A database is a collection of records. It can be general or subject specific. Made up of names, addresses and other pertinent information, a database can comprise customers, members, firms, employees, organizations, adults, children, students … the list goes on.
To be effective, a database must be large, clean and segmentable.
Size is key. A database must have enough records to accommodate the needs of mass marketers, who mail millions of pieces nationwide a year, and the very targeted requirements of small local businesses.
Selectivity is essential, whether a mailer is large or small. Marketers must be able to select data that will complement their customer profile. Segmentation can mean making demographic selections as wide as geographic area, age, gender, family income or graduation year, or as narrow as particular interests or activities.
List hygiene also is critical for the performance of a database. The cleaner and more accurate the list, the better it will perform. Databases should not contain undeliverable addresses, inappropriate names, incorrect data, duplicate records or people who do not want to receive mail. And it is up to the compiler to remove such records from the database on a regular basis.
Customer lists, survey information and/or appropriate public records may be combined to compile a database. The compiler must confirm with each data provider that the people in their file received full disclosure that their names would be shared with other companies and were given the choice to opt out of this use of their information.
All data should be validated before being added to the database. When you obtain a new source of names, call an “nth” selection of those records to confirm that their information is valid. If you find that too many of the subjects are represented incorrectly on the list in question, the source should be eliminated.
Once validated, run the data through postal standardization, CASS certification, ZIP+4 and the National Change of Address file to make it as deliverable as possible. Run all records against the Direct Marketing Association’s pander file and any proprietary “do-not-promote” lists and delete the matches. Next, merge/purge the new list against your database and eliminate the duplicates.
You also can run all new records against an “inappropriate name” file to delete obscene, politically incorrect or goofy names. Because of the transient nature of our society, all mailing lists will have some undeliverables (nixies). Clients who mail First Class will get these nixies back from the U.S. Postal Service. You can arrange to key the nixies and then use them as suppression against your database.
A good, clean, segmentable database is an excellent source of names for direct marketers. Using bad data will result in reaching out to people who are no longer – or never were – there, or who just don’t want your product or service, which certainly isn’t cost effective.
