Stay or Go? Predicting Customer Churn through Graph Relationships

Session Track: Data Intelligence

Session Time: 04:00 am - 04:30 am November 6

Session description

Customer churn is a critical concern for businesses aiming to retain users and sustain growth. Traditional churn prediction models often fail to capture the complex and dynamic relationships between customers, their interactions, and their environments. Neo4j, a graph database, offers a powerful alternative by modeling customer behavior as an interconnected network of entities, such as purchases, support tickets, subscriptions, and engagement events. This approach enables organizations to identify early warning signs of churn, such as decreased activity, unresolved complaints, expired subscriptions, or disconnection from the service network. By leveraging graph-based features and relationship patterns, companies can detect clusters of at-risk users, trace behavioral paths leading to churn, and analyze how customer interactions evolve over time. Additionally, integrating Neo4j with the GDS library and external machine learning tools allows for advanced churn prediction through community detection, similarity algorithms, and real-time graph analytics. This results in more accurate, actionable insights, enabling timely interventions and personalized retention strategies.