Predictive Audiences Overview

You can build predictive audiences, which are goal-oriented, dynamic, and adaptive. By contrast, real-time and standard audiences are static and based on historical data.

Create a predictive audience by specifying a user prediction in your regular audience creation workflow in mParticle. The user prediction accesses Cortex machine learning algorithms that analyze data on customer behavior, preferences, and interactions with your brands. You can use predictive audiences to anticipate the needs and desires of your target audiences, and thus deliver more relevant and personalized messaging. Predictive audiences maximize impact by automatically finding the best-fit users for a desired outcome.

Example

In the past, you created a static (real-time) audience that identifies every user who has viewed shoes two or more times in the last week, and sent a coupon to all those users. However, now you can create a goal-oriented, predictive audience that contains all the users most likely to purchase shoes based on an analysis of all the available information, not just one factor (purchased shoes twice before).

How it works

Creating a predictive audience is simple:

1. Create an audience using the same criteria builder that you use for any audience or journey creation.
2. In the criteria builder, select User Prediction and fill in the requested information.
3. When you save the audience, mParticle creates two versions of the the Cortex pipeline. a. A score version, the predicted value for the entity; for example, a user’s conversion probability. b. A percentile version, the percentile rank of the entity’s score relative to all scores; for example, the percent of other users that have received a lower prediction than this user.
4. mParticle populates your audience. After a brief delay, your audience is available for use.
5. Connect the output to the forwarding destinations as you do for real-time audiences.

For step-by-step instructions, see Using Predictive Audiences.

More about Cortex

Cortex is the machine-learning engine available with mParticle’s CDP. To learn more, you can visit the Cortex documentation.