Matching Methods Used with Matching Rules

The Exact matching method looks for strings that match a pattern exactly. If you're using international data, we recommend you use the Exact matching method with your matching rules. We've provided an exact matching method that can be used for almost any field, including custom fields.

The Fuzzy matching methods look for strings that match a pattern approximately.

Matching Method	Matching Algorithms	Scoring Method	Threshold	Special Handling
Exact	Exact
Fuzzy: First Name	Exact Initials Jaro-Winkler Name Variant	Maximum	85	The Middle Name field, if used in your matching rule, is compared by 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 Corpbefore comparing fields. Also, company names are normalized. For example, IBMis 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. 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 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.
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. 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, suppose these two billing streets are being compared: 123 Market Street, Suite 100 and123 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.
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. First 5 digits (Exact, 90% of field's match score) Next 4 digits(Exact, 10% of field's match score) For example, suppose these two ZIP codes are being compared: 94104–1001and 94104. Because only the first 5 digits match, the field has a match score of 90, which is considered a match because it exceeds the threshold of 80.
Fuzzy: Title	Acronym Exact Kullback-Liebler Distance	Maximum	50