# Donor Data Cleanup Before Migration: Duplicates, Households and Soft Credits

By EvolveFundraising editorial team

Published: 2026-10-27

Updated: 2026-10-27

Category: Data and reporting

Canonical: https://getevolvefundraising.com/blog/donor-data-cleanup

## Summary

Clean your donor data before you migrate, not after. A good pre-migration cleanup covers seven areas: duplicates, addresses and contact preferences, households and salutations, soft credits and splits, codes and custom fields, gifts with missing details, and a baseline report of gift totals. Cleaning first makes the migration faster, the new system more trustworthy and AI features like scoring and segmentation far more accurate.

## Key takeaways

- Resolve duplicates before migration.
- Review households, preferences and gift credit carefully.
- Create a documented baseline for reconciliation.

Clean your donor data before you migrate, not after. A good pre-migration cleanup covers seven areas: duplicates, addresses and contact preferences, households and salutations, soft credits and splits, codes and custom fields, gifts with missing details, and a baseline report of gift totals. Cleaning first makes the migration faster, the new system more trustworthy and AI features like scoring and segmentation far more accurate.

## Why clean before migrating?

- **Imports multiply mess.** Every duplicate you carry in becomes harder to find among new records.
- **AI depends on clean records.** A donor split across three records gets three weak scores instead of one strong one.
- **First impressions stick.** If staff find bad data in week one, they stop trusting the new system and start keeping spreadsheets again.
- **It's cheaper now.** Mapping work shrinks when you retire unused codes before mapping them.

## 1. Merge duplicate records

Duplicates are the most common problem in donor databases. Typical patterns:

- Name variations: "Robert Smith" vs. "Bob Smith" vs. "R. Smith."
- Same email or phone on two records.
- Married name changes.
- Records created by different systems (online giving, events, membership) for the same person.

**How to dedupe safely:**

1. Match on combinations, not single fields: last name + email, last name + postal code + first initial.
2. Review likely matches in priority order: major donors first.
3. Pick a surviving record and merge gift history, relationships and notes into it.
4. Never auto-merge low-confidence matches. A human approves.

## 2. Standardize addresses and preferences

- Standardize formats (state abbreviations, street suffixes).
- Run a change-of-address check if you mail.
- Mark undeliverable addresses and emails.
- Flag deceased donors.
- Carry over every do-not-mail, do-not-email, do-not-call and anonymous-gift preference. Getting these wrong damages trust fast.

## 3. Fix households and salutations

Households are where migrations most often go wrong.

- Confirm which individuals belong together, and that divorced or deceased spouses are handled respectfully.
- Choose a primary contact for each household.
- Set formal salutations ("Dr. and Mr. Patel") and informal ones ("Priya and Sam") for letters.
- Decide whether gifts are credited to individuals with household totals rolled up, which is the safest approach for most organizations.

## 4. Resolve soft credits and splits

| Situation | What to check |
| --- | --- |
| Spouse soft credits | Credited consistently, not double-counted in totals |
| DAF and foundation gifts | Hard credit to the fund, soft credit to the advisor or donor |
| Employer matching gifts | Linked to the original employee gift |
| Peer-to-peer gifts | Soft credit to the fundraiser |
| Split gifts across funds | Split amounts add up to the gift total |

The rule: hard credits drive financial totals; soft credits drive recognition. Make sure your reports don't mix them up.

## 5. Retire unused codes and custom fields

Pull a usage count for every constituent code, attribute, appeal code and custom field. Then:

- **Keep** those used in the last three years and still needed.
- **Consolidate** near-duplicates ("Board," "Board Member," "BOD").
- **Archive** the rest in a spreadsheet for reference instead of migrating them.

It's common to cut codes and fields by half or more at this step.

## 6. Fix gifts with missing or wrong details

- Gifts without a fund, campaign or date.
- Gifts coded to inactive or misspelled funds.
- Pledges with payments that don't add up.
- Refunded or bounced gifts still counted as revenue.

## 7. Create your baseline

Before you export for migration, run and save:

- Total giving by year and fund for at least the last five years.
- Donor counts by year.
- Open pledge balances.
- Active recurring gift count and monthly value.

After each test import, the new system must match these numbers. This is how you prove nothing was lost.

## How long does cleanup take?

For most organizations, two to six weeks of part-time effort. Plan it to overlap with vendor selection so it doesn't delay the migration.

## How Evolve Fundraising helps clean data

Evolve Fundraising includes AI-assisted data quality tools: duplicate detection with merge recommendations a person approves, record audits that flag missing or inconsistent fields, and AI-assisted import mapping that matches incoming columns to the right fields. Households are optional records, so you can group people without forcing every individual into a household. Because gifts post to the general ledger by fund, your baseline reconciliation can run as a report.

## Frequently asked questions

### Should you clean donor data before or after migrating?

Before. Cleaning first reduces mapping work, prevents duplicates multiplying and protects trust in the new system.

### How do you find duplicate donor records?

Match on combinations of fields (last name plus email, last name plus postal code plus first initial) and review likely matches, starting with major donors.

### What is the difference between hard and soft credit?

Hard credit goes to the person or entity whose money it is and drives financial totals. Soft credit recognizes others who influenced the gift, such as a spouse, fundraiser or donor-advised fund advisor.

### How long does donor data cleanup take?

Usually two to six weeks of part-time work for small and mid-size databases.

**Start clean on a new platform.** Ask the Evolve team for a data readiness review: [Request information from EvolveFundraising](https://getevolvefundraising.com/request-information).

## Sources and further reading

1. [The Donor Bill of Rights — further reading on donor trust and confidentiality](https://afpglobal.org/donor-bill-rights) — Association of Fundraising Professionals

## Related reading

- [Fundraising Software Migration: The Complete Guide for Nonprofits and Associations](https://getevolvefundraising.com/blog/fundraising-software-migration-guide)
- [Donor Database Migration Checklist: 40 Steps From Contract to Go-Live](https://getevolvefundraising.com/blog/donor-database-migration-checklist)
- [From Spreadsheets to Donor Management Software: A Step-by-Step Move](https://getevolvefundraising.com/blog/spreadsheet-to-donor-management-software)
- [Discuss your fundraising goals with the Evolve team](https://getevolvefundraising.com/request-information)

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