Send Time Optimization (STO) is an AI-driven solution for your email campaigns, which analyzes email responses of every recipient in relation to campaigns launch time, and then sends the next email at the optimal time for them.
As of the end of 2022 STO uses a new, improved algorithm to reach an overall email campaign performance increase.
- Send Time Optimization - FAQ
- Send Time Optimization - Improved algorithm FAQ
- Send Time Optimization - End-User Guide
- Send Time Optimization - Troubleshooting
What is Send Time Optimization?
Send Time Optimization staggers email campaign launches over 24 hours and sends each email at a time after which the recipient is most likely to open it.
The improvements made to specific STO components, boost the engagement rates of launched email campaigns.
Improved STO algorithm: How does it work?
STO uses machine learning to analyze your contacts’ behavior and identify the times when they are most responsive. This is independent of time zone, language or region. The contacts are then divided among 12 child campaigns, sent on the hour at two-hour intervals.
The improved algorithm has a more sophisticated way of using account-level information to optimize sending times for contacts with scarce behavior data. It is more likely to quickly learn the actual preferences of these contacts (and the new patterns in contact behavior) and consequently it tends to have a better performance in the long run.
Note that limited behavior history includes those cases when there is a solid amount of engagement data for a contact but email sends are limited to only a few send times.
For new contacts with no behavior history, their first emails will be sent at a time calculated on the basis of your account’s aggregate historical data on open times. But the algorithm analyzes new responses for every contact on a daily basis and updates its model accordingly. This means that we will have more accurate data over time. Our research shows that, after sending send time optimized campaigns, with the new algorithm, it generated an average 4.3% uplift in terms of click rates. It is nearly twice as large as the relative click-rate uplift (2.3%) that the original algorithm achieved. Note that the overall performance improvement and account-level results vary depending on multiple factors.
In order to measure the benefits of STO, a control group of 10% of the launch list is also created for each campaign. The engagement results of this control group are used as benchmarks against which STO results are compared. This is done completely automatically, so you do not need to devote time and effort to performing A/B testing on your own.
Are there any limitations?
- Send Time Optimization can currently only be enabled for batch email campaigns and for batch email nodes in Automation Center programs that do not start with a transactional entry node.
- Currently, the option Do not send to duplicate email addresses, which you can select in the Email Settings step when creating your campaign, does not apply to STO campaigns.
- The campaign launch cannot be scheduled for the current day, the earliest available option offered in the date picker is the following day.
- Send Time Optimization can handle user lists that contain maximum 10 million contacts: the target segment or the Automation Center list of participants cannot contain more users. If the user list is bigger, sending will be canceled and the account owner will be notified via Emarsys Support.
As Send Time Optimization needs past contact behavior data to determine the optimal sending times, we do not recommend using STO until you have at least 1 month of email campaign history. Turning STO on too early can actually result in a decline in your engagement results.
Why is this good for my customers and for me?
You want to maximize the chances that your emails will engage your customers. Send Time Optimization is designed to help you achieve just that. For your customers, this is one more example of how you are treating them as an individual, strengthening their loyalty to your brand and improving their experience.
The new algorithm is even more likely to quickly learn the new patterns in contact behavior than the old algorithm was.