Better Mailing Lists with Data Card Intelligence

With more than 60,000 mailing lists on the market and 40% of your direct marketing success contingent on your choice of lists, you need strategic intelligence to make the best list purchasing decisions. Data cards provide the detailed information necessary to analyze mailing lists. But not all data cards are created equal. Here is how seasoned list brokers use data cards to make strategic purchasing decisions.

What to look for:

Completeness: You should expect your data cards to include all of the information you need to make a preliminary purchasing decision – profile, pricing, counts, selects, restrictions, ordering instructions, etc. Don't settle for less.

Recency: List counts are changing all the time and a neglected data card is a good indication of a neglected list. The "counts through" date indicates that fresh names are available, along with the seasonal context of those names. For example, 3-month hotline subscribers to Field and Stream would be a much better target for your fishing tackle offer if the "counts through" date is in the spring versus early fall (hunting interest).

Mailer usage information: Ask a group of veteran list brokers what they look for to support their list recommendations and you'll hear one thing in common – usage. You'll have more confidence in a new test list recommendation when you know it has worked well for someone else, and that someone else may be a competitor.

Popularity: Mailing lists get popular for a reason – they work. The data card should indicate how often the mailing list was included in recommendations and how often the list was ordered. Just be sure that the list is not new to the market before you judge it too harshly for missing a score (the data card will also indicate when the list was first available).

Related lists: Got a mailing list that works for you? Congratulations. Now your challenge is to find more lists that are just like that one. Your data card can solve this problem by revealing the other lists that are correlated to your successful list. The correlation between two lists is determined by analyzing how frequently they appear together in marketing campaigns. Using highly correlated lists is a great shortcut to find 'more lists like this.'

What to avoid

Tricks of the trade: 'Gaming the system' occurs when list marketers publish data cards with the intention of improving their position on search engine results pages. For example, a few companies have appended list titles with prefixes that take advantage of alpha sorts; a data card does not represent an "A+" list just because it says so in the title. Many brokers are already immune to this and you should be, too. Whenever possible, use a system that returns search results sorted by relevance instead of simple alphabetical listings.

Excessive categorization: A list may work great for some, but not all secondary markets. Mailer usage will indicate the relevance of the categories on the data card. If all of the usage is from cataloguers, then you may want to question why the data card states that the list is highly responsive for fundraisers and publishers.

Outdated information: The last update date is a pretty good indicator of the list manager's attention to the data card. Keep in mind that not all lists are updated monthly; for example, some compiled databases are updated semi-annually. Therefore, the last update date should be considered in the context of the update frequency.

Remember, do not confuse data card quality with list quality. There are some impressive data cards out there that promote poor quality lists and vice versa. However, the mailer usage, list popularity index, and highly correlated lists can give you some insight as to what is working for other direct marketers—and may work for you.

Chris DeMartine is director of business development at NextMark Inc.