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Matching Methods Used in Matching Rules
The matching method determines how a specific field in a record is compared to the same field in another record. Each matching method is defined by normalization criteria, match key definitions, and matching algorithms.
Required Editions
| Available in: Lightning Experience and Salesforce Classic |
| Available in: Essentials, Professional, Enterprise, Performance, Unlimited, and Developer Editions |
The exact matching method looks for strings that exactly match a pattern. If you’re using international data, we recommend using the exact matching method with your matching rules. You can use the exact matching method for almost any field, including custom fields.
The fuzzy matching methods look for strings that approximately match a pattern. Some fuzzy matching methods, such as Acronym and Name Variant, identify similarities using hard-coded dictionaries. Because the dictionaries aren’t comprehensive, results can include unexpected or missing matches. Specific fuzzy matching methods are available for commonly used standard fields on accounts, contacts, and leads.
| Matching Method | Matching Algorithms | Scoring Method | Threshold | Special Handling |
|---|---|---|---|---|
| Exact | Exact | |||
| Fuzzy: First Name | Exact Initials Jaro-Winkler Name Variant |
Maximum | 85 | If the Middle Name field is used in your matching rule, it’s compared using the Fuzzy: First Name matching method. |
| Fuzzy: Last Name | Exact Keyboard Distance Metaphone 3 |
Maximum | 90 | |
| Fuzzy: Company Name | Acronym Exact Syllable Alignment |
Maximum | 70 | Removes words such as “Inc” and “Corp” before comparing fields. Also, company names are normalized. For example, “IBM” is normalized to “International Business Machines”. |
| Fuzzy: Phone | Exact | Weighted Average | 80 | 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.
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, so the field has a match score of 90. This score is considered a match because it exceeds the threshold of 80. |
| Fuzzy: City | Edit Distance Exact |
Maximum | 85 | |
| Fuzzy: Street | Exact | Weighted Average | 80 | 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.
For example, these billing streets are being compared: 123 Market Street, Suite 100, and 123 Market Drive, Suite 300. Only the street number and street name match, so the field has a match score of 70. This score isn’t considered a match because it falls below the threshold of 80. |
| Fuzzy: ZIP | 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 come up with one score for the field.
For example, these ZIP codes are being compared: 94104-1001 and 94104. Only the first five digits match, so the field has a match score of 90. This score is considered a match because it exceeds the threshold of 80. |
| Fuzzy: Title | Acronym Exact Kullback-Liebler Distance |
Maximum | 50 |
