We took the same CSV file, a real export of 500 contacts from a Google Sheets sales tracker with 18 columns including a "Full Name" column, phone numbers in mixed formats, a "Company" column with inconsistent casing, and 3 custom columns, and imported it into three CRMs: SalesSheet, Pipedrive, and Copper. We timed each import from file upload to confirmed data in the CRM. We counted every click and every manual correction. Here is what happened.
CSV import is the first real interaction most users have with a CRM. If it is painful, everything that follows feels harder than it should.
Drag the CSV onto the import area in Settings. The file uploads and SalesSheet immediately shows a preview of the first 10 rows with detected column types. The header row is identified automatically. Encoding is detected as UTF-8 (our file had accented characters in company names).
SalesSheet's AI auto-mapper correctly matched 15 of our 18 columns on the first attempt. "Full Name" was recognized and flagged for smart splitting into first and last name. "Phone" was detected despite our column being labeled "Phone Number (Work)." "Company" mapped to organization. The three custom columns ("Lead Source," "Deal Size," and "Notes") were shown as unmapped, with a one-click option to create matching custom fields.
The smart name splitting deserves detail. Our "Full Name" column contained entries like "John Smith," "Mary Jane Watson," "Jose Luis Garcia Fernandez," and "Kim." SalesSheet's AI parser split all of these correctly: "John/Smith," "Mary Jane/Watson," "Jose Luis/Garcia Fernandez," and "Kim/(empty)." No manual corrections needed. The parser handled compound first names, dual surnames, and single-name entries without error.
The review screen flagged 8 duplicate emails, 12 phone numbers in non-standard formats (auto-corrected to E.164), and 2 rows with empty email fields. We chose to merge duplicates, accepted the phone corrections, and imported the empty-email rows with a "needs email" tag automatically applied. Total: 500 contacts imported, 0 errors, 0 manual field corrections.
Pipedrive's import is under Settings, then Tools and apps, then Import data, then the Import from spreadsheet option. Four clicks to get to the upload screen. Not terrible, but deeper in the menu than it should be for something users do on day one.
Upload the CSV and select "People" as the import type. Pipedrive requires you to declare what you are importing before it processes the file. If your CSV has both contacts and deals mixed together, you need separate imports for each.
Pipedrive's auto-mapping correctly identified 10 of 18 columns. "Full Name" was mapped to the "Name" field, but Pipedrive does not split it automatically. The entire name goes into a single name field, which means "Jose Luis Garcia Fernandez" appears as the contact's full name with no first/last separation. If you want separate fields, you need to split the column in your spreadsheet before importing.
"Phone Number (Work)" was not auto-detected because Pipedrive's mapper looks for exact header matches, and its phone field is labeled "Phone" not "Phone Number." We had to manually drag-and-drop 8 columns to their correct fields. The custom columns required creating custom fields in Pipedrive first, then mapping to them, an extra 3 clicks per custom field.
Pipedrive shows a duplicate detection screen but only matches on exact email. Our 8 duplicates were flagged correctly. However, the duplicate handling options are limited: skip, update, or create new. There is no merge option that combines data from both records. We chose "update" which overwrites the existing contact with the import data, losing any notes or activity history on the original record.
The actual import took about 2 minutes for 500 contacts, noticeably slower than SalesSheet's near-instant import. During the import, you cannot navigate away from the page or you risk an incomplete import. After completion, a summary screen showed 492 successful imports and 8 updates. No information about the phone number formatting or the non-standard entries. They were imported as-is, formatting inconsistencies and all.
Copper recommends downloading their CSV template first and reformatting your data to match their column headers before uploading. This is an extra step that SalesSheet and Pipedrive do not require. The template has specific column names like "First Name," "Last Name," "Company Name," "Email Address," which means you need to rename your columns before importing.
Because Copper requires separate "First Name" and "Last Name" columns and our file had a single "Full Name" column, we had to open the CSV in Google Sheets and manually split the name column using a formula. For simple "First Last" names this is straightforward, but for compound names and dual surnames, the formula approach produces errors. We spent 3 minutes fixing name splits manually for about 30 of the 500 rows.
After reformatting, the upload itself was quick. Copper accepted the file and showed a column mapping screen.
Copper's auto-mapping worked well once the columns matched their expected format. 16 of 18 columns mapped correctly (the two custom columns needed manual mapping). However, Copper does not support creating custom fields during import. You have to leave the import flow, go to Settings, create the custom field, then come back and restart the import. We lost our mapping progress when we navigated away.
After creating the custom fields in Settings, we had to re-upload the CSV and re-map all columns. The auto-mapper picked up the same mappings as before, but we still had to verify each one and manually assign the two custom fields.
Copper's review screen is minimal. It shows a count of rows to be imported and any rows with errors (we had 2 rows with invalid email formats). No preview of the data, no duplicate detection at this stage. Duplicates are handled after import, which means you can end up with duplicate contacts that you then have to find and merge manually.
The import completed in about a minute. Post-import, we discovered 8 duplicate contacts that Copper did not flag. We had to search for and merge them manually, adding another 5 minutes to the effective import time. The phone numbers were imported without any formatting normalization.
A bad import creates data debt that compounds over the life of your CRM. Every unsplit name means someone eventually has to fix it manually or live with it. Every unnormalized phone number means your click-to-call feature might dial the wrong format. Every undetected duplicate means two reps might reach out to the same prospect independently, which is embarrassing and unprofessional.
The 7 minutes you save on a smart import today prevents 7 hours of data cleanup next quarter.
SalesSheet's import was built with the understanding that CSV files are messy. They come from spreadsheets maintained by people who do not think about data types, character encoding, or naming conventions. The importer's job is to absorb that mess and produce clean, normalized, enriched contacts on the other side. Not to require you to clean your data before the CRM will accept it.
After import, SalesSheet automatically triggers Layer 0 enrichment on every contact. Within 30 seconds of completing the import, your 500 contacts start showing company logos, headquarters locations, and employee counts. Neither Pipedrive nor Copper offer automatic post-import enrichment on any plan.
The enrichment step transforms an import from a data loading exercise into a database building exercise. You start with names and emails. You end with rich profiles that give you context for every conversation. That difference in starting quality affects every sales activity that follows.
If you are evaluating CRMs and CSV import is part of your workflow, try importing the same file into each CRM you are considering. The import experience tells you everything about how the vendor thinks about user experience and data quality. A CRM that makes import painful will make everything else painful too.
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