This is annoying. In your mail box you receive four solicitations from your favorite charity--one addressed using your full name; one addressed using your initials; one addressed to your spouse; and one addressed to your entire family.
Why are they wasting my donation on multiple solicitations, you wonder? While receiving duplicate mailings annoys recipients, the problems it causes sending organizations are even more significant. Maintaining duplicate records costs money in wasted printing and postage, reduces response rate and can hurt a company's credibility with customers and suppliers.
And the problem is pervasive and costly.It is estimated that 10 percent of the names and addresses in the average mailing list are duplicates. If your mailing list contains 10,000 records with production and postage costs averaging $.83 per piece, your total mailing cost would be $8,300. If 10 percent of your list is made up of duplicate records, you are wasting $830 on duplicate records every time you mail.
Thankfully there is deduplication software available that is as powerful as it is easy to use. Melissa Data's Match Up allows you to merge and/or purge files in different software formats (Xbase, ASCII, Paradox, FoxPro) with different name, address and city/state/ZIP structures. . No changing structures! No importing! You can do it all with Match Up.
Find More Duplicates Faster
Match Up allows for the use of up to sixteen "matchcodes" in a single run for quick, thorough results. You can set one code search on addresses, a second on phone numbers, etc. If any matchcode hits, dups are recorded! It's easy to create unique matchcodes that look for any length of data from any place in any field.
Select from exact matching, Soundex, or Phonetics matching which recognizes phonemes like "ph" and "sh". Match Up also recognizes nicknames (Liz, Beth, Betty, Betsy,
Match Up can compare similar information that may not be in the precisely the same format. For example, the street number and name may be in one field in one database and two fields in another. Match Up can parse the first record to allow matching on either the street number or name. It can compare data that sounds alike even if it is spelled differently, and data that may be the same but contain data input errors. It can compare nicknames to given namesmatching Bill with William--initials to full names, and company acronyms, like IBM, to their full names. Finally, addresses can automatically be standardized and validated using the USPS CASS system to maximize the success of your ZIP + 4 encoder and fix ZIP + 4 blind spots.
Speed and Accuracy
Match Up can process as many as 50 million records an hour. And depending on the quality of the data, its accuracy can approach 100 percent. End users can generate a full range of reports about merge and purge operations and can run quasi "what if" scenarios to determine which data fields are the most efficient matches. Today, companies collect and receive data from multiple sources. With Match Up, organizations now have the ability to better manage their own data, improving their responses and efficiency, while cutting costs.