Description:
Introduce a predictive LTV (Lifetime Value) model natively inside Real-Time CDP that scores customers dynamically based on their engagement, transactions, browsing behavior, and other attributes - allowing marketers to automatically prioritize higher-value users for premium campaigns, offers, and loyalty experiences.
Why is this feature important?Maximizes ROI by investing marketing resources in users who are likely to bring more revenue over time.
Enables smarter segmentation beyond simple past-purchase or recency-frequency models.
Enhances personalization by aligning offer types, discount levels, and campaign intensity based on user potential value.
Improves strategic planning by giving marketing, sales, and service teams a predictive view of customer quality, not just quantity.
Current Behavior:
Marketers manually segment users based on historical transactions like total purchase value or number of orders.
No predictive scoring available natively in Real-Time CDP; advanced LTV modeling typically requires external data science tools or custom machine learning pipelines.
LTV-based prioritization isn't updated in real-time based on new events (e.g., sudden buying spree, major complaints, loyalty program milestones).
Use Case:
A user shows sudden high engagement with luxury products. The system recalculates their predicted LTV upward and automatically enrolls them in a premium loyalty tier campaign.
A segment of users with declining engagement but historically high spend are identified early, allowing for targeted re-engagement offers before they churn.
Campaign orchestration tools can use predictive LTV scores to decide who gets personal shopper assistance, exclusive previews, or early access to limited-stock items.
