Here’s a common way it happens. A guest buys a ticket online with just a name and email. Two months later the same person joins as a member at the front desk, and staff enter a new record because nothing came up in the search. Run a duplicate check later and those two records still don’t show up as a match. They share a name and an email, so why doesn’t Altru flag them?
Usually the answer is in how Altru scores possible duplicates and how much weight it gives to address data. Once you see how the matching works, the gaps are easy to predict, and you can plan around them.
How Altru’s duplicate matching works, in plain terms
Altru doesn’t look for exact copies. When a record is added, or when you run a duplicate search, it compares the new or existing record against other constituents field by field. Each field that agrees adds to a match score, and records above a threshold are presented as possible duplicates.
The fields that matter most for individuals are the ones you’d expect:
- Last name and first name, often with some allowance for nicknames, initials, or small spelling differences
- Address, especially the street line
- Postal code
- Depending on your version and configuration, other contact details such as email or phone
Exactly which fields are compared, how much each one counts, and where the thresholds sit can vary by Altru release and by how your database was set up. If you need the precise rules for your environment, check the duplicate and matching settings in Administration with your system administrator, or ask Blackbaud support to confirm. Don’t assume the behavior you saw at a previous organization applies to yours.
Why records without addresses slip through
The scoring model was designed for a donor database where most people have a mailing address. Address and postal code carry a lot of the evidence. A name on its own is weak proof: two “Maria Garcia” records with different addresses really could be two different people, so the matcher is cautious about name-only agreement.
That caution works against you when one or both records have no address at all. With nothing to compare in the address fields, those records can’t build enough score to cross the threshold, even if the names are identical. In practice, that means:
- Online ticket buyers who only gave a name and email are often invisible to address-weighted duplicate checks.
- Walk-up and group visitors entered quickly at the desk tend to have thin records.
- Imported lists (event sign-ups, school contacts, newsletter subscribers) often arrive with email only.
Visitor-heavy organizations collect a lot of exactly this kind of record. That’s why museums, zoos, and gardens usually have more hidden duplicates than a typical fundraising shop of the same size.
Ways to catch what the matcher misses
1. Find them with queries
Build a constituent query that outputs name, primary email, address, and date added, filtered to records with no address. Export it and sort by email. Two records sharing the same email address are your strongest leads. A second pass sorted by last name and first name will turn up name-only pairs worth a human look.
2. Review with judgment, then merge
A shared email is strong evidence, but couples and families sometimes use one inbox. Before merging, check memberships, households, and revenue on both records so you know which one should survive and that nothing important ends up on the wrong person. Use Altru’s constituent merge so history moves with the record instead of deleting one side.
3. Collect a little more at the point of entry
Even a postal code helps. If your online checkout or desk process can capture ZIP without slowing people down, address-weighted matching has more to work with. For desk staff, a short rule helps: search by email and last name before creating anyone new.
4. Test before you change matching settings
If your Altru environment lets you adjust duplicate criteria or thresholds, be careful. Loosening them to catch name-only matches will also raise false positives on common names, and staff will start clicking past the warning. Try any change in a sandbox or on a small test set first, and confirm with Blackbaud what the setting actually controls.
5. Make it a routine
Duplicates without addresses build up steadily, especially in summer. A monthly review of new records missing an address keeps the list short enough to handle by hand. If you wait a year, you’ll have thousands.
Why it’s worth the effort
Split records cause real problems: a member who gets a renewal notice for the wrong record, a donor whose giving history looks half as large as it is, an email list that sends the same message twice. They also distort visit and conversion reporting, because a ticket buyer who later became a member looks like two separate people.
A tool built for this gap
If manual query reviews are taking too long, Social Good’s Deduper is designed to find and fix duplicate constituent and email records in Altru, including the email-only records that address-based matching tends to miss. Altru stays your database; Deduper just helps with the cleanup.
Quick answers
Will adding an address to an old record make it match?
Often, yes. Once both records have comparable address data, they have a much better chance of scoring as possible duplicates. Rerun your duplicate search after the update to check.
Does Altru match on email alone?
Don’t count on it. Whether email factors into matching, and how much, depends on your version and configuration. Test with two known duplicate records in a sandbox to see how your setup behaves.
