to lapse later and benefits from a nudge.
- Separate follow-up lists, so a post-visit survey goes only to people who actually came.
Before acting on no-show numbers, spot-check them against a day you remember. If the query says forty percent of Saturday’s tickets went unused and the floor staff remember a packed building, the problem is scanning compliance, not attendance.
Honest limits
Altru wasn’t built around a single “attended” flag on the constituent record, and there isn’t one switch that produces it. What you can build depends on whether your entry process creates a reliable, queryable record of each visit. If it does, queries and smart fields get you most of the way. If it doesn’t, the most valuable change is operational: scan every ticket, mark every roster, and tie walk-up sales to a real constituent wherever you can. The reporting only gets better after that part is in place.
Quick answers
Can I see attendance directly on the constituent record?
Not as a built-in summary in most setups. A smart field or attribute you maintain is the usual way to surface it there.
Why do some patrons show tickets but no attendance?
Either they didn’t come, or their ticket was never scanned or checked in. Your scanning consistency decides which explanation is more likely.
