Categorizing Contacts Prior to Import

1. Merging Spreadsheets from Multiple Sources

1. Gather All CSVs or Excel Files
   • Export your contacts from LinkedIn, Gmail/Outlook, iCloud, or any other table/spreadsheet you have.
   • If you have multiple files, consider copying or importing them all into one master spreadsheet (e.g., “Master Contacts.xlsx”).
2. Combine Into a Single Workbook or Master Sheet
   • Create a new master sheet in Excel or Google Sheets.
   • Copy/paste each file’s rows into the master, aligning columns correctly (e.g., First Name, Last Name, Email, Phone).
   • This consolidation ensures you have one source of truth for data cleaning.
3. Check for Inconsistent Columns
   • Different exports may have varying column headings (e.g., “Name” vs. “First Name” & “Last Name”).
   • Standardize these columns so you can clean and deduplicate data systematically.

2. Why Pre-Import Data Cleaning?

• Saves Time: Fixing issues in a spreadsheet is often faster than updating dozens or hundreds of records inside HubSpot.
• Better Data Hygiene from Day One: You’ll start with a clean CRM instead of dragging in legacy issues.
• Less Duplication: Identifying duplicates and consolidating them now avoids manual merges later in HubSpot.

3. Eliminating Duplicates

Identifying Duplicate Rows

1. Use Spreadsheet Tools
   • In Excel, you can use Conditional Formatting > Highlight Duplicate Values on email or name columns to spot duplicates.
   • Alternatively, use Data > Remove Duplicates (though be cautious not to remove partial matches).
2. Detecting Name Variations
   • Sometimes you’ll have two records for the same person, e.g., William Shakespeare on one row, Will Shakespeare on another.
   • Look for the same email address to confirm. If the email is absent, compare phone numbers, or look for matching last names plus a close first name.
3. Merging Duplicate Rows
   • Decide which row is the “most complete” record. Transfer any missing fields from the second row.
   • Delete the less-complete row once the info has been consolidated.

Handling Tricky Name Variations

• “William” vs. “Will”: Standardize to one consistent name if possible (e.g., “William (Will)” in the First Name field, or note the nickname in a separate column).
• Suffixes or Titles: If a LinkedIn record has “MBA” or “PhD” in the last name, move that to a separate note or remove it.

4. Practical Tips on Further Data Cleaning

First Name and Last Name Columns

1. Check for Spaces or Extra Characters
   • Some exports might include trailing spaces (“ T. Boone ”) or combine a first name and middle name in the same field.
   • Clean these up so the First Name column only contains the person’s actual first name (and middle initial if you desire).
2. Trim Titles and Degrees
   • LinkedIn exports sometimes include “, MBA” or “PhD” after the last name.
   • Move those credentials to a separate field or remove them if they’re not essential.

Email Addresses

1. Verify “@” and Valid Domains
   • Scan for emails missing the “@” symbol or containing invalid text (“example..com”).
   • Remove incomplete or test addresses (like “abc@abc”).
2. Consolidate Work vs. Personal Emails
   • If you have multiple emails for the same person, decide which one to keep or designate as “Primary Email.”

Phone Numbers

1. Standardize Phone Formats
   • Remove parentheses, dashes, or spaces if you want a consistent format. Alternatively, keep a consistent pattern like “+1 (555) 123-4567.”
   • Add country codes if known.
2. Separate Extensions
   • If someone has an extension, store it in a separate field if you plan to dial directly from a calling platform.

Additional Fields

1. Populate Key Properties
   • If you track “Industry,” “Metro Area,” or “Continent” in your custom fields, fill them in where possible before import.
   • Similarly, add LinkedIn profile URLs or job titles if your data is missing them.
2. Clear Out Unnecessary Columns
   • If there’s a column with data you’ll never use (like “Export Date” or “File Source”), consider deleting it before import to simplify your final file.

5. De-Dupe by Email vs. Names

• Use Email as a Primary Identifier: If an email is present, that’s typically the best unique identifier.
• Name Matching: If no email is provided, you may rely on name + phone or other data to confirm duplicates.

6. Checking for Consistency Before Import

• Spot-Check Random Rows: Pick 5–10 random rows and ensure the data in First Name, Last Name, and Email looks correct. Make sure you’re not missing any crucial fields.
• Standardize Column Headings: e.g., First Name, Last Name, Email, Phone Number, Company, etc. This makes the HubSpot import mapping smoother.
• Save a Backup: Before importing, save or copy the cleaned spreadsheet so you can revert if something goes wrong.

7. Final Steps: Ready for HubSpot

Once your data is clean and consolidated:

• Export to CSV (Comma-Separated Values): This is the format HubSpot typically reads most reliably.
• Use HubSpot’s Import Tool: In HubSpot, go to Contacts > Import. Map columns to the correct HubSpot fields or custom properties.
• Review the Import: Check HubSpot’s import summary to see if any rows failed or if there are duplicates. Address any flagged records if needed.