How to clean up your hospital newsletter mailing list

Even if your hospital newsletter is filled to the brim with the information your fans have been clamoring for, those fans probably don't want to see multiple copies of your publication in their mailboxes.

That's why we recommend taking your hospital newsletter mailing lists through a merge/purge process. Here, we combine all of your disparate lists into one master list—and we look for and eliminate duplicates.

Merge/purge is a delicate process, and the right steps must be performed in the right order for the best results.

4 key merge/purge steps to take

Step 1: Analyze your list and perform cleanup.

A typical mailing list contains predictable fields, including address, city, state and ZIP code. But some lists utilize an unusual format, which means merging that list into a larger list can cause big problems.

Looking over your lists for these issues, and fixing what you can fix before you merge, can help you ensure that the data in your different lists will combine into one cohesive whole you can use.

Step 2: Scrutinize addresses.

List processing software will scan your list to make sure all addresses appear in a standardized format. (That can help catch issues such as "St." for "Street.") The software can also move through ZIP code correction processing to ensure that the right code is connected to the right address. And it can remove addresses that just can't be standardized or corrected.

Step 3: Remove duplicates.

Once each list is as clean and standardized as possible, the final merge/purge step can be performed to combine all the lists and remove duplicates. There are several ways to run this process. If your goal is to remove as many duplicates as possible, you can search for duplicates by address, city, state and ZIP code.

Sometimes a last name plus address, city, state and ZIP code can be used for matching (for example, in a list that contains a lot of different people at the same business address). But sometimes a full name and address must be used for matching.

Unfortunately, the more fields you add to the matching criteria (such as name fields, which can't be standardized or corrected by the software), the more likely it is that duplicates will remain in your final list. That's because the software can't detect duplicates due to all the variations involved. For example, while you could infer that Jackie Smith and Jacquelyn Smith, both at 1234 Any Street, might be the same person, the computer software will not be able to make this logical leap.

Step 4: Track your results.

At the end of this process, you should have a list you can use for your mailing project. But you'll want to take note of the number of duplicates you found. When you complete the next project, you can compare numbers.