Preparing your mailing list for address validation

Before running your list through our List Processor, take a moment to make sure your data is properly formatted.

In order to process your address data, you must submit the following:

1. The street address in one or two fields (such as Street or Street2)
2. One of the following combinations of city, state, and ZIP:
   • city and ZIP
   • state and ZIP
   • city and state
   • ZIP
   • city, state, and ZIP

The first row of every column should have clear header names (Name, Address, City, State, ZIP, etc.) telling what that column contains. Using clear headers will ensure that the list processor is able to read your list correctly. Check your list to make sure that address data is in the address column, city is in the city column, and so on.

Formatting your list file

If you are exporting or saving from a spreadsheet application like Excel, you likely won't have any issues. However, if you are generating your own .csv file from your own code it's not uncommon to have formatting problems. Make sure that every field—whether it has data in it or not—is accounted for and that you don't have any extraneous or misused double quotes, and you should be good to go.

Be sure that records with any blank fields are not forgotten. In other words, every row must have the same number of fields. For example, consider the following fields taken from a sample .csv file:

Sequence,First Name,Last Name,Firm Name,Address1,Address2,City,State,ZIP Code

It's very likely that not all of your records will have information for each and every field. If a record is missing information in a field you still need to account for it, like this:

1,SHERRIE,SMITH,,11804 Nicholas Dr,,Fredericksburg,VA,22407-7365

Notice that this record does not have a Firm Name or Address2 information. You can see that the empty values were explicitly declared by inserting delimiters next to each other, denoting an empty value.

Another common formatting issue looks like this:

"51,John,Blake,JOHN'S TIRES,132 Jefferson St,,Ft Wayne,IN,46802-0001"

The double-quotes encapsulate the entire string, telling our CSV parser that it is all one field. Make sure the quotes are removed. The tricky part is that looking at this in a spreadsheet will likely NOT reveal that there are quotes present. Try opening it in a text editor.

What to do with messy data

If your list doesn't have addresses broken down into components or if it's just really messy, you may want to check out our extraction tool. Its aggressive algorithm extracts address data from messy text and will help get your list into much better shape. Try it out.

More examples

Make sure that your address list can be matched to relevant fields. Here's an example of a good, clean list file:

Name	Address 1	Address 2	City	State	ZIP
Karen Thomas	1257 Main Street		Oakland	CA	94878
Bob Jones	400 Elm Boulevard	Suite 203	San Fransico	CA	98718
Kevin Tomlinson	2342 Oakdale Drive		Vancouver	WA	98665

Notice that each piece of data is in its own column and there are clear column headers. The data for each record in your list needs to be in the same order.

Here is an example of a list that cannot be mapped and processed effectively:

Name	Address 1	Address 2	City	State	ZIP
Karen Thomas	1257 Main Street	Oakland	CA	94878
Bob Jones	400 Elm Boulevard	Suite 203	San Fransico	CA	98718
Kevin Tomlinson	2342 Oakdale Drive	Vancouver	WA	98665

Notice that the address information is not lined up properly—the city, state, and ZIP Code fields for the records are not in a corresponding city, state, and ZIP Code column. When you process this list, it will fail the standardization process. You can improve the accuracy of the output by making sure that each record's data fields (i.e., city, state, ZIP Code, etc.) are stored in corresponding columns with clear, concise field names.