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Step 1. Create an input
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Future behavior predictions use machine learning to estimate the likelihood that each of your customers will take a specific action that matters to your business. They help you identify which users are most likely to convert, churn, or engage, so you can deliver more personalized and effective campaigns and experiences.
Unlike next best actions, which determine what to offer a user, future behavior predictions reveal who is most likely to act. Each prediction appears as a user attribute in mParticle and continuously updates as new data flows in, keeping your targeting and activation aligned with customer behavior.
The following examples highlight practical ways to use future behavior predictions to drive results across your customer lifecycle. Whether you’re focused on conversions, churn prevention, upgrades, or engagement, these workflows show how predictions can make your targeting smarter and more efficient.
Use a future behavior prediction to identify users most likely to complete a purchase so you can focus campaigns and offers where they’ll have the greatest impact.
Focus on users who have shown strong intent signals but haven’t yet completed a purchase—for example, those who viewed a product or added an item to their cart but didn’t check out.
Specify the conversion you want to predict, such as “complete purchase.”
Use a future behavior prediction to estimate which users are most likely to convert within a set timeframe. Then, create a campaign that prioritizes high likelihood users with personalized messaging or limited-time offers.
Predict which customers are at risk of becoming inactive or unsubscribing, allowing you to re-engage them before they churn.
Identify subscribers or active customers who have recently reduced activity—for example, those who haven’t logged in or made a purchase in several weeks.
Set the prediction goal as “unsubscribe” or “no purchase activity.”
Use a future behavior prediction to calculate each user’s churn probability. Deliver re-engagement offers or proactive support to users with the highest churn likelihood, while avoiding unnecessary incentives for low-risk users.
Forecast which users are most likely to upgrade or renew, helping you deliver personalized offers that drive subscription growth.
Segment users who currently hold a basic or mid-tier subscription plan.
Choose an upgrade or renewal event, such as “renew subscription” or “upgrade plan.”
Generate a future behavior prediction that identifies which users are most likely to upgrade in the next 30 days. Use that signal to tailor communications—offering early-renewal discounts or highlighting premium features only to users who show strong intent.
Determine which users are most likely to engage with specific content so you can personalize recommendations and boost viewership.
Select users who interact with your media or content platform—for example, viewers who browse specific genres or series.
Set the desired engagement, such as “watch episode” or “start new series.”
Create a future behavior prediction that forecasts which users are most likely to watch particular content. Use these insights to recommend shows, surface personalized home-page carousels, or trigger notifications about upcoming releases to high likelihood users.
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