--- schema_version: "1" id: bc3ebca6cdef709c5482bb44d9e433669522e9b133fe57c543a81ca92850f173 title: Standard Person Account Matching Rule collection: admin version: current locale: en-us portal_url: https://docs.salesforce.com/admin/current/en-us/sales/264-0-0/matching_rules_standard_person_account_rule.md canonical_url: https://help.salesforce.com/s/articleView?id=sales.matching_rules_standard_person_account_rule.htm&release=264.0.0&type=5 source_site: help.salesforce.com content_type: documentation content_hash: a9dd595e8638a82d09afc3cab2eea2a5de1f14f82ba4ee36957ac1940a1bd29f description: 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. products: - Sales - Sales Enablement release: "264" --- # 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 - [Enable Person Accounts](https://help.salesforce.com/s/articleView?id=sales.account_person_enable.htm&type=5&language=en_US) - [Matching Criteria in Matching Rules](https://help.salesforce.com/s/articleView?id=sales.matching_rule_matching_criteria.htm&release=264.0.0&type=5 "A matching rule applies criteria to determine how closely a field on a new or edited record matches the same field on an existing record. Standard matching rules include predefined criteria. When you create a custom matching rule, you define the criteria.") - [How Match Keys Are Generated and Used in Matching Rules](https://help.salesforce.com/s/articleView?id=sales.matching_rules_match_key_process.htm&release=264.0.0&type=5 "When a matching rule runs, it applies one or more match key formulas before applying a comprehensive matching equation. Match keys help to increase the performance of duplicate rules through a preliminary comparison that narrows the matches to the 100 most likely duplicate records. Then the rule applies the matching equation only to those likely matches.")