What can I do to reduce spam traps now?
- Establish an on-going process for actively removing subscribers that are both aged and inactive
- Following a spam trap hit, isolate any new data that was sent within the last 24 hours. This will define which set of emails or segment contained the trap hit. (Example: If the total file is 1M addresses, this segment or section of the file with the trap hit might be 100,000 email addresses
- Divide the email segments into a smaller sub-set of addresses that can you mail, to determine which sub-set contains the spam trap hit. (Example: create 10 sub-sets out of the file with 10,000 email addresses each).
- Mail each of the subsets in turn over a period of time to see if you continue to hit a trap. (Example: Mail a single sub-set every day over a 10 day period).
- If the segment doesn’t contain a trap hit you can add it back to the main segment or file knowing that it is clean at this point in time.
- If the segment does contain a trap hit you can continue to break the emails down to smaller subsets and continue steps 3-5. You can also send a re-opt-in email notifying users that they will need to opt-in to your email again or else they will no longer continue receiving email from your brand. Then remove the addresses or segment of addresses that do not respond from your file.
- Ensure that a bounce, opt-out, or suppression file was not mailed.
- Review old inactive data to determine if the risk mailing to these addresses is worth hitting a spam trap. Remember, spam traps will not open, click URLs, or make online purchases.
How do I prevent Future spam trap hits?
- Consider implementing a double opt-in subscription process. Each time you get a new subscriber, send them an email requiring that they click on the confirmation URL to confirm their subscription. Remember, spam traps will not click through so this is a very effective way to prevent traps from staying on your lists.
- Make sure your address collection process automatically excludes/removes:
- abuse@ and postmaster@ addresses,
- malformed addresses with misspelled domains (i.e. firstname.lastname@example.org)
- role accounts (i.e. email@example.com, firstname.lastname@example.org)
- nonsensical email addresses (ex. email@example.com)
- Test a sample of the file from a separate IP space and monitor spam trap hits before adding the file to your database.
- Review the partner data file to ensure malformed, role account, nonsensical addresses are removed.
- Mail partner data from separate IP space to monitor on-going data quality.
- Regularly audit the partner's sign-up process to ensure that it meets industry best practices for address collection.