Enhance What You've Got, to Get What You Want
Kleinfelter finds that you can prevent poor enhancement results by: 1) watching the data hand-offs to ensure that the list compiler can read your file correctly; 2) eyeballing a data dump of non-matches to see why they didn't hit. If you haven't cleaned your file well enough before having it enhanced, it's not the list compiler's fault.
If you know your data is clean, then it's time to go back to the list compiler and ask tougher questions about its matching procedure and the completeness of its data.
Regardless of how many matches came back, Kleinfelter says it's always wise to eyeball the results to make sure data appended to the correct fields in the record structure. Before rolling out any mail or phone campaign, do a test run. And if you're using enhanced data for personalization on direct mail or Web sites, do an internal test with any new data to prevent mistakes that damage customer trust.
According to Hunt, list compilers might use one of two different methods to price data appending services:
1. A flat per thousand charge for processing the marketer's entire file, regardless of how many matches result.
2. A match charge only for records appended, which may include a passing charge to cover the vendor's processing costs—especially if it's the third or fourth data pass for the file.
Basic appending costs from $100/M for a low volume of data to $2/M or $3/M for a high volume, Kooker estimates.
Also, the more specific the data, and the harder it is to get, the more expensive it is. An example of this comes from Van Roekel: "On the high side, you can expect to pay $20/M for records appended to get mortgage information, but only $3/M appended to find homeowners." He adds that many vendors offer discounts on bundled services.