Duplicate constituents pile up in Altru without anyone doing anything wrong. A guest buys tickets online with a work email, renews a membership at the desk under a home address, and donates at a gala where a volunteer types the name by hand. Now there are three records for one person, and none of them shows the whole relationship.
This page covers where Altru duplicates come from, what they cost you, how to tackle cleanup, and where a dedicated tool like Deduper fits.
Where the duplicates come from
Every way a person can enter your database is a way to create a second copy of them. The usual sources:
- Online sales and web forms. Guests rarely log in before buying. If the incoming name or email doesn’t match an existing record closely enough, a new constituent is created.
- Daily sales and the membership desk. Staff under pressure at the register will often add a new record rather than search carefully for an existing one, especially for common names.
- Imports. Event lists, school group contacts, and legacy data loads are classic duplicate generators, particularly when the source file has inconsistent formatting.
- Changed details. People move, marry, switch email providers, or use a nickname. The person stays the same; the data doesn’t match anymore.
- Households. Two adults in one household may each end up with their own record and a separate household, instead of one household with two members.
What duplicates actually cost
The damage is mostly quiet, which is why cleanup keeps getting pushed down the list.
- Membership confusion. A member’s active membership sits on one record while they’re searched up at the desk under another. They look lapsed, and staff have to dig to prove otherwise.
- Split giving history. A donor’s lifetime giving is spread across records, so your reports undercount them and your development team may not recognize a major supporter.
- Double mailings and emails. The same household gets two renewal letters, or someone who unsubscribed on one record keeps getting email from another.
- Unreliable reporting. Counts of members, visitors, and donors are inflated, which makes year-over-year comparisons harder to trust.
What Altru gives you on its own
Altru includes some duplicate checking when records are added and a way to merge two constituent records. Those are useful for one-off fixes when a staff member notices a problem. What’s less clear is how well native tools scale to a backlog of hundreds or thousands of suspected pairs, and how much your own configuration affects the matching. If you’re unsure what your database supports, test in a sandbox or ask Blackbaud support before planning a large cleanup around it.
Queries also help. A query that groups constituents by email address, or by last name plus postal code, gives you a rough list of likely duplicates to review. It’s manual and slow, but it’s a reasonable way to size the problem before you commit to anything.
A sensible cleanup approach
- Decide your merge rules first. When two records conflict, which address, email, and name wins? The newest? The one with an active membership? Write it down so every person doing merges makes the same call.
- Start with the safest matches. Exact email matches with the same last name are low risk. Fuzzy name matches with different addresses need a human look.
- Watch for false positives. Parents and adult children with the same name at the same address are not duplicates. Neither are two people who share a family email account. Merging them is much harder to undo than leaving them alone.
- Back up before bulk work. Export the affected records or make sure you know how to recover them before you merge in volume.
- Fix the intake, not just the data. Look at which channel produced most of the duplicates you found. If it’s the desk, retrain search habits. If it’s online sales, review how incoming records are matched.
- Make it routine. A short monthly review is far easier than a once-every-few-years purge.
Video walkthrough
The video below gives an overview of Deduper.
Where Deduper fits
Deduper is built for organizations that run on Altru and want to keep it. It finds and fixes duplicate constituent and email records in Altru, so the work of spotting likely pairs doesn’t depend on someone writing and reviewing queries by hand. Altru stays where your records live; Deduper works against that data. It doesn’t replace the policy decisions above: you’ll still want clear merge rules and a plan for borderline matches. See how Deduper works.
Quick answers
How do I know if we have a duplicate problem?
Run a query grouping constituents by email address and count how many emails appear on more than one record. If the list is long, the problem is bigger than occasional manual merges can handle.
Should we clean up before or after fixing intake?
Do both, but start tightening intake early. Otherwise new duplicates arrive as fast as you remove old ones.
