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.
|
| 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.
|
