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            Matching Algorithms Used with Matching Methods

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            Matching Algorithms Used with Matching Methods

            The matching method and its corresponding matching algorithms are part of the matching rule’s matching criteria. They help determine how a specific field in one record is compared to the same field in another record and whether the fields are considered matches.

            Required Editions

            Available in: Lightning Experience and Salesforce Classic
            Available in: Essentials, Professional, Enterprise, Performance, Unlimited, and Developer Editions

            We provide an exact matching method and several fuzzy matching methods. When the exact matching method is selected, the exact matching algorithm is used to compare the fields. When a fuzzy matching method is selected, various fuzzy matching algorithms are used to compare the fields.

            More than one matching algorithm can be used to compare a field. Each matching algorithm is scored based on how closely it matches the fields. The fields being compared aren’t case sensitive.

            Matching Algorithms Available with the Exact Matching Method

            Matching Algorithm Description
            Exact Determines whether two strings are the same. For example, salesforce.com and Salesforce aren’t considered a match because they’re not identical. The algorithm returns a match score of 0.

            Matching Algorithms Available with Fuzzy Matching Methods

            Matching Algorithm Description
            Acronym Determines whether a business name matches its acronym. For example, Advanced Micro Devices and its abbreviation AMD are considered a match, returning a score of 100.
            Edit Distance Determines the similarity between two strings based on the number of deletions, insertions, and character replacements needed to transform one string into the other. For example, VP Sales matches VP of Sales with score of 73.
            Initials

            Determines the similarity of two sets of initials in personal names. For example, the first name Jonathan and its initial J match and return a score of 100.

            Note
            Note When First Name is set to fuzzy matching and Last Name is set to exact matching, in the index, the initials include the last letter of a contact’s first name. For example, Jane Smith is indexed as jesmith instead of jsmith. To match only the first initial of a first name, create a custom matching rule with both First Name and Last Name set to fuzzy matching.
            Jaro-Winkler Distance Determines the similarity between two strings based on the number of character replacements needed to transform one string into the other. This method is best for short strings, such as personal names. For example, Johnny matches Johny with a score of 97.
            Keyboard Distance Determines the similarity between two strings based on the number of deletions, insertions, and character replacements needed to transform one string into the other, weighted by the position of the keys on the keyboard.
            Kullback Liebler Distance Determines the similarity between two strings based on the percentage of words in common. For example, Director of Engineering matches Engineering Director with a score of 65.
            Metaphone 3 Determines the similarity between two strings based on their sounds. This algorithm attempts to account for the irregularities among languages and works well for first and last names. For example, Joseph matches Josef with a score of 100.
            Name Variant Determines whether two names are a variation of each other. For example, Bob is a variation of Robert and returns a match score of 100. Bob is not a variation of Bill and returns a score of 0.
            Syllable Alignment

            Determines the similarity between two strings based on their sounds. First, the character strings are converted into syllables strings. Then the syllable strings are also compared and scored using the Edit Distance algorithm. This matching algorithm works well for company names.

            For example, Syllable Alignment gives Department of Energy and Department of Labor a relatively low score of 59. The syllable sequences of these two company names differ more than their character sequences (energy sounds very different from labor). Edit Distance gives the two strings a score of 74. Therefore, Syllable Alignment works better because the two strings aren’t good match candidates.
             
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