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WinPure Clean & Match Software can be used to merge together duplicates from various sources to create one master record.
Merge Purge is the process of combining two or more lists or files, simultaneously identifying and/or combining duplicates and eliminating unwanted records. The goal of the merge/purge process is to produce a unique set of records, such as unique names and addresses. One goal is to cut down on waste by only sending one mailing piece to each person.
Merge Purge software tools like WinPure Clean & Match, can be used to perform merge purge tasks automatically, to help save on duplicate mailings and increase customer satisfaction. Download a free trial and start merging purging your lists and databases today.
The essence of the merge purge software is to create a list that will eventually contain only unique names and addresses without any duplicates. This is particularly important when a business or organization is trying to create a huge mailing list to market its goods and services.
A merge purge software application will clean up the data set to achieve the following benefits:
1. More efficient database performance leading to improved productivity
2. Cost savings due to the elimination of duplicate mailings
3. Improved customer satisfaction
Here is a simple illustration of what may occur in a typical merge purge exercise.
First, examine the following customer records:
ID Customer Name Company Phone City
256 Bob Smith IBM Corp 761 111 216 New York
577 Robert Smith Intl Business Machines 761 111 216 New Jersey
742 William James HP Corp 817 216 761 Georgia
891 Bill James Hewlett Packard 817 216 774 Georgia
A hypothetical company ABC that desired to build up a big mailing list of prospects ended up with these records in their new database. They however desire to remove all duplicates so that they can save money on their mailing and maintain a good customer relationship with their prospects and clients.
Some of the key features of excellent merge purge software include:
* The use of fuzzy matching techniques to identify the percentage of similarities between probable duplicate records
* Effective identification of acronyms (e.g. HP for Hewlett Packard)
* Cleaning up and standardization of data before they are examined for duplication
* Application of massive libraries for standardization of names and states (e.g. Bob for Robert, Tom for Thomas etc.)
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