Over time your database may increase, and some particularly heavy mailings (with a large number of emails) may see the stats drop.
Sometimes it is your activity that triggers blacklistings directly in some email clients (Gmail, Microsoft...).
When all your settings are set and you already fine-tune your shipments, a method can effectively and durably improve your deliverability: remove the least qualified contacts from your normal mailings (high-speed) and email them afterwards at low speed.
By sending to your optins contacts, you benefit from open rates and high clicks - which improves your reputation with servers - before sending to your other contacts. Gradually when these contacts open an email, click or subscribe, they will pass in the fast sendings.
Example with 2 similar newsletters with a long interval
We tested on the same tool, same SMTP relay.
1st step - Sending the 1st test email: to qualified contacts
We sent an email classically to a specific target 3 months ago (the reference email).
Then our 1st test email, to contacts who clicked at least once in last 12 months.
The reference email | The 1st test email |
Prime ID 192 Latest peer-review articles in Aesthetic and Anti-ageing Medicine |
Prime ID 236 |
:
Three months after the reference email sent to 114 623 people, we send the first test email only to the clickers of the last 12 months, only 20 668 emails.
First advantage: the drop of the new target (18% of the original target) already allows us to reduce the speed of sending.
Two days later we are already seeing comparable number of openings and clicks:despite very different target sizes (114 623 emails / 20 668 emails).
Stats 2 days after the:sending the 1st test email, still running | ||
Stats observation moment: 8 march 2018, 17h00 | ||
Params & results | Email with no filter - Reference email - ID 192 | Email with filter - 1st test email - ID 236 |
First send date | 12 dec 2017, 5h05 | 6 march 2018, 16h03 |
Last send | No info | Still running |
Sent | 114 323 emails | 17 125 emails |
Speed average | No info but params around 9000 emails/h | 357 emails/h |
Open | 5.995% / 6838 unique opens | 26.68% / 4569 unique opens |
Clicked | 0.356% / 407 unique hits | 1.617% / 277 unique hits |
Efficiency | No info | No info |
Unsub | 0.07% / 80 unsubs | 0.128% / 22 unsubs |
Bounces | 0.23% / 263 bounces | 0.145% / 25 bounces |
Cost | 67.45 $ (0.59 $/1000 emails) | 10.15 $ (0.59 $/1000 emails) |
:
When the 1st test email is finished, the trend is confirmed. Numbers of unique hits are very close.
Stats a few hours after the end sending of the 1st test email | ||
Stats observation moment: 9 march 2018, 12h03 | ||
Params & results | Email with no filter - Reference email - ID 192 | Email with filter - 1st test email - ID 236 |
First send date | 12 dec 2017, 5h05 | 6 march 2018, 16h03 |
Last send | No info | 9 march, 05h49 |
Sent | 114 323 emails | 20 668 emails |
Speed average | No info but params around 9000 emails/h | 333 emails/h |
Open | 5.995% / 6839 unique opens | 27.75% / 5729 unique opens |
Clicked | 0.356% / 407 unique hits | 1.7% / 351 unique hits |
Efficiency | No info | No info |
Unsub | 0.07% / 80 unsubs | 0.116% / 24 unsubs |
Bounces | 0.23% / 263 bounces | 0.120% / 25 bounces |
Cost | 67.45 $ (0.59 $/1000 emails) | 12.17 $ (0.59 $/1000 emails) |
2nd step - Sending the 1st test email to low-qualified contacts
On 9 march 12h38, we will send our test email to other contacts: We just use the same email and change our filters, to send only contacts that have not clicked during 12 last months.
Before sending, we greatly reduce the speed of sending: 100 emails/h.
Now 16 march 12h27 we have this stats:
Stats 7 days after the sending the 1st test email to low-qualified contacts | ||
Stats observation moment: 16 march 2018, 12h27 | ||
Params & results | Email with no filter - Reference email - ID 192 | Email with filter - 1st test email - ID 236 |
First send date | 12 dec 2017, 5h05 | 6 march 2018, 16h03 |
Last send | No info | 15 march 2018, 15h04 |
Sent | 114 323 emails | 30 428 emails |
Speed average | No info but params around 9000 emails/h | 141 emails/h |
Open | 6% / 6844 unique opens | 22.34% / 6786 unique opens |
Clicked | 0.357% / 408 unique hits | 1.563% / 475 unique hits |
Efficiency | 5.961% | 6.999% |
Unsub | 0.07% / 80 unsubs | 0.125% / 38 unsubs |
Bounces | 0.23% / 263 bounces | 0.187% / 57 bounces |
Cost | 67.45 $ (0.59 $/1000 emails) | 17.9 $ (0.59 $/1000 emails) |
Uniques opens are:very close and unique hits are superior! This for a cost 3.7 times cheaper.
Furthermore we see even several months after sending, stats still move (as email ID 192). So we can be sure that stats from email ID 236 will still increase and unique opens will be superior also.
Another advantage is that by doing so you keep good opening stats throughout your shipment, and therefore a good reputation.
In this example newsletter, after sending to qualified contacts (20 668 emails), and sending to non-qualified contacts (9760 for now), we recovered 248 new openings. So many contacts who are now considered qualified, and who will increase the first mailing of the next newsletter.
Stats by cost
Stats by cost | ||
Stats observation moment: 16 march 2018, 12h27 | ||
Params & results | Email with no filter - Reference email - ID 192 | Email with filter - 1st test email - ID 236 |
Cost by email | 67.45 $ (0.59 $/1000 emails) | 17.9 $ (0.59 $/1000 emails) |
:Cost by open | 9.8 $/1000 opens | 2.6 $/1000 opens |
:Cost by hit | 16 $/100 hits | :3.7 $/100 hits |