Optimal send time prediction

Optimal send time (OST) prediction calculates the best hour to reach each customer. The time is based on their past campaign engagement, so every message lands when they're most likely to interact.

Packaging

FeaturePackage required
Wait node and campaign scheduleAll packages
Optimal send time prediction templateLoomi AI Audience Optimization
Messaging channel selectorLoomi AI Journey Orchestration

If you're unsure which packages you have, contact your CSM.

How OST prediction works

OST optimizes for clicks by default. The previous "optimize for open vs. click" selector has been removed because open event data can be unreliable. When click data is insufficient, OST uses opens as a fallback.

OST relies on historical campaign event data. There's no minimum number of campaigns required, but accuracy improves with every campaign. A single click at 8 AM already sets 8 AM as that customer's predicted send time, and the model continues to refine this as more data comes in.

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Note

For configuration in scenarios using the Wait node or in campaigns using the Schedule setting, see Optimal send time in scenarios and campaigns.

OST prediction workflow

  1. Go to Analyses > Predictions, click + New prediction, and select Optimal send time.

  2. Select the channel to optimize send time for. Select from the following options:

    • Email
    • SMS / MMS / RCS
    • All channels: Used to set universal send time.
    • Other: Used for custom channels, for which you specify the event and filter. For example, any campaign_open where channel = WhatsApp.
  3. Set the default send time. This serves as a fallback for customers without a calculated optimal send time. 0 represents midnight. OST is always calculated in UTC-0. The timezone defined in user settings doesn't affect it.

    Configure the optimal send time prediction.
  4. If the prediction is part of an initiative, mark it as a global object to display it in the customer profile. Click the Initiative icon next to the prediction name and check the Global object box.

    Add the prediction to an initiative.
  5. Click Save, then Start to launch the calculation.

The calculation typically takes only a few seconds. The Results tab won't be populated for this prediction type. To review results, check the optimal send time value on any customer's profile, or create a report to view optimal send times across your audience.

Use the OST property

The optimal send time attribute can be used across:

  • Segmentations: Filter customers by OST to build time-of-day audiences. For example, evening-only email campaigns.
  • Scenarios: Use a wait node to hold each customer until their OST before triggering the next message.
  • Reports: Compare click rates across send time buckets to validate whether timing improves engagement.

Related articles

Optimal send time in scenarios and campaigns: Learn how to use optimal send time predictions in scenarios and campaigns.

Prediction use cases: Explore real-world examples of how predictions can personalize customer experiences and drive engagement.


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