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.
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).
When you create a predictive audience, you can choose between two types of results:
For example, if you wanted to know how likely it is that a user will purchase shoes in the next 7 days, you could see that likelihood displayed as a score or percentile:
Creating a predictive audience is simple:
For step-by-step instructions, see Using Predictive Audiences.
Predictions are rerun weekly to regenerate fresh predictions.
Predictions created in mParticle have the following limitations:
Cortex is the machine-learning engine available with mParticle’s CDP. To learn more, you can visit the Cortex documentation.
Was this page helpful?