The project
Through a customer churn prediction approach, we conducted an in-depth analysis of purchase data collected via loyalty cards, building a predictive system capable of early detection of churn signals among loyal customers.
The project included the creation of a detailed dataset on purchasing behaviors, the training of machine learning and artificial intelligence models to classify churn risk, and the implementation of explainability tools to identify the main drivers behind the phenomenon.
Results
Integration with the existing CRM platform led to a 30% reduction in churn within the loyal customer segment, thanks to timely interventions based on predictive data analytics. An interactive dashboard generates automatic alerts according to risk variables, enabling the team to act proactively.
The outcome is a significant increase in the lifetime value of recovered customers and a transformation of risk into long-term growth opportunities, supported by advanced AI-driven data analytics systems.
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