Standard Matching Rule for Leads on Accounts

The standard matching rule for leads on accounts is available for use only with certain Pardot licenses. The rule is activated automatically when you add the Matched Leads component to an account page layout. By reducing redundant data, the rule and the component help you work toward complying with various data protection and privacy regulations.
Note

Note

The Standard Matching Rule for Leads on Accounts works best when your accounts have values in the account name, phone, address, and city fields.

Matching Equation

Rule Name Matching Equation
Standard Matching Rule for Leads on Accounts (Account Name AND Billing Street)

OR (Account Name AND City AND State)

OR (Account Name AND ZIP)

OR (Account Name AND Phone)

OR (Website)

Matching Criteria

Field Matching Algorithms Scoring Method Threshold Blank Fields Special Handling
Account Name Acronym

Edit Distance

Exact

Maximum 70 Don’t match Removes words such as Inc and Corp before comparing fields. Also, company names are normalized. For example, 1st National Bank is normalized to First National Bank.
Phone Exact Weighted Average 80 Don’t match on all sections except Area Code, which ignores blank fields Phone numbers are broken into sections and compared by those sections. Each section has its own matching method and match score. The section scores are weighted to come up with one score for the field. This process works best with North American data.
  • International code (Exact, 10% of field’s match score)
  • Area code (Exact, 50% of field’s match score)
  • Next 3 digits (Exact, 30% of field’s match score
  • Last 4 digits (Exact, 10% of field’s match score)

For example, suppose that these two phone numbers are being compared: 1-415-555-1234 and 1-415-555-5678.

All sections match exactly except the last 4 digits, so the field has a match score of 90, which is considered a match because it exceeds the threshold of 80.

Billing Street Edit Distance

Exact

Weighted Average 80 Don’t match Addresses are broken into sections and compared by those sections. Each section has its own matching method and match score. The section scores are weighted to come up with one score for the field. This process works best with North American data.
  • Street Number (Exact, 20% of field’s match score)
  • Street Name (Edit Distance, 50% of field’s match score)
  • Street Suffix (Exact, 15% of field’s match score)
  • Suite Number (Exact, 15% of field’s match score)

For example, suppose that these two billing streets are being compared: 123 Market Street, Suite 100, and 123 Market Drive, Suite 300.

Because only the street number and street name match, the field has a match score of 70, which is not considered a match because it’s less than the threshold of 80.

ZIP Exact Weighted Average 80 Don’t match ZIP codes are broken into sections and compared by those sections. Each section has its own matching method and match score. The section scores are weighted to come up with one score for the field.
  • First 5 digits (Exact, 90% of field’s match score)
  • Next 4 digits (Exact, 10% of field’s match score)

For example, suppose that these two ZIP codes are being compared: 94104-1001 and 94104.

Because only the first five digits match, the field has a match score of 90, which is considered a match because it exceeds the threshold of 80.

City Edit Distance

Exact

Maximum 85 Don’t match
Website Exact Maximum 100 Don’t match The prefix http:// is appended to the website domain. For example, a field value www.salesforce.com becomes http://www.salesforce.com for matching purposes. Matching for an account record that has a website without thehttp:// prefix identifies the record as a duplicate.