Hanover firm wins patent for direct-mail tool

NextMark Inc., a Hanover-based designer of direct-mail applications, has been awarded a patent for a new mailing list selection technique.

According to the company, the patent describes a method for generating a highly targeted mailing lists of prospective customers for a list purchaser, which is partially based on successful prospect lists previously used by the list purchasers, other similar list purchasers and/or their current customers.

“We’re sort of a combination of Google and a matchmaker for mailing lists,” said Joseph Pych, inventor and president of NextMark.

The NextMark tool allows direct-mailers to narrow down their marketing campaigns to specific demographics, such as female charity golfers. Further refinement in the sophisticated algorithm can trim lists to just those that have information on possible customers within a state or ZIP code area or another data point, he said. NextMark then provides the contact information for the direct-mailer to reach the list’s owner, who can then purchase the tailor-made list. What’s more, the direct-mailer never sees actually names of prospective customers, ensuring privacy, said Pych.

“Most people don’t realize that direct-mailers hate junk mail as much as or more than people do,” said Pych.

Instead of creating more unwanted junk mail, the system targets mailing lists, saving money and headaches for mailer, the company and the consumer, said Pych.

“With rising postage rates and waning tolerance of junk mail and spam, it’s more important than ever to send your marketing promotions to recipients who welcome your offer,” said Pych.

“For example, suppose you are trying to reach women who buy upscale casual clothes through catalogs. Your search might reveal the Boston Proper Catalog Buyers mailing list. When you view the data card for the list, you will find a list of other mailing lists to consider, such as Coldwater Creek, Bloomingdale’s by Mail, J. Jill, Newport News and Garnet Hill. These suggestions are generated by complex algorithms, but the result is simple to understand and easy to use — it shows you which lists should work best for you.” — CINDY KIBBE