You are looking for cleaning address data, am very sure you know why you need this service. If not let me give you an example : Suppose you have received a postal mail about some business conference and your address is not properly structured or complete, your gender is wrong, blah blah. It will make a bad impression on the sender.
Isn’t it? There have several possibility, the sender get potential lead database from somewhere and that is not properly structured to be used for marketing. Yes! This is the main reason why you need to clean your data, that might be addresses or an entire mailing list. We are about to discuss some easy tricks you can do on your own to clean your address database.
Now you got the point why address data cleansing is very serious from a business perspective. Just few days back I got couple of couriers from a trade show organiser. Actually what happen here, they have my details on there database for several time (actually duplicate entries). So just think based on this scenario – They spend 4 times higher cost on mailing to the same individual even it made me think them unprofessional. They could, and should check there database for duplicate lead to avoid such errors. So address cleansing is highly possible to prevent such issues. If you are planning to outsource such services then you musk know the things to look for in a data cleansing service provider.
Be Careful About Wrong Use Of Case While Cleaning Address Data
First of all let’s discuss about the common error which is using uppercase, lower case and proper case wrongly. You know you can automatically resolve the use of cases in Excel. Let’s learn how? Excel does have following functions: UPPER, LOWER & PROPER. Now this is very easy to understand which one you can use based on your requirement. ‘UPPER’ is used to convert all the selected letters into uppercase, similarly ‘LOWER’ transform text into lowercase, and ‘PROPER’ will convert each word first alphabet to capital. For example: john smith to John Smith.
Let’s say you have your mailing list database with random cases on cell B, which hold lead name. Now you need to fix the case for each of the name, and place on cell#C. So use this following formula on cell C =PROPER(B1) and then to extend this formula to the rest of the cell, just click and drag the + sigh at the right bottom border of the cell and drag till the end cell you need. Learn some more latest data cleansing steps, which you can implement on your own.
The above discussed way to solve text case for cleaning address data is very simple and fast but some cases there won’t have any automated way to fix. Need an example? Some countries in the word like Belgium peoples have last name in lower case. So only names start with a small alphabet indicate the person form a nobel family (for example if you got some last name like van or de you have keep it like that (No need to capitalised “Van” or “De”). So we have to manually check and resolve something like that.
Essential Of Multiple Entries Cleansing
We had discusses about an example about address data cleansing in the very beginning of the content, which is doubles or multiple entries. Think about a scenario where you are mailing some printed matrial to your list of potential clients. And if you have duplicate entries in your database, you will end up spending unnecessary production and postage cost. So let’s learn how to resolve it. First of all sort your database by postal code, then address and last of all the name. It will give you a easy indication which one is duplicate.
So as far what we have discussed are the most common and automated way for cleaning address data on your own. But you definitely need experts if you have large number of dataset or you have some custom requirement. Fell free to discuss with us for data cleansing services requirement.
Joydeep Singha is the Founder at AskDataEntry – India’s leading online virtual administration services provider for businesses and individuals. He is a seasoned marketing professional who is an expert in digital marketing and entrepreneurship.