The optimal send time is a prediction model that takes into account the past behavior of your customers and calculates the time, rounded to an hour, when they are most likely to interact with your campaign. This allows you to automatically send out email campaigns to individual customers at a time when they are most likely to click or open your email.

Because the feature works with opened and clicked statuses of the campaign event that you have collected in the past, for it to work correctly, you will need to have run numerous campaigns before using the optimal send time.

To read about the difference between opened and clicked status values, go to the System events article.

Using optimal send time

There are two options for implementing optimal send time in your campaigns:

1. Build-in options in Scenarios or Email campaigns.
2. Through a Prediction template.

You can read about both options below, or watch a video overview first.

Scenarios and Email campaigns

You can use optimal send time directly in Scenarios using the wait node or in Email Campaigns under the Schedule setting.

The optimal times in the Scenario wait node and Email Campaigns are determined from three inputs:

1. The scheduled time of your Scenario/Email Campaign
2. A maximum wait time that you can specify to make sure the campaign is sent out within the desired timeframe (default is 24hours)
3. The event history of each customer (with the historical timeframe set to 90 days).

In case it is not possible to calculate the optimal time (customers with no activity), the default time is equal to the scheduled time of the campaign. In case the optimal send time for a particular customer would be later than the maximum wait time, the maximum wait time will be used.

The Scenarios and Email Campaigns always run in UTC-0 so you do not need to worry about time offsets due to different timezones.

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Example

Daily newsletter to all customers that should be delivered no sooner than 10 AM and not later than 10 PM. The solution will be a scheduled campaign at 10 AM containing wait node with "optimal time prediction" setting with maximum wait time set as 12 hours.

In both Scenarios and Email Campaigns, you can specify whether you want to optimize the time for open rate or click-through rate, depending on whether your priority is your customers opening your email or clicking on the link inside.

Wait node

In the picture below, you can see the available settings for the optimal send time in the Scenario wait node.

Email campaign schedule

In the picture below, you can see the available settings for the optimal send time in the Email campaign schedule settings.

Prediction template

You can also calculate the optimal send time using the Optimal email time prediction template. As with other predictions - the output will be stored as an attribute for every customer and can be used later in Reports or Scenarios.

You can set up the prediction in the following three steps:

1. How much data should be considered?

Select a timeframe in the past according to which all campaign events relevant for the analysis will be retrieved and used for the calculation. Generally, the longer timeframe you specify the more accurate the result will be.

2. What should be the default send-time?

Choose the default time for sending the email to customers for whom the personalized email time cannot be calculated due to the lack of collected data. This can be your usual send time. A value of 0 corresponds to midnight.

3. What campaign events and attributes should be considered?

Specify which events and attributes should be used in the analysis. You should fill in this information according to the screenshot below so that the optimal send time is calculated for your customers based on their existing 'open' or 'click' engagement with your emails.

After you click Save and Start, the calculation process takes only a few seconds. You can check the result immediately in any of your customers' profiles or you can create a report to see the optimal time values. The results tab won’t be populated for this type of prediction.