Customer Spending Anomaly & Alert System
The client required a solution to anticipate customer attrition within a complex retail ecosystem. The primary challenge was the high heterogeneity of purchasing behaviors, rendering standard "one-size-fits-all" models ineffective across different product verticals (e.g., Fashion, Beauty, FMCG) and customer value tiers. The system needed to decouple predictions to handle different levels of data granularity.
We enhanced the predictive core by integrating digital behavioral signals and sentiment indicators to refine customer value forecasts. The solution employs clustering algorithms to infer the potential value of new users and features an automated monitoring system. This mechanism tracks real-time spending against predicted baselines, triggering immediate business alerts when significant deviations or anomalies are detected.
The tool empowers the Customer Value team to operationalize hyper-personalized retention strategies. By leveraging granular predictions based on specific category affinities and time horizons, the business can automatically trigger retention actions for strategic segments, protecting the high-value customer base and optimizing marketing ROI.
AI-Driven B2B Lead Prioritization
