Using throttling with HubSpot email deliveries

Email throttling is controlling the amount of email messages delivered to inbox service providers (ISPs) or corporate email servers at one time. Sometimes ISPs or corporate filtering systems block, immediately quarantine or deliver messages directly to spam when a high volume is sent by one sender at one time because they might be concerned its spam. While this may seem somewhat restrictive, it does provide senders with some unique opportunities.

Understanding throttling types

For advanced use cases, Seventh Sense has developed a number of throttling types for email deliveries.  This knowledge base article provides insight into how and when to use them.

Using a HubSpot workflow "Advanced delivery optimization" action step, you can select a "Throttling type" based on your use case.

Below is a listing of the throttling types that are options when using the advanced delivery optimization action step.

  • Personalize delivery (default)
  • Throttle randomly
  • Throttle evenly
  • Throttle on audience engagement patterns

Note: All Seventh Sense delivery types use micro-throttling which is a process of using the entire hour to deliver emails which is different than the way HubSpot delivers which is typically on the hour or every 15 minutes.  The reason for this is that it acts like a human in the way we send emails.

 

Personalize delivery (default)

This is the default setting and what is used as the most common method when using Seventh Sense with a batch or automated email campaign.  When this throttling type is used, the system will personalize the email delivery time for any person that has a history of engaging in your emails and throttle based on your overall audience engagement patterns for any person that does not have a history of engaging with your emails.  For example, if 16% of your audience has shown higher engagement in the 10am hour on Tuesday, then 16% of the people that do not have a history of engaging with your emails will have their emails delivered within the 10am hour on Tuesday.

The below example illustrates a sample 24 hour delivery window and how many people would have their email delivered at a personalized time and how many people would have their email delivered within each hour based on the engagement patterns of your overall audience. 

Personalized.png

Throttle randomly

Using this throttle type, the system will pick a completely random time for each person to have their email delivered within the selected delivery window. 

This can be useful with ABM strategies where you're sending to a large number of people within a single corporate domain.  It can also be useful to remove potentially older bias data caused by months or years of blasting email at the same scheduled time.

The below example illustrates a sample 24 hour delivery window and how many people would have their email delivered within each hour.

Random.png

Throttle evenly

Using this throttle type, the system will still pick a random time for each person to have their email delivered within the selected delivery window similar to the above, however, it will distribute the randomization more evenly. 

This can be useful when testing to find a top engagement time for a particular list or audience, similar to a study we wrote about in this article.  It can also be useful with ABM strategies where you're sending to a large number of people within a single corporate domain or to remove potentially older bias data caused by months or years of blasting email at the same scheduled time. 

The below example illustrates a sample 24 hour delivery window and how many people would have their email delivered within each hour.

Even.png

Throttle on audience engagement patterns

In this delivery scheme, the system will pick a random time for each person in the list to be delivered their email within the selected delivery window similar to the above, however, it will weight the number of people to have their email delivered per hour based on the overall engagement patterns of your entire audience. 

This is the systems default mechanism of using randomization throughout as we know it provides an even balance of randomization and taking advantage of data you have on your overall audience.

The below example illustrates a sample 24 hour delivery window and how many people would have their email delivered within each hour.

Audience.png

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