Predictive Audience Churn Modeling with Real-Time Interventions

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Description:
Introduce a feature in Adobe Real-Time CDP that utilizes machine learning to predict customer churn based on real-time behavioral and transactional data. The system would allow marketers to define churn-risk thresholds and automatically activate personalized retention journeys (e.g., email offers, push notifications, or loyalty incentives). It would also maintain a version history of churn prediction models for transparency, comparison, and improvement over time.

Why is this feature important?

• Increases ROI of marketing campaigns through targeted retention efforts.
• Enables real-time action, which is crucial for saving at-risk customers.
• Provides transparency and iterative improvement through model versioning.
• Supports personalization at scale with minimal manual effort.

• Reduces revenue loss by proactively identifying customers likely to churn.

Current Behavior:
Adobe Real-Time CDP allows segmentation and audience activation, but lacks built-in churn prediction and automated intervention capabilities.

• Churn analysis typically happens outside Adobe in third-party tools.
• Manual workflows are needed to respond to churn risk.
• There is no native model history/version control in audience predictions.

Use Case:

An eCommerce brand wants to reduce churn among its subscription customers. Using this feature:

1. The brand configures a churn prediction model in Real-Time CDP, trained on past behavior like reduced usage, late payments, or service downgrades.
2. When a customer’s data shows signs of churn risk, the system immediately triggers a retention campaign offering a limited-time discount via email and mobile app.
3. Marketers monitor model performance and view previous model versions to refine targeting strategies over time.