TLDR
CSV donor import handles field mapping, household de-duplication, and data validation before a single record writes to the database. Most CRM import failures come from one cause: inconsistent household formatting in the source file. GrantPipe surfaces those errors at preview time, not after the import runs.
CSV donor import is the fastest path from a spreadsheet or legacy CRM to a clean database. The importer handles field mapping, de-duplication, and validation before any record writes — so the errors you fix are the errors in your source data, not errors created by the import itself.
TL;DR
- Field mapper connects any CSV column to any GrantPipe donor or gift field
- De-duplication runs on email and normalized name-plus-address before import completes
- Validation preview shows all errors by row and column before a single record writes
- Giving history imports in the same file or in a separate pass
- Large files (50K+ rows) process in background batches with progress tracking
What this feature does
The CSV import takes a file from your current system — a spreadsheet, a Blackbaud export, a Salesforce CSV, a DonorPerfect extract — and walks it into GrantPipe through three steps: map the fields, review flagged rows, confirm and import.
The field mapper handles the reality that every system names columns differently. “First Name” in your file might be “fname” or “Contact: First Name” or “Donor First.” You drag and drop until every column you want is linked to a GrantPipe field. Columns you do not map are ignored.
The validation step reads the entire file and surfaces every error before the import runs: missing required fields, duplicate email addresses, date values it cannot parse, currency values with unexpected formatting. You see the error count by category, and you can download a report listing each flagged row. Fix the source file and re-upload, or accept the import without the flagged rows.
The de-duplication step compares incoming records against existing donors and against each other. Matches are flagged for review, not automatically merged. You decide whether to merge or create a separate record.
Who it’s for
Development directors migrating from a spreadsheet-based donor tracking system. Finance staff handling a CRM switch after the organization outgrew its previous platform. Operations teams that received a donor database from a predecessor organization and need to bring it into the current system without losing the giving history.
Workflow example
- Export donors from your current system as a CSV (most platforms have an export button; spreadsheets save directly to CSV)
- Upload the CSV to the GrantPipe import screen
- Map each column header to the corresponding GrantPipe field
- Review the validation report — fix any flagged rows in the source file or accept the import without them
- Review de-duplication flags — merge, skip, or create for each probable duplicate
- Confirm the import; records write in a single transaction
- Run a second import for giving history if you imported donors-only in the first pass
The total time for a 5,000-donor file with clean source data is typically under 30 minutes from export to completed import.
Why clean source data matters
The importer surfaces problems in source data it cannot resolve programmatically. The most common: multiple records for the same person under different name formats, addresses that do not parse cleanly, and gift rows that reference donor records not yet in the system.
Most of these are fixable in the source file before the import runs. The validation report tells you exactly which rows to fix. Spending 20 minutes cleaning the export before re-uploading is faster than correcting records one at a time after import.
Integration with the rest of GrantPipe
Imported donor records work identically to manually created records. They appear in segments, donor reports, and communications history. Imported gift records appear in giving history and roll up into retention and LYBUNT reports. The full import history is logged in the audit trail — every batch is timestamped with the importing user and the file name.
What it replaces
- Manual data entry for donor lists received as spreadsheets
- The consultant engagement most CRM vendors require for a data migration
- The multi-day import processes used by legacy platforms that process in overnight batches
- The partial-import failures that leave databases in inconsistent state
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Q&A
What is the most common reason CSV donor imports fail?
Inconsistent household formatting. A spreadsheet that has 'John and Jane Smith' in one row and 'Smith, John' in another cannot be de-duplicated automatically. Standardizing name columns before import — last name, first name as separate fields — eliminates the majority of import failures and review flags.
Q&A
How do I migrate from Salesforce NPSP using CSV import?
Export contacts, accounts, and opportunities from Salesforce as separate CSVs. Import donors first (contacts/accounts), then gifts (opportunities linked to contact records by email or external ID). The field mapper handles the Salesforce column naming conventions. Most NPSP-to-GrantPipe migrations using the CSV path complete within one business day.
Q&A
Does CSV import support recurring gift history?
Yes. Include a 'gift type' or 'recurrence' column in the giving history CSV and map it to the recurring flag. Historical recurring gift records appear in the donor's giving timeline. Active recurring schedules must be re-created manually or via the recurring-gift import path.
Q&A
What format should the CSV be in?
UTF-8 encoded CSV with a header row. Comma-delimited. Date fields should be ISO 8601 (YYYY-MM-DD) or US format (MM/DD/YYYY) — the importer auto-detects the format from the first ten rows. Currency values should be numeric, no dollar sign or commas.
Frequently asked