How to Prepare CSV Data for a CRM Import

Updated 26 September 2026

Whether you're moving to HubSpot, Zoho CRM, Salesforce or Pipedrive, the import will only be as good as the file. This guide covers the preparation steps that apply to every CRM, in the order that avoids rework.

1. Start from a raw export and keep it

Export from the source system as CSV (UTF-8 if offered) or Excel, and keep that file untouched. Every cleanup step should produce a new file, so you can always compare against — or go back to — the original.

2. Get the structure right

Headers in the first row, one per column, no duplicates or blanks.
One record per row; no subtotal, title or note rows.
Remove empty rows and columns.
Split records by object if the CRM imports them separately (contacts, companies, deals).

3. Check required fields

Each CRM requires certain fields for each object — typically an email or last name for contacts and a name for companies and deals. Find rows missing those values before the import rejects them, and decide whether to complete them or leave them out.

4. Standardise formats

Field	Recommended format
Email	Trimmed, lowercase, valid
Phone	E.164, e.g. +919876543210
Date	YYYY-MM-DD
Country	One spelling per country (name or ISO code — match the CRM's options)
Names	Consistent capitalisation; leave intentional mixed case (McDonald) alone
IDs & codes	Stored as text so leading zeros survive

5. Deduplicate last

Remove duplicates after standardising, so formatting differences don't hide them. Deduplicate contacts by email, companies by domain or name, and use a composite key (first name + last name + phone) when there's no reliable identifier. Keep the most complete record.

6. Validate, then test with a small import

Run a final check on the cleaned file: no invalid emails left unintentionally, no unreadable dates, no missing required values. Then import a small sample (20–50 rows) first and check how fields landed in the CRM before importing everything.

7. Make it repeatable

Migrations rarely happen once — there's usually a second export, a delta or a weekly sync. Record your cleanup steps so the next file is prepared identically. In TheSpreadFix, save the fixes as a recipe and run it on the next export in one click.

CSV Cleaner — Find duplicates, invalid emails and inconsistent formats — fix them in minutes.
Excel Cleaner — Clean .xlsx data without formulas or macros — leading zeros stay intact.
Remove Duplicates from CSV — Deduplicate by whole row, email, ID or several columns — you choose what to keep.
Remove Duplicates from Excel — Finds the duplicates Excel misses — and shows exactly which rows go.
Remove Duplicate Emails — One row per email address — even when capitals and spaces hide the duplicates.
Phone Number Normalizer — Convert messy phone numbers to one international format — +919876543210.
Date Format Standardizer — Mixed date formats to one — without guessing whether 03/04 is March or April.
Fix CSV Encoding — Garbled accents in Excel? Get clean UTF-8 that opens correctly everywhere.
Compare CSV Files — Find added, removed and changed rows between two versions of a file.
Merge CSV Files — Stack rows or join by a key — combine two files in seconds.
Validate CSV Data — Set rules for each column and find every row that breaks them.
Column Mapper — Auto-detect column matches and rename for your import destination.
Convert Excel to CSV — Upload an .xlsx and download a clean CSV — encoding, dates and leading zeros handled.
Convert CSV to Excel — Upload a CSV and get a clean .xlsx — leading zeros, dates and encoding handled.
CSV Viewer — Open any CSV in a clean table — encoding and delimiters detected automatically.
Remove Empty Rows — Find and remove blank, partial and whitespace-only rows in one click.
GSTIN Validator — Check GSTIN format in bulk — state code, PAN, entity code and check digit.
PAN Validator — Check PAN format in bulk — catch typos, truncation and invalid patterns.
Format Indian Phone Numbers — Normalize messy Indian phone numbers to +91 E.164 — in seconds.