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Counts Count
by Bob Moore

Bob’s Data Processing Tech Tips
Volume 1 - Counts Count

We’ve heard the horror stories … the donors were mailed to rather than suppressed in a recent non-profit acquisition mailing … a 120-page catalog was mailed twice to old buyers and not at all to recent buyers … three test segments all got assigned the same keycode for a mailing, now response rates can’t be tracked. All costly mistakes. And, all could have been avoided if someone had simply checked the counts.

In my experience, more than fifty-percent of data processing errors can be caught by spending just ten minutes reviewing the detailed counts that come with your reporting. Here are questions to ask as you look at the reports from your next mailing project:

Does it all add up? There are fewer records when the processing is done. So, where’d they all go? Are there any unexplained missing records? If so, did a segment get dropped? Your service bureau or lettershop should be able to account for every single record. Did they?
How many records were dropped as undeliverable or for some other quality issue? There should be some. Almost no list is perfect. If one list stands out with a lot of drops, it may be a bad list or maybe the data was misinterpreted. If no records were purged as undeliverable your service bureau may not have been very thorough and part of your mailing may end up in the dead letter bin.
Check mail counts by keycode BEFORE you mail. Make sure the counts are from a query of the actual final mail file and not extrapolated from other reports. Check the counts against your list of lists. Are all keycodes represented? Are they in the proportions you expect? If not, maybe keycodes were incorrectly assigned.
How many duplicates purged from each list against the other lists? Was the merge/purge done in the priority assigned per your instructions? Do the list-by-list duplicate counts make sense? There should be a higher duplicate rate between similar lists than dissimilar lists.
How many duplicates were found within each list? There should be few. If not, there may be a problem with a list – a bad data pull or a bad purge. If you have a suppress list, how many records were suppressed from the other files? You know best how many to expect. Do the counts meet those expectations?
What is your gut feeling? You know your mailing. You think you know the lists you’re mailing to. Trust your gut feelings. Compare your instincts to the results. Do the counts make sense? This last question will uncover more potentially costly errors than 50 pages of sample dumps.

Nobody wants mistakes on your next mailing project, least of all you. But they happen. Don’t let the reports from your processing sit on your desk to gather dust. Review them as soon as you get them and you’ll protect your next mailing by becoming part of the quality assurance process.

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