SalesQL uses multiple mechanisms to determine whether an email addresses is valid or not. These mechanisms include:
Talking to the email server to ask whether the email is a valid recipient or not.
Checking whether the email address is publicly available on any trusted website (for example, the website of the company the person works for).
Checking whether the email address is listed on the person’s Linkedin profile.
SalesQL combines all these methods and performs periodic checks to determine whether the emails are still valid. However, none of these methods is 100% accurate, as people often switch positions and change their email addresses without updating their LinkedIn profile accordingly.
An unverifiable email will be marked with a warning sign, as indicated in the graphic below:
Why are some email addresses unverifiable?
Some companies choose to simply accept all emails that are sent to the company domain. If a recipient mailbox does not exist, the company redirects the email to a generic mailbox such as [email protected].
Other companies use these "accept all" policies to protect the email server. So, during the communication with the email server, they first say the address is valid, but when the email is delivered, it bounces back. Here is an example from Google (google.com has an "accept all" policy):
There are multiple programs to validate email addresses and they typically provide the same kind of service as SalesQL. These include:
All these services include the "accept all" state, even though the email server is basically saying that all emails are valid so there is no way to determine whether an individual address is valid or not.
Does SalesQL charge credits for unverifiable emails?
Yes, SalesQL charges credits when the software finds at least one email address. We do not charge credits for contacts when no address can be found.
For more information, check out the article How credits are consumed.
Where are these unverified addresses coming from?
By finding and validating addresses from a variety of sources, we can calculate the probability of an organization maintaining a specific pattern for its addresses, and create a 'best guess' formula on this basis.
For example, when we search for addresses at Accenture, we find that the validated addresses have three main structures and one is extremely dominant.
71% use the pattern [FIRST_NAME].[LAST_NAME]@accenture.com.
4% use the pattern [FIRST_NAME_INITIAL].[LAST_NAME]@accenture.com
25% use various other patterns
Given that over 70% of addresses follow the [FIRST_NAME].[LAST_NAME]@accenture.com pattern, we can take this as our best guess if we can’t find a valid source for a particular individual within Accenture.
How can I reduce the percentage of email bounces?
In order to reduce email bounces, you can filter out the addresses that are unverifiable and export only those that are valid. There is an option for this in the export settings:
When you uncheck that box, you will only export addresses that are considered valid and reduce bounce rates to a minimum.