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7 Secrets to Scoring High on Data Card Quality

Monday, March 16th, 2009

Good mailing list purchase decisions depend on a good data card database, and NextMark ensures the quality of its data card database through the Data Card Quality Report and other monitoring tools. While it is important not to confuse data card quality with list quality, counts are changing all the time and a neglected data card could be a warning sign of a neglected list. This blog entry is primarily for list managers who are looking for insights on how to keep their data cards up-to-date most efficiently using NextMark's data card publishing tool.

How are data cards scored?

The scoring process is reviewed on a quarterly basis, and subsequently refined to address list specific criteria.  Individual data card scores are calculated using a weighted average of thirteen attributes, Dcqr-samplewith an emphasis on last update (the date when the card was last updated and/or confirmed by the list manager). The basic principle is to create a quality data card from the start, and to manage the update process efficiently. List managers may also contact NextMark to learn how these updates may be processed automatically on their own web site, and integrated on a search engine optimized platform to be indexed by Google and the other search engines.

Seven secrets to scoring high on data card quality:

#1 Review your data card quality report: select either 'Data Card Quality Spreadsheet' or 'Data Card Quality Print View' from the 'CHOOSE A REPORT' menu on the Lists – Management tab. You must be signed in to NextMark under your list management organization to run this report.

#2 Use the 'next update date' field: by populating this field you get the benefit of receiving an e-mail reminder (to update the data card) seven days prior to the date you enter. The next update date must be greater than or equal to the current date in order to receive full credit for last update. If you decide that you do not want to update this field, then be sure to leave it blank and manage your edits based on the update frequency.

#3 Check the update frequency: it is important for list brokers to know how often the names on a mailing list are updated. If the next update date is not populated, then the data card quality score will be based on the update frequency and the last date and time when the data card was updated by the list manager. For example, a data card representing a list that updates monthly should be confirmed every 30 days. However, if the update frequency is semi-annually, then you would only need to update the data card twice per year. Of course, this assumes that there have been no pricing or other changes to the file during the update cycle.

#4 Populate all scored fields for postal list types: make sure that every one of the fields representing the 13 attributes are populated with valid information. There are a few exceptions to this, for example: if a list is available for email addresses only, then you would not be required to select outputs; or if a list is available on exchange only, then you would not be required to enter a base rate.

#5 Audit your list type selections: the scoring process for an insert program is slightly different than it is for a postal mailing list. The same holds true for other types such as blow-in or statement stuffer programs. It is important to make these selections carefully to make sure that your data cards are scored by the most appropriate criteria.

#6 Create a high quality list description:   although there is a minimum character length required for a high quality listDCQ Description description, the scoring process also considers your creative efforts as part of the grade. The html is also credited in a manner similar to the text length of your data card description. Therefore, you are able to focus your efforts on the quality of a list description and not solely its length. You may also create and edit custom tables in the description area to provide counts and/or other information about the list. These tables are also considered as part of the overall data card description score. It is also important to remember to populate the short description field, as you be unable to achieve a perfect grade without that.

#7 Select three relevant categories: you'll need to select at least three categories on the data card that would be relevant to the broker or mailer who is renting the list. Excessive categorization is discouraged because it not only dilutes the uniqueness of a list, but also makes it difficult to determine the target audience for the list. However, you are not required to limit the number of categories, especially in cases where a list or database is enhanced with additional data for targeting specific demographics or psychographics.

The next update of the top list managers by data card quality will be run and published in April for Q1 2009. If you need help with understanding the terms on the data card, then please reference the direct marketing glossary for more information.