The Scenario
You are a CRM manager at a mid-size nonprofit. Your organization has used Raisely across a dozen campaigns over three years. The new executive director wants a clean donor database before the end of the quarter. The first step: pull all Raisely users into Excel, find the duplicates, and tag high-value donors before the data enrichment firm gets involved.
The slow version:
- Export Raisely users via CSV — only basic fields come through
- Discover custom fields are missing from the export
- Manually look up missing fields for each record in Raisely
- Import the CSV into Excel, use COUNTIF formulas to find duplicate emails
- Write a formula for total giving per email, apply "Major Donor" labels by hand
- Two days in. 2,000 rows touched. The exec director's follow-up email has arrived.
The fast version is one prompt.
The Easy Way: One Prompt in SheetXAI
SheetXAI pulls all Raisely user records including custom fields, flags duplicates, and segments donors — without a formula in sight.
Open the SheetXAI sidebar and type:
Fetch all users from my Raisely organisation including email, name, phone, and custom fields, paste them into the Donors tab, highlight rows where the email appears more than once. Add a 'Segment' column: 'Major Donor' for total donations over $500, 'Regular' for everyone else.
SheetXAI pulls all users, writes them into the Donors tab, highlights duplicates, and adds the Segment column. You walk into the executive director's meeting with a segmented database.
What You Get
A clean, segmented donor database in the Donors tab:
- Full user records — email, name, phone, and custom fields included
- Duplicate email highlighting — any email appearing more than once is flagged
- Segment column — "Major Donor" or "Regular" based on total giving
- All 2,000+ users — pagination handled automatically
What If the Data Is Not Quite Ready
A three-year-old donor database has quality issues beyond duplicates.
When names are inconsistently formatted
Some users show "FIRSTNAME LASTNAME" in all caps, others have only a first name.
Import all Raisely users into the Donors tab. Normalize names to Title Case. Flag any record where the name appears to be a first name only (no space) as 'Incomplete' in a Name Status column.
When you need to identify lapsed donors for re-engagement
The re-engagement team wants everyone who gave in 2022 or 2023 but not in 2024 or 2025.
Import all Raisely users with donation history dates into the Donors tab. Add a 'Last Donation Year' column. Flag anyone with a last donation in 2022 or 2023 and no donation in 2024 or 2025 as 'Lapsed' in a Re-engagement Status column.
When custom fields are blank for a large portion of records
The "Referral Source" custom field was added mid-campaign and many early donors have it blank.
Import all Raisely users. Add the 'Referral Source' custom field as a column. Flag blank rows as 'Missing Referral Data'. Write the count of populated vs blank in cells A1 and B1 above the data in the Donors tab.
When the enrichment firm needs a fully formatted export
The data firm needs email, name, phone, total giving, last donation date, segment label, and duplicate flag — one clean sheet.
Import all Raisely users into the Donors tab. Add: total giving, last donation date, segment ('Major Donor' if over $500, 'Regular' otherwise), duplicate flag ('Duplicate' if email appears more than once). Normalize names to Title Case. Sort by total giving descending. Ensure all columns have labels in row 1.
The pattern: pull, clean, enrich, and format for handoff in one prompt.
Try It
Get the 7-day free trial of SheetXAI and ask it to pull your full Raisely user database into Excel with deduplication and segmentation. The Raisely integration is included in every plan. See also how to bulk-create supporters without duplicates or the Raisely in Excel overview.