Clean Your CSV Before a CRM Import: A Small QA Checklist

Clean Your CSV Before a CRM Import: A Small QA Checklist

Importing a messy CSV into a CRM can create hours of cleanup later.

The problem usually is not the CRM itself. It is the source file: duplicate contacts, mixed date formats, broken email fields, inconsistent company names, blank required fields, and notes that were pasted into the wrong columns.

Before you upload a spreadsheet into HubSpot, Salesforce, Zoho, Airtable, Notion, or another client database, run a small QA pass.

What To Check First

Start with the fields that can break the import:

- Email addresses

- Phone numbers

- First and last names

- Company names

- Country, state, or region fields

- Dates

- Lead source

- Notes

- Owner or assignee fields

- Required CRM fields

Then check for values that look correct at a glance but will create duplicate or unusable records later.

Common CSV Cleanup Problems

Here are the issues I would check before sending a CSV to a client or importing it into a CRM:

- Duplicate rows with slightly different names

- Blank email fields in rows that need a contact record

- Multiple people stored in one contact field

- Inconsistent company names, such as Acme Inc, ACME, and Acme Incorporated

- Phone numbers with mixed country formats

- Dates written in more than one format

- Pasted notes that spill into the wrong column

- Hidden blank rows

- Merged cells from the original spreadsheet

- Columns that do not match the CRM import template

These are small problems, but they matter because automation tools depend on clean inputs.

A Simple CSV QA Workflow

1. Make a backup of the original file.

2. Freeze the header row and check every column name.

3. Remove empty rows and columns.

4. Check required CRM fields.

5. Standardize date, phone, country, and company-name formats.

6. Sort by email and company to spot duplicates.

7. Create an unclear-values log instead of guessing.

8. Save a clean copy and a delivery note.

The goal is not to make the spreadsheet look pretty. The goal is to make the data safer to import, easier to review, and clearer for the next person in the workflow.

Useful Download

I made a free spreadsheet cleanup QA checklist here:

Download the free spreadsheet cleanup QA checklist

If you want the full workflow with templates, sample files, an unclear-values log, and a delivery note, the AI Data Cleanup QA Starter Kit is here:

Get the AI Data Cleanup QA Starter Kit

For a small done-for-you cleanup task, the direct service page is here:

Book a small data cleanup task

Clean data is not glamorous, but it is one of the easiest ways to make AI workflows, CRM imports, dashboards, and client handoffs less painful.