--- schema_version: "1" id: f0cd707cb0a89753a427cc3f34c9a38764cc00b7a6650a7d953f3a229185e76b title: Standard Contact Matching Rule and Standard Lead 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_contact_rule.md canonical_url: https://help.salesforce.com/s/articleView?id=sales.matching_rules_standard_contact_rule.htm&release=264.0.0&type=5 source_site: help.salesforce.com content_type: documentation content_hash: 8a240d69277fb67d4962b745473491fd236dab14e6e0492829bedfcc57f5ea6d description: The standard contact matching rule and standard lead matching rule identify duplicate contacts and leads using match keys, a matching equation, and matching criteria. They’re activated by default. products: - Sales - Sales Enablement release: "264" --- # Standard Contact Matching Rule and Standard Lead Matching Rule The standard contact matching rule and standard lead matching rule identify duplicate contacts and leads using match keys, a matching equation, and matching criteria. They’re activated by default. ## 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.
Contact Match Key Notation Contact Examples
Email

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

Key: johndoe@ibm.com
First_Name (1,1) Last_Name Email

First Name: John = j

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

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

Key: jt@salesforce.com
First_Name (1,1) Last_Name Company (2,5)

First Name: Marc = m

Last Name: Benioff = pnf (with double metaphone applied)

Company: salesforce.com = sales

Key: mpnfsales
First_Name (1,1) Last_Name Phone (drop leading 1 and last four digits)

First Name: Marc = m

Last Name: Benioff = pnf (with double metaphone applied)

Phone: 1-415-555-1234 = 415555

Key: mpnf415555
Lead Match Key Notation Lead Examples
Email

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

Key: johndoe@ibm.com
First_Name (1,1) Last_Name Email

First Name: John = j

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

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

Key: jt@salesforce.com
First_Name (1,1) Last_Name Company (2,5)

First Name: Marc = m

Last Name: Benioff = pnf (with double metaphone applied)

Company: salesforce.com = sales

Key: mpnfsales
First_Name (1,1) Last_Name Phone (drop leading 1 and last four digits)

First Name: Marc = m

Last Name: Benioff = pnf (with double metaphone applied)

Phone: 1-415-555-1234 = 415555

Key: mpnf415555
## Matching Equations The standard contact matching rule uses the following matching equation. (First Name AND Last Name AND Title AND Account Name) OR (First Name AND Last Name AND Email) OR (First Name AND Last Name AND Phone AND Account Name) OR (First Name AND Last Name AND Mailing Street AND (City OR ZIP OR Phone)) OR (First Name AND Last Name AND Mailing Street AND Title) OR (First Name AND Last Name AND Title AND Email) OR (First Name AND Last Name AND Phone) The standard lead matching rule uses the following matching equation. (First Name AND Last Name AND Title AND Company) OR (First Name AND Last Name AND Email) OR (First Name AND Last Name AND Phone AND Company) OR (First Name AND Last Name AND Mailing Street AND (City OR ZIP OR Phone)) OR (First Name AND Last Name AND Mailing Street AND Title) OR (First Name AND Last Name AND Title AND Email) OR (First Name AND Last Name 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 Don’t match (ignores blank fields when Email is included in field grouping)

If record contains a value for 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 Felix and the last name is Michael. The matching rule also evaluates the first name as Michael and the last name as Felix.
Last Name Last Name

Exact

Keyboard Distance

Metaphone 3

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

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

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

Acronym

Exact

Kullback-Liebler Distance

 Maximum 50 Don’t match  
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 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.
Mailing 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 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.

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.
Mailing ZIP/Postal Code ZIP/Postal Code 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 determine a score for the field.

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

Edit Distance

Exact

 Maximum 85 Don’t match  
#### See Also - [Standard Contact Duplicate Rule](https://help.salesforce.com/s/articleView?id=sales.duplicate_rules_standard_contact_rule.htm&release=264.0.0&type=5 "The standard contact duplicate rule defines what happens when a user views a duplicated contact or starts to create a record that duplicates a contact. If your org was created in Summer ’17 or later, this duplicate rule includes a cross-object matching rule: It matches contacts that duplicate other contacts and leads that duplicate contacts. If your org was created before Summer ’17, you can get the same functionality by activating a new duplicate rule available in Setup. Otherwise, your default rule matches only contacts that duplicate other contacts.") - [Standard Lead Duplicate Rule](https://help.salesforce.com/s/articleView?id=sales.duplicate_rules_standard_lead_rule.htm&release=264.0.0&type=5 "The standard lead duplicate rule defines what happens when a user views a lead with duplicates or starts to create a record that duplicates a lead. If your org was created in Summer ’17 or later, this duplicate rule includes a cross-object matching rule: It matches leads that duplicate other leads and contacts that duplicate leads. If your org was created before Summer ’17, you can get the same functionality by activating a new duplicate rule available in Setup. Otherwise, your default rule matches only leads that duplicate other leads.") - [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.")