|
|
|
2. Clean
|
|
|
|
|
|
|
| |
|
WinPure Clean and Match
2007 provides five unique list/data cleaning
modules that will help to ensure your lists are fully populated,
accurate, and have correct email addresses. WinPure recommends
you use this Clean section before a data deduplication
(Match), ensuring better and more accurate results.
|
| |
|
|
| |
|
1. Statistics Module:
|
| |
|
|
| |
|
2. Case Converter Module:
|
| |
|
|
|
 |
This module
ensures that your data is professionally presented
and is ideal for standardizing mailing lists. It
allows you to make any field upper case, lower case
or proper case. Proper case (or Title Case) will
take data such as “john mcneal” and convert it to
“John McNeal” or convert addresses from “123 maple
lane” to “123 Maple Lane”. |
|
Features
a powerful Prefixes and Exceptions
functions to fully customize how names and addresses
are corrected. |
|
|
| |
|
3. Text Cleaner Module:
|
| |
|
|
|
|
This powerful
module will quickly and effectively remove unwanted
characters from text columns in your lists. |
|
At a click of a button,
the text cleaner can automatically remove non-printable
characters, leading or trailing spaces, and even
repetition of non-alpha characters. |
|
|
Features
'Alpha only' and 'Numeric only' column cleaning
options, ideal for telephone numbers and name fields. |
|
|
| |
|
4. Column Cleaner Module:
|
| |
|
|
|
|
This module
groups together and counts common values in a column
to help identify duplicates or discrepancies. The
data can then be corrected for any incorrect group
and set to any value that you choose, without having
to visit every wrongly spelt occurance. |
|
Easily identify wrong
spellings such as 'Stret' ,'Steet' and 'Strreet'
and correct all occurances to the correct value
'Street' with one single click. |
|
|
| |
|
5. Email Cleaner Module:
|
| |
|
|
|
|
Ensure
your e-mail campaigns are effective by quickly identifying
inaccurate or duplicate e-mail addresses. |
|
The Email
Analysis Engine will help provide corrections for
invalid e-mail addresses (e.g. “alan#bakers.com”
becomes “alan@bakers.com”) . You are also able to
filter email addresses on the basis of domain name.
For example you can filter hotmail email addresses
or all admin@emails, etc.
|
|
|
|
|
|
  |
