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Standard Person Account Matching Rule

The standard person account matching rule identifies duplicate person accounts using match keys, a matching equation, and matching criteria. To use the rule, first enable person accounts, and then activate rule in Setup.

Match Keys

Match keys speed up matching by narrowing the potential matches to the most likely duplicates before the rule applies the comprehensive matching equation.

Match Key Notation Examples
Email

Email: john_doe@us.ibm.com = johndoe@ibm.com

Key: johndoe@ibm.com
First_Initial (1,1) Last_Name City (1,6)

First Initial: J = j

Last Name: Doe = doe = t (with double metaphone applied)

City: Philadelphia = philad

Key: jtphilad
First_Initial (1,1) Last_Name ZIP (1,3)

First Initial: J = j

Last Name: Doe = doe = t (with double metaphone applied)

ZIP: 10001 = 100

Key: jt100

Street Address

123 Maple Avenue

Key: 123maple
Phone (drop last four digits)

415-555-1234

Key: 415555

555-1234-5678

Key: 5551234

Matching Equation

The threshold for the first three conditions in the equation is 85; the threshold for the fourth condition is 75.

(First Name AND Last Name AND Email)

OR (First Name AND Last Name AND Billing Street AND (City OR ZIP))

OR (First Name AND Last Name AND Phone )

OR (First Name AND Last Name AND Phone AND (City OR ZIP) AND Mailing Street AND Phone)

Matching Criteria

Fields on Contacts Fields on Leads Matching Algorithms Scoring Method Threshold Blank Fields Special Handling
First Name First Name

Exact

Initials

Jaro-Winkler Distance

Metaphone 3

Name Variant

 Maximum 85 and 75 Don’t match (ignores blank fields when Email is included in field grouping)

If the record contains a value for both the First Name and Last Name fields, the values are transposed to account for possible data entry mistakes.

For example, suppose that the first name is George and the last name is Michael. The matching rule also evaluates the first name as Michael and the last name as George.

Last Name Last Name

Exact

Keyboard Distance

Metaphone 3

 Maximum 90 and 75 Don’t match (ignores blank fields when Email is included in field grouping)

If the record contains a value for both the First Name and Last Name fields, the values are transposed to account for possible data entry mistakes.

For example, suppose that the first name is George and the last name is Michael. The matching rule also evaluates the first name as Michael and the last name as George.

Account Name Company

Acronym

Edit Distance

Exact

 Maximum 70 Don’t match  
Email Email Exact Maximum 100 Don’t match  
Phone 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 determine a single 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 three digits (exact, 30% of field’s match score
  • Last four digits (exact, 10% of field’s match score)

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

All sections match exactly except the last four digits. The field has a match score of 90, which is considered a match because it exceeds the threshold of 80.
Billing Street 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 determine a single score for the field. This process works best with North American data.

  • Street Name (Edit Distance, 50% of field’s match score)
  • Street Number (exact, 20% 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, these addresses are being compared: 123 Market Street, Suite 100, and 123 Market Drive, Suite 300.

Only the street number and street name match. The field has a match score of 70, which isn’t considered a match because it’s less than the threshold of 80.
Billing ZIP/Postal Code ZIP/Postal Code Exact Weighted Average 80  

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 determine a single score for the field.

  • First five digits (exact, 90% of field’s match score)
  • Next four digits (exact, 10% of field’s match score)
Billing City City

Edit Distance

Exact

 Maximum 85 Don’t match  

See Also