The 9 Gotchas of Bad Mailing Data

By: Deb Williams

Two weeks ago, we looked at the importance of good data and entering it correctly in our systems from the get-go. Good data plays a huge part in good marketing, especially when doing a physical mailing. A lot of money can be saved by avoiding a few simple, common data mistakes.

Here is our official list of the Top 9 Gotchas of Bad Mailing Data:

9. Multiple recipients per household: You wanted to send one postcard per household but you ended up sending three to the same address. How? Both spouses and a failed-to-launch son from the same family are all in your database. Microsoft Excel offers a remove duplicates feature which can help you eliminate duplicate entries from your data. You can auto-search based on specific columns like address and then decide which duplicate entry to keep. Checking for duplicates is an easy way to avoid sending out more pieces than necessary.

8. Multiple entries for the same person: Sometimes a customer will accidentally be added to your data system more than once. Exact duplicates are no problem. As we learned from gotcha 9 above, those are easy to remove. Trickier are variations of the same name: Michael, Mike, Mikey, M. or even an incorrect spelling. The best solution for avoiding these types of duplicates is committing to accuracy and consistency when capturing the data in the first place.

7. The wrong address: Wrong addresses can also happen due to data entry problems. Or it’s possible that your data is out of date and the person no longer resides at the address you have on file. As part of data preparation for our customers’ mailings, we run a postal report to look for any recipients who have recently moved. Any updated address info gets sent to our clients so they can update their records. Unfortunately, this is search will only show results for address changes within the last six months.

6. Address information in the wrong field: This is another issue that happens during data entry. It is easy to accidentally enter the state designator in the city box and suddenly all subsequent address data is shifted over into the wrong field. This can be avoided by using electronic forms with validation logic. If that is not available, a second glance to confirm proper placement is worth the time it takes to ensure good data.

5. Complete addresses in a single data cell: Creating a spreadsheet with all of the address information in one cell can make for easy typing. But this makes using handy features, like sort, in Microsoft Excel almost impossible. There are formulas that can be used to break-up the data, though sometimes data can be relocated into the wrong fields when using these formulas if the breakpoints are not as obvious. It is better to give each piece of data its own cell.

4. Dropped leading 0 in the zip code: Some states have zip codes that begin with a zero. If the cell a zip code is entered into is not formatted correctly in Excel, that zero is dropped. To fix this, set the cell format to Special Characters – Zip Code, and Microsoft Excel will be smart enough to hold on to the zero.

3. No address at all: If only part of a customer record is created and there is no address, there is no way to connect with the customer through a mailing. The only way to correct this is research by someone on your staff. Upfront processes to ensure good data from the beginning is the best solution.

2. Special characters: Special characters can really mess up processing mailing data. Suddenly, your 2,000 records turn into 20,000 because Microsoft Excel is reading delimiters that are not meant to be there – dividing up your data in less than desirable ways. Even worse than a special character is a hidden special character like a carriage return. It is hard to find something you cannot see!

And the number 1 gotcha: not using the most current, accurate, cleaned-up version of your data. Whenever The H&H Group processes data for a mailing, we always run the data through checks looking for these very gotchas. Finding and correcting these errors ensures that our customers receive the best possible outcome at the best possible price. We always share our corrections with our customers so they can improve their data. Unfortunately, sometimes we see the the old unfixed data come back to us a couple of weeks later for a new mailing, with the exact same mistakes. Updating your master data set with corrected data from your mailing provider will help you avoid this gotcha.

That is a quick run through of the most common errors we see when prepping mail data. If you ever have any questions on how to make your mailing data better, we would love to assist.

Happy Mailing!

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