Send Time Optimization
The Send Time Optimization model leverages the past email behavior to predict
the best send time to optimize email open behaviour. The model predicts the
best send time for weekdays and weekends separately as well as collectively.
Use cases
Email engagement optimization
Identify when customers are likely to open an email and tailor the send time
at the individual level accordingly. This helps avoid email fatigue for
customers who receive multiple emails. In addition, marketers can prioritize
the quality of emails over quantity.
Target audience
The Send Time Optimization model predicts the behavior of all customers,
including buyers and non-buyers.
How does the model work?
For each contact, the model:
- Calculates the number of successful email sends for each hour slot in a day,
across the past year. A successful send is defined as an email that was
opened within 24 hours of being sent.
- Leverages this information to learn about the user behaviour to enhance the
model’s predictions.
- Sends random send slots to a small percentage of users to enhance the data
diversity.
- Uses population-level best send time for users without sufficient data.
- Predicts the final outcome, which is a combination of all the above points.
Using the Send Time Optimization model
CDP displays the output from the Send Time Optimization model in:
- 360 > 360 Profiles
- Actions > Campaigns and Actions > Campaigns+
- Analytics > Metrics
To learn more about how the Send Time Optimization model can enhance
your marketing workflow, contact us.
