What is the different between merge/purge and duplicate elimination?
Are the three standard options the only methods for determining duplicates?
What determines which record(s) will be kept and which will be dropped?
What if the record chosen to be dropped has important information?
Merge/Purge is our specialty. It identifies duplicate records in your list or from multiple lists.
What is the different between merge/purge and duplicate elimination?
The terms merge/purge and duplicate elimination are often used interchangeably. Duplicate elimination is a more accurate term when there is only one file involved.
What are the benefits of Merge/Purge?
Merge/Purge is an essential list maintenance tool and includes Preprocessing Optimization, a friendly return file and easy-to-read statistics.
What are the criteria used to determine duplicates?
There are three standard options for determining duplicates:
One Per Person - The matching is based on first name, last name, and address information. First names are standardized for matching purposes only (Bill matches with William, Peggy with Margaret, etc.). Input name data is not changed. First names are matched to first initials (J. Smith matches with John Smith). One record per first and last name and complete address will be retained.
One Per Household - One per household means the matching is based on last name and address information. For example, roommates with different last names are retained. Most hyphenated last names are matched to non-hyphenated last names (John Smith matches with Nancy Nelson-Smith). One record per last name and complete address will be retained.
One Per Address - One per address means the matching is based on address information only. Last names are only considered on rural route addresses with no box number. One record per complete address will be retained.
Are the three standard options the only methods for determining duplicates?
No. Custom matching can be based on any information contained in the lists. Some examples of custom matching options are telephone number comparison, account number comparison, social security number comparison, and one per company.
What determines which record(s) will be kept and which will be dropped?
List priority, field priority and random priority are three ways to determine which records will be kept, and which will be dropped. If you have more than one list, list priority may be set. For example, List A records have priority over List B records, etc. Field priority determines which records are dropped first based on the value in a field. For example, records coded as male have priority over records coded as female. If you have no preference, random priority may be set.
What if the record chosen to be dropped has important information?
Useful data can be copied from drop records to keep records before being discarded. Call for more information.
Will Merge/Purge find all the duplicates?
When including "address information" in the match criteria, only domestic
(United States) addresses are considered unless otherwise requested. Foreign records are passed through to the output file without duplicate detection or are eliminated prior to processing. Domestic addresses are standardized prior to processing, but those records that have a delivery address that can not be parsed (broken down into house number, pre-directional, street name, street suffix, post-directional) will also be passed through to the output file without duplicate detection. It is not uncommon to find university campus, government, building and school addresses that can not be parsed. When including name information in the match criteria, the way the data is formatted directly affects the results. Names must be formatted in a logical sequence. E.g., “first name, middle name, last name” or “last name, comma, first name, middle name”. If names are formatted out of logical sequence, e.g., “last name, first name, middle name” with no delimiter between last name and first name or “last name, first name, suffix”, the name matching will not be accurate, so duplicates may be missed or false matches may occur. Extraneous data such as a professional title must be separated from the name by a delimiter or be in a discrete field.
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