TrueLoyal, the AI-driven loyalty platform for multi-channel consumer brands, has unveiled its new Churn Prediction engine, designed to help companies identify and re-engage customers before they leave. The tool leverages predictive modeling to forecast churn risk and enables brands to deliver personalized marketing campaigns that strengthen retention and boost lifetime value.
Early beta testing has shown promising results, with brands using Churn Prediction seeing a 10% increase in customer winbacks—a meaningful lift in revenue per member and overall loyalty program impact.
Unlike traditional platforms that only track past behavior, TrueLoyal’s engine analyzes a wide range of signals—including online and in-store transactions, survey responses, social sentiment, and browsing behavior—to flag at-risk customers in advance. This proactive approach enables marketers to replace broad, costly campaigns with targeted, one-to-one outreach.
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“In today’s economy, customer retention is a core driver of profitable growth. We’re moving brands from guesswork to certainty, making one-to-one personalization at scale a reality,” said Sameer Kamat, CEO of TrueLoyal. “Our Churn Prediction engine eliminates the guesswork, allowing brands to understand and anticipate their customers’ needs. We’re delivering the strategic foresight needed to build lasting relationships and maximize lifetime value.”
Key features of Churn Prediction include:
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Actionable Risk Scoring: Each loyalty member receives a churn risk score, allowing brands to prioritize offers and outreach.
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Industry-Specific Intelligence: AI models are tailored to unique purchase behaviors across sectors like automotive, beauty, and consumer packaged goods.
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Seamless Integration: Risk insights are embedded directly into member profiles, campaign tools, and analytics dashboards for immediate action.
By shifting from reactive to predictive retention strategies, TrueLoyal aims to give consumer brands a competitive edge in an increasingly fragmented and costly customer acquisition landscape.
