Frequency modelling | Broadcast weekly sends metrics and predictions

See also: Frequency modelling overview

This panel shows your recommended send frequency (optimal) against your current average send frequency.

You can look at data for all of your contracts, or choose a specific segment to investigate:

Top level metrics

Optimal sends

Your recommended (predicted) weekly frequency cap for all email sends.

This number is calculated using the following formula:

Optimal sends = predictive # of weekly broadcast emails + current weekly average automation emails

See also: How frequency modelling works

Optimal broadcast sends

Your recommended (predicted) weekly frequency cap for broadcast email sends.

Current avg broadcast sends

Your current average broadcast email sends.

This number is calculated using the following formula:

Current average weekly broadcast sends = total # of broadcast emails sent in the last month / # of weeks that month

Current avg automation sends

Your current average automation email sends.

This number is calculated using the following formula:

Current average weekly automation sends = total # of automation emails sent in the last month / # of weeks that month

Revenue and engagement metrics

The hover-over graphs display panels to help you analyse some key metrics affected by email frequency.

You can identify the the highest, medium, and lowest points for each KPI at a glance:

Hover over the optimal broadcast frequency that our model has predicted for you. 

In our example, the optimal number of broadcast emails per week is five, compared to the current weekly average of 10:

You can see that sending half the number of current emails will improve all engagement metrics as well as revenue by 5%.

We also recommend looking at least at the predicted performance for frequencies either side of the optimal number.

In our example, based on the engagement metrics, sending either four or five weekly emails performs equally well. 

However, reducing the number of emails to 4 would result in a decrease in revenue. 

Conversion rate

Your predicted and current conversion rates for a range of frequencies, based on broadcast sends only. 

The predicted numbers are calculated using the following formula:

Conversion rate = (total # of attributed purchases/total # of delivered broadcast emails in the 

Email revenue

Your current revenue attributed to broadcast and automation campaigns, and the predicted revenue for other email frequencies.

The predicted revenue is calculated using the following formula:

Conversion rate x number of emails

Click through rate (CTR)

Your predicted or current click through rates (CTR) for a range of frequencies, based on broadcast campaigns only:

The predicted numbers are calculated using the following formula:

CTR =(total # of clicked emails/total # of delivered broadcast emails in the last X months) x 100last X months) x 100

Unsubscribe rate

Your predicted and current unsubscribe rates for a range of frequencies, based on broadcast sends only:

The predicted numbers are calculated using the following formula:

Unsubscribe rate = (# of unsubscribe contacts/total # of delivered broadcast emails in the last X months) x 100

Optimise for revenue

If increasing email frequency leads to a significant increase in email revenue, you can consider gradually increasing the frequency to maximise revenue without negatively impacting user engagement.

Optimise for engagement without sacrificing revenue

If the unsubscribe rate is high with increasing email frequency, it may be an indication that users are feeling overwhelmed or disengaged. In this case, you can consider reducing the frequency or segmenting your email list to target users with more relevant content.

By analysing these metrics and adjusting your email campaigns accordingly, you can optimise your strategy and achieve better results over time.