Set Up Duplicate Matching Condition

Duplicate matching conditions enable you to define the primary and matching entity fields of records for detecting duplicates based on various matching methods.

For example, to detect duplicates in the ‘First Name’ field of contact entity record, you will have to define a duplicate matching condition for detecting duplicates between the ‘First Name’ fields of contact entity records.

To set up Duplicate Matching Conditions, follow the steps given below:

Once you have set up the ‘Duplicate Matching Rule’, click on the ‘New Duplicate Matching Condition’ button.

This will open up a ‘New Duplicate Matching Condition’ quick-create form, where we need to fill in the following details:

Primary Entity Field: Select the entity field for which you want to enable duplicate detection. For e.g. Here we will select ‘First Name’.

Matching Entity Field: Select the matching entity field in which you want to detect duplicates. The fields in the Matching Entity Field dropdown will be of the same data type as the field selected in the Primary Entity Field.

Matching Method: Here you can select a matching method for duplicate detection from four different matching method options:
- Exact Matching Method: With this method, you can detect duplicates with exactly same characters.
  For example, if an existing contact record contains first name ‘Smith’ and a new record is also created with first name ‘Smith’, then by using this method, you can detect duplicate records that contain fields with exactly same characters.
- First N Characters Matching Method: With this method, you can detect duplicates in the record fields based on same first N characters. Additionally, you can also define the number of characters for duplicate detection; let’s say you select ‘3’.
  For Example, if an existing contact record’s first name is ‘Smith’ and a new record is also created with the same first name as ‘Smith’, then by using this method, you can detect duplicate records containing same first ‘3’ characters as the existing record, i.e., ‘Smi’.
- Last N Characters Matching Method: With this method, you can detect duplicates in record fields based on the same last N characters. Additionally, you can also define the number of characters for duplicate detection; let’s say you select ‘3’.
  For Example, if an existing contact record’s first name is ‘Smith’ and a new record is also created with a first name as ‘Smith’, then by using this method, you can detect duplicate records containing the same last ‘3’ characters as the existing record, i.e., ‘ith’.
- Contains Matching Method: With this method, you can detect duplicates based on any character in a primary entity field.
  For Example, if an existing contact record's first name is ‘Smith’ and a new record is created with the first name 'Smit', then by using this method, you can detect duplicate records containing the word ‘Smit’ from the existing record.
- Fuzzy Matching Method: With this method, you can identify duplicates by comparing the phonetic similarity of records, even if they're spelled differently.
  
  For example, it can detect that "John Smith" and "Jon Smyth" are likely two contact records for the same person, despite variations in spelling. This method ensures that similar-sounding names or terms are accurately identified and merged, enhancing data accuracy and consistency.
Ignore Blank: You can select whether to ignore blank fields while executing duplicate matching conditions. By default, it is set to ‘Yes’.
For Example, if you select ‘Ignore Blanks’ as ‘Yes’ and have an existing contact record with a blank ‘First Name’ field and you create a new record with a blank ‘First Name’ field, then the matching condition will ignore the blank field while duplicate detection, and let you create a new contact record with a blank ‘First Name’ field.
If you select ‘Ignore Blanks’ as ‘No’ then the matching condition will not ignore the blank field while duplicate detection, and it will not let you create a new contact record with a blank ‘First Name’ field.