Once your prediction has a Ready status, it's ready to be reviewed.
In this article
View your prediction
Navigate to Insights > Predictive Goals and select the goal you want to view.
On the Goal Details page, you'll see information about how likely users are to convert on your goal from the last refresh date through the period of time you specified (for example, 1 month).
Predictive goals don't have an end date, but instead "roll" from one refresh to the next. This can help you keep an eye out for potential trends in a single goal.
Some of the information you see will look familiar—you defined the goal criteria, the contact property name, and a description when you created the goal.
The rest of the information is new, so let's take a look at what's most helpful when evaluating predictions.
Predictive strength is a quality gauge that tells you how reliable a prediction is likely to be.
|Value||What it means|
|Strong||A prediction's ready for use, with expected high accuracy. Changing the goal criteria is unlikely to improve the quality of the prediction.|
|Satisfactory||Also a reliable prediction. You could try changing the criteria, but you don't have to in order achieve reliable results.|
|Weak||Not a reliable prediction. Update the predictive goal criteria, check whether associated events and properties (user profile fields) have enough data, or wait for the next refresh cycle to see if the predictive strength improves.|
Predictive strength is refreshed monthly to account for recent changes to relevant project-related data.
Review Explainable AI prediction insights
Exploring details of the events and properties involved in your prediction can be incredibly insightful. To access this information, from Understand your prediction, click Explore. You'll see a panel that looks something like this:
This view shows the number of events and properties that Predictive Goals evaluated when the prediction was generated (in this case, 120). It also shows a breakdown of the statistically significant contributors.
Let's consider an example predictive goal that looks for users who are likely to be highly engaged with your brand. Based on this sample result, you can see that Predictive Goals found that events such as making a purchase, viewing your blog, or starting a trial are good indicators of user engagement.
In addition, you can view the events and properties that are likely to increase
or decrease the likelihood of goal outcome. Using the previous example (increasing
engagement with your brand), you'd want more users to create accounts, make
purchases, and view your blog as reflected by the
custom events, and by the
And, you'd want fewer instances of users canceling orders and unsubscribing, as
reflected by the
Unsubscribed custom events.
For some ideas that might help motivate users toward the actions you want them to take, check out Dynamic Content Personalization to Boost Your Marketing Efforts.
In the Prediction area, you'll find a histogram chart with Percentile on the x-axis and Probability on the y-axis.
To focus on a portion of your users, select a range of Percentile values. Use the radio buttons to quickly select the top and bottom 10% of your users, or select a custom range by entering specific values (or using the sliders).
To understand the likelihood that the selected users will convert on your goal, examine their Probability values. These values vary based on the results of your prediction.
User probability scores are updated weekly to reflect recent activity. When you create user segments based on a specific range of conversion probability, the mix of users within that range varies each week. When segmenting users with this approach, think about how often you want to build new user lists to ensure that you're reaching the desired audience.
As you change the selected range of users, the chart's details refresh. Interactive values include:
The number of users included in your selection and how likely they are to convert on the goal compared to the average of all users.
The multiplier of how much more likely to convert the selected users are compared to the average of all users.
The approximate number of conversions you might expect on all or part of the goal criteria. This value is calculated by multiplying the average probability by the total number of selected users.
For considerations involved in choosing a user segment, see Creating user segments and personalized messaging.
Prediction details are refreshed weekly to reflect recent user data.
Now, learn how to create segmentation from your prediction. See Creating user segments from predictive goals.
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