ML Models
The ML Models page shows the machine-learning models that power predictions across your workspace - and lets you check their status, see when they last trained, and trigger training manually.
Navigate to Settings → ML Models from the sidebar.
The models
Six workspace-level models are listed:
| Model | What it does |
|---|---|
| Send-Time Optimization | Predicts the best hour and day to send messages for maximum engagement |
| Personalized Paths | Predicts which experiment path will convert best for each user |
| Churn Prediction | Identifies users at risk of becoming inactive |
| Channel Preference | Predicts the best messaging channel for each user |
| Engagement Scoring | A 0-100 score calculated from a published formula - not a trained model, so it is exact and always current |
| Predicted CLV | Forecasts each customer's expected spend over the next 90 days, plus how likely they are to still be active (predicted customer lifetime value) |
Each model trains per workspace, on your workspace's own event data - not on pooled data from other customers.
Reading a model card
Each card shows:
-
Status - one of:
Status Meaning Not Trained No model exists yet for this workspace Training... A training job is currently running Trained A model exists and is serving predictions Failed The last training attempt failed; the card shows the error message -
Version - the current model version, shown once a model exists.
-
Last Trained - relative time of the last successful training run, or Never.
-
Accuracy - the model's evaluation metric from its last training run, shown when available.
Statuses refresh automatically every 30 seconds while the page is open.
Models activate as data accumulates
Models cannot train on an empty workspace. Every model needs enough event history - and enough variety in it - before it produces anything:
- If there is not enough data, training fails with a message like "Not enough data to train - continue using the platform to generate training data."
- If the data lacks variety (for example, churn prediction needs both active and inactive users), training fails with an explanatory error.
This is expected for new workspaces. Keep tracking events and sending messages; models activate and improve as real traffic accumulates. A Failed or Not Trained status early on is not a defect.
Training cadence
Once your workspace has event activity, models retrain automatically on a fixed schedule (all times UTC):
| Model | Schedule |
|---|---|
| Engagement Scoring | Recalculated daily at 02:00 (no training - it is a formula) |
| Personalized Paths | Daily at 03:30 |
| Predicted CLV | Daily |
| Churn Prediction | Weekly, Monday at 03:00 |
| Send-Time Optimization | Weekly, Tuesday at 03:00 |
| Channel Preference | Weekly, Wednesday at 03:00 |
After each scheduled retrain, user-level scores are refreshed so segments built on them stay current.
Scheduled runs cover workspaces with event activity in the last 90 days; a dormant workspace is skipped until events flow again.
Training manually
You don't have to wait for the schedule. Click Train Now on any card and confirm to start a training run immediately - useful after a burst of new data, or to retry after a failure.
- Training runs in the background and may take a few minutes.
- The button is disabled while that model is already training.
- If training fails, the card shows the error so you can tell whether it's a data problem (wait for more traffic) or something to report.
Where the predictions are used
- Send-Time Optimization - powers send-time optimization when scheduling messages.
- Personalized Paths - drives the journey Experiment node in personalized mode, picking the best path per user. Until this model is trained, the experiment falls back to a plain random split.
- Churn Prediction, Engagement Scoring, Channel Preference - their per-user scores are available as segment filters (engagement score, churn risk, channel preference), so you can target audiences on them directly.
- Predicted CLV - forecasts each customer's likely spend over the next year; exposed as e-commerce segment filters (Predicted CLV amount and Predicted CLV tier). See RFM Analysis.
Event-specific likelihood models (for example "likely to purchase in 30 days") are configured separately - see Predictive Events.
Troubleshooting
- Everything says Not Trained - normal for a new workspace. Models need real event traffic first; either wait for the scheduled runs or click Train Now once you have meaningful data.
- Failed with a data-variety error - the model needs contrasting examples (e.g., both engaged and inactive users). This resolves on its own as your audience's behavior diversifies.
- Failed to load model status - the page could not reach the ML service; the statuses shown may be stale. Refresh the page.
Notes
- Viewing this page requires settings access in your role.
- Models are stored and served per workspace; switching workspaces shows that workspace's own model statuses.
Related
- Predictive Events - Train likelihood models for specific events
- Creating Segments - Use model scores in segment filters