Luminate CRM synchronizes duplicate records from Luminate CRM to Luminate Online using API calls. The system checks for duplicates when you enter new contacts or accounts in Luminate CRM. The system also standardizes existing contact data and identifies potential duplicates, including data synchronized from Luminate Online.

Potential duplicates are listed on the Duplicate Management tab in Luminate CRM. Resolve duplicate records by marking them as not a duplicate or by merging them. Luminate CRM synchronizes the merged duplicates to Luminate Online.

The following functional changes were made to duplicate management in the Summer 2011 release:

• Pre-duplicate matching is automatically turned off and is no longer used in Luminate.
• Automated Duplicate Matching (ADM) is totally disabled in Luminate.

The duplicate matching tools include:

• Address Standardization, which corrects such things as abbreviations, placement, layout, and the order in which addresses appear as recommended by the United States Postal Service (USPS).
• Phonetic Matching Algorithm, which matches names with similar pronunciations, but differences in spelling, such as John Doe and Jon Doe.
• Name Variant, which matches different, common ways to represent the same name, such as Robert and Bobbie.
Preparing for Duplicate Management
Before you begin duplicate management, ensure that Luminate Online and Luminate CRM are working correctly. If a record exists in Luminate Online and Luminate CRM, Luminate CRM merges the records you select during duplicate management and synchronizes the changes with Luminate Online. If the records differ, Luminate CRM is the database of record and overwrites the Luminate Online record.

What is Used to Match Duplicates in Luminate CRM?
Luminate assumes that contact records match if and only if:
• First name is similar (sounds the same or same name group)
• Last name is similar (sounds the same)
• At least one of the following is true:
o Any of the phone numbers are similar (digits match ignoring area code and extension)
o Email is the same (also checks against other email field)
o Mailing address is similar with at least one of the following:
- Zip+4 is the same
- Zip + street (unparsed) is the same
- City + street (unparsed) is the same

The following also apply:
• If gender is known and different, records are not a match
• If date of birth is known and different, records are not a match
• If name suffix is known and different, records are not a match

Example
In the following example, if A matches B and B matches C, all three will become one match group, even if A does not match C:

• A = Alice Smith, null, asmith@example.com
• B = Alice Smith, 555-1212, asmith@example.com
• C = Alice Smith, 555-1212, null