Manage Future Behavior Predictions

When you open a future behavior prediction, the details page helps you understand how the prediction is performing and how to use it in your campaigns. It summarizes what the prediction is measuring, shows key quality and refresh metrics, and breaks down how different ranges of users are expected to behave. You can use this view to review model strength, compare potential target ranges by reach, CVR, and lift, and save the range you want as a new audience for activation.

Prediction Recap

The Prediction Recap gives you a quick snapshot of what this prediction is measuring. It summarizes the target behavior, the prediction window, and the users for whom the prediction will be made. Use this section to confirm that you’re viewing the right prediction, understand its overall scale, and get a quick sense of the opportunity before diving into the detailed results or creating an audience for a range of targeted users.

Prediction Results

The Predictions Results provides everything you need to understand how reliable your prediction is and how to put it to work in your campaigns. You see key metadata about prediction strength, when it was last refreshed, how often it updates, and its current status.

The Target Range selector lists the estimated reach, predicted conversion rate (CVR), and predicted lift for different ranges of users. The Likelihood Percentiles chart shows how users are distributed from least likely to most likely to perform the action. Together, these pieces help you balance reach and efficiency, so you can decide which users to target, and how narrow or broad your audience should be.

Prediction Strength

Prediction strength reflects how accurately the prediction can identify users who are likely to perform the target behavior. It is based on the quality of your data, the strength of behavioral patterns, and how well the prediction’s training process performed.

• Weak: The model strength is low. Behavioral signals are limited or inconsistent, so results should be interpreted with caution and not used for campaign targeting.
• Moderate: The model strength is acceptable. There is a meaningful behavioral pattern, but still some uncertainty. You should use these results alongside other insights when planning a campaign.
• Strong: The model strength is high. Behavioral patterns are clear and consistent, so results are very reliable and should be used to inform campaign strategy.

If a prediction’s strength is lower than expected, verify that you are sending enough historical events to mParticle and that the target behavior occurs frequently enough to provide meaningful training data.

Last Refresh

Shows the last time the prediction was recalculated, according to the prediction’s refresh frequency.

Refresh Frequency

The refresh frequency is how often your prediction is recalculated using the latest available data. It tells you how frequently mParticle updates the prediction scores for your users, so you know how current the results are before using them to create and activate audiences.

Update a prediction’s refresh frequency

To update a prediction’s refresh frequency from the details page:

1. Click the three-dot action icon from the prediction’s detail view (or from the Predictive Attributes page).
2. Click Update frequency.
3. Use the left-hand dropdown to select one of the following intervals: Weekly, Monthly, or On demand.

4. Use the right-hand dropdown to specify when or how often (within the selected interval) the prediction should refresh.

   • For example, if you select Weekly, choose which day of the week to refresh.
   • If you select On demand, the prediction will only refresh when you click Run Now from the action menu.
5. Click Save.

Run now

You can recalculate a prediction manually by clicking Run Now from the prediction’s action menu, either on the detail view or from the Predictive Attributes page.

Running a prediction on demand updates its results immediately using the most recent data available.

Status

The status shows the current state of each prediction’s pipeline.

• Calculating: mParticle is in the process of generating predictions. Predictions that are currently being calculated cannot be used in audiences.
• Active: The prediction has been calculated and is ready to be used in an audience.
• Inactive: The prediction’s pipeline has not been run within the last 30 days.
• Failed: mParticle could not complete the prediction calculation for your users. This often happens when the number of eligible users is too small or there is an issue with the underlying data. To diagnose and resolve the issue, contact Support.

Target Range Definition

The Target Range section helps you explore different slices of your scored users and choose the one you want to turn into an audience.

You can select a preset range or define your own, then compare how each option is predicted to perform before you commit it to a campaign. For the selected range, mParticle shows:

• Estimated Reach: How many users fall within the selected range.
• Predicted CVR: The predicted conversion rate for users in the selected range.
• Predicted Lift: How much higher or lower the predicted conversion rate is for the selected range compared to targeting all scored users.

Click the dropdown menu to select one of the preset ranges, or create a custom range:

• Most likely users (0.90 to 1.00): The top 10% of scored users with the highest predicted CVR. This is often the best option when you want to maximize ROI with a highly efficient audience.
• Users above average (for example, 0.72 to 1.00): Users with a higher-than-average predicted CVR. The exact percentile cutoff is calculated dynamically based on the prediction results, so the range can vary between predictions. This option is commonly used to boost performance while maintaining broader reach.
• Users below average (for example, 0.00 to 0.71): Users with a lower-than-average predicted CVR. The exact percentile cutoff is also dynamic and depends on the prediction results. This can be helpful for churn-prevention, nurturing, or awareness campaigns where you want to re-engage or educate users who are less likely to convert today.
• Custom range: Lets you define your own percentile range to reach a specific audience size, CVR, or lift. This is useful when you want to fine-tune the balance between performance and volume.
• All scored users (0.00 to 1.00), reference only: All users evaluated for the prediction. This represents the full distribution of percentiles, from least likely to most likely to perform the target behavior. It is provided as a reference baseline and cannot be used directly as a targetable range.

Save a target range as a new audience

Once you’ve identified a user range you want to target, you can create an audience directly from the prediction’s details page:

1. From the prediction details page, once you’ve selected your target range, click Save as New Audience.
2. The Audience Builder will open with the criteria selecting your targeted users prepopulated.
3. Save and activate the audience to use it in downstream campaigns in the same way you activate other audiences.

Predictive audiences automatically stay up to date as your predictions refresh each week, helping you maintain targeting accuracy across changing customer behavior.

Learn more about saving target ranges as audiences in Build an Audience with Future Behavior Predictions.

Likelihood Percentiles Chart

The Likelihood Percentiles chart shows how your scored users are distributed from least likely to most likely to perform the target behavior, and how the predicted conversion rate changes across that spectrum.

Users are grouped into 10 equally sized percentile ranges (deciles) along the x-axis, and the predicted conversion rate (CVR) is shown on the y-axis. You can use this chart to balance reach and efficiency.

The chart includes several visual elements:

• Predicted CVR curve: The purple line shows the predicted CVR for each percentile group. The curve typically increases as users become more likely to perform the target behavior.
• Selected range: The purple shaded area highlights the percentile range you have selected in the Target Range controls. This makes it easy to see where your chosen audience sits on the likelihood spectrum.
• Average CVR line: The gray dotted line represents the average predicted conversion rate across all scored users. You can compare the selected range to this baseline to understand whether your audience is expected to perform better or worse than average.

Troubleshooting low-quality or inactive predictions

If your prediction’s quality is rated as Fair or Poor, or if its status becomes Failed, check the following:

• Confirm that enough historical data is available for the target behavior. Predictions need a substantial number of past events to train effectively.
• Check that data ingestion is active and that relevant events are still being sent to mParticle.
• Review applied filters or audience criteria. Overly narrow targeting can reduce the prediction’s sample size and accuracy.

Revisiting your prediction setup and ensuring data completeness can help restore prediction quality and improve long-term performance.