Posted by Brian Berg Google+
Careful consideration should be taken when determining who your direct mail audience is. You may have the ideal direct mail offer that pulls at the heart strings of a good prospect, but if mailed to the wrong audience, the mail piece may end is zero sales leads. Likewise, mailing to the perfect audience and with a lack-luster offer could also end in little to no sales lead response. One way to define your direct mail audience is to through mailing list data modeling.
Data modeling is a process of using a mailing list of existing good customers to better understand who to mail to in order to find new customers. Done correctly, data modeling can segment your existing customers as defined by what they’ve purchased, how frequent they purchase, and how much they spend.
The more transactional and response measurement you’ve recorded on your existing customer database, the better you’ll be able to not only build a better mailing list for future mailings but you’ll also be able to mail these new prospects the more likely offer they’ll respond to. This data modeling process is done by using your existing mailing list of “good customers”, those names and addresses of existing customers, and defining which mailing list records purchase Product A vs. Product B, which mailing list records purchase 12 times a year vs. which purchase twice a years, and which have purchased year after year vs. which purchased just once then disappeared.
The names and address of this mailing list are then matched to a master database of all the names and addresses in the entire U.S. Any demographic or psychographic information contained on those matched records on the master file are recorded and evaluated. The data modeling demographer attempts to draw conclusions by looking for patterns within the various transactional segments provided. Also considered is the patterned of differences found but evaluating those residence within the various geographic markets found on the internal customer mailing list.
How do the direct mail marketer customers look differently to those residences within the same market? The more defined these similarities and differences become, the better the direct mail marketer can size up who they will want to mail to in the future. They may discover that the existing customers all have a specific income and child age within the household. Knowing that, one can now identify other “look-alike” households in the same area and spend more direct mail dollars mailing just them.