Every CRM import is a risk. Import dirty data and you pollute your database with duplicates, invalid contacts, broken formatting, and records that will cause problems for months. Import clean data and you strengthen your database, improve campaign performance, and make your sales team more effective.
The difference between the two outcomes is usually 15-20 minutes of checking. This checklist gives you the 10 specific things to verify before every import, in the order you should check them. Print it, bookmark it, or share it with your team.
Check 1: Row Count and Column Inventory
Before anything else, confirm the basics. Open your CSV and verify:
- The row count matches what you expect. If you exported 5,000 records but the CSV has 4,200 rows, something was lost in the export.
- All expected columns are present. A missing email column or a merged name column (full name instead of separate first/last) will break your import mapping.
- The header row is actually a header, not the first data record. Some exports omit headers, causing the first record to be mapped as column names.
Time needed: 1 minute.
Check 2: Encoding and Special Characters
Open the CSV in a text editor (not Excel) and check for garbled characters. Names like "Jose" should show the accent mark correctly, not as "José" or "Jose?" Common encoding issues:
- UTF-8 file opened as ASCII or Latin-1 (accented characters become garbled)
- Windows-1252 encoded files with smart quotes and em dashes that do not convert properly
- Byte Order Mark (BOM) at the beginning of the file that some systems treat as part of the first column name
Fix: Re-save the file as UTF-8 without BOM. Most text editors and data tools offer this option.
Time needed: 2 minutes.
Check 3: Whitespace Problems
Whitespace issues are invisible in Excel but cause matching and validation failures in CRMs. Check for:
- Leading spaces: " John" instead of "John"
- Trailing spaces: "john@acme.com " instead of "john@acme.com"
- Double spaces: "New York" instead of "New York"
- Non-breaking spaces (character 160) that look like regular spaces but are not
- Tab characters embedded in field values
Fix: Apply TRIM to all text columns in Excel, or use a bulk cleaning tool.
Time needed: 3 minutes to check, 5 minutes to fix manually.
Check 4: Email Validity
Invalid emails cause bounces, hurt your sender reputation, and waste CRM storage. Check for:
- Missing @ symbol
- Common domain typos: gmial.com, yaho.com, hotmial.com
- Placeholder values: test@test.com, noemail@noemail.com, none@none.com
- Role-based addresses you may want to exclude: info@, sales@, support@
- Personal emails when you want business emails (or vice versa)
Fix: Remove or flag invalid emails. Consider running the list through an email verification service for large imports.
Time needed: 5 minutes for a visual scan, 2 minutes with a validation tool.
Check 5: Phone Number Formatting
Phone numbers are stored in dozens of formats across different systems. Before importing, standardize to your CRM's expected format:
- Decide on a format: (555) 123-4567, 555-123-4567, 5551234567, or +15551234567
- Remove non-digit characters if your CRM stores digits only
- Check for short numbers (fewer than 10 digits for US) that are likely invalid
- Handle international numbers if your data includes contacts outside the US
Time needed: 5 minutes.
Check 6: ZIP Code Integrity
ZIP codes break in predictable ways. Check for:
- 4-digit codes that need a leading zero (common for Northeast US: 01234, 02345, etc.)
- ZIP+4 codes that need truncation (90210-1234 to 90210)
- Floating-point artifacts (90210.0 instead of 90210)
- Non-numeric values (N/A, none, TBD)
Fix: Format the column as text and apply zero-padding: =TEXT(A2, "00000")
Time needed: 3 minutes.
Check 7: Duplicate Records Within the File
Before worrying about duplicates against your existing CRM data, check for duplicates within the import file itself. Sort by email address and scan for repeats. Check for near-duplicates by sorting by last name and scanning for the same person with different formatting.
Fix: Remove exact duplicates. For near-duplicates, merge the records manually or use a deduplication tool to identify and resolve them.
Time needed: 5 minutes for exact duplicate check, 15+ minutes for near-duplicate review.
Check 8: Required Field Completeness
Check your CRM's required fields for the object type you are importing. Common required fields include:
- Salesforce Contacts: Last Name (required), Account (usually required)
- Salesforce Leads: Last Name, Company (both required)
- HubSpot Contacts: Email (strongly recommended though not technically required)
Records missing required fields will either fail to import or create incomplete records that cause issues later. Count the empty cells in each required column and decide whether to fill them, remove those rows, or set a default value.
Time needed: 3 minutes.
Check 9: Picklist and Category Values
If your import includes fields that map to CRM picklists (dropdown fields), verify the values match exactly. Common mismatches:
- Lead Source: "Tradeshow" vs "Trade Show" vs "Trade show"
- Industry: "Healthcare" vs "Health Care" vs "Medical"
- State: "California" vs "CA" vs "Calif"
If values do not match your CRM's picklist options, the import may either reject the record, create a new picklist value (if the CRM allows), or leave the field blank.
Fix: Map all variant values to the exact CRM picklist values using find-and-replace.
Time needed: 5 minutes.
Check 10: Duplicate Check Against Existing CRM Data
The most important and most time-consuming check. Export your current CRM records and match them against your import file. At minimum, check for exact email matches. Ideally, also run fuzzy matching on name + company to catch records where the email differs but the person is the same.
Flag duplicates and decide: skip those records, update the existing CRM records with new information, or create a review queue for manual decision-making.
Time needed: 15-30 minutes manually, 5 minutes with a matching tool.
Automating the Checklist
Running these 10 checks manually takes 30-60 minutes per import. If you import data weekly or monthly, that adds up quickly.
ListMatchGenie automates every check on this list. Upload your CSV and the Data Health Check scans for whitespace issues, encoding problems, email validity, ZIP code formatting, duplicate records, and completeness statistics. The results page shows exactly what needs fixing, with one-click options to apply corrections before running your match or exporting the cleaned file.
Whether you use a tool or run through this checklist manually, the key is consistency. Every import should go through the same validation process. One dirty import can take weeks to clean up after the fact. Fifteen minutes of checking prevents that entirely.
