Case Study: Predictive Churn Reduction & Personalization

Banking

Predictive Churn Reduction & Personalization

The Challenge: Stemming the Tide of Customer Attrition

A well-established regional bank was experiencing a concerning rise in customer churn, particularly within its retail banking segment. Increased competition from digital-native banks and fintechs offering highly personalized experiences highlighted the bank's shortcomings. Their existing digital platforms felt impersonal, and marketing efforts were often generic, failing to resonate with individual customer needs. Critically, the bank lacked a systematic way to identify customers exhibiting early warning signs of churn, making proactive retention efforts difficult and reactive. This attrition not only impacted revenue but also increased customer acquisition costs needed to replace lost business.

Our Solution: Proactive Retention Powered by AI and GenAI

Lydatum developed a multi-faceted solution hosted on Microsoft Azure, combining predictive analytics with generative AI to enable proactive and personalized customer retention strategies. The key elements were:

• Churn Prediction Modeling: Leveraging Azure Machine Learning (Azure ML), we analyzed historical customer data stored in Azure SQL Database. This included transaction history, product usage, demographics, service interactions, and engagement patterns. We built and deployed sophisticated machine learning models that could accurately predict the likelihood of an individual customer churning within a defined future timeframe, assigning a "churn risk score" to each customer.
• GenAI-Powered Personalization Engine: We integrated OpenAI's powerful language models (accessed via Azure OpenAI Service) directly into the bank's Customer Relationship Management (CRM) system. When the churn prediction model flagged a customer as high-risk, the GenAI engine analyzed that customer's specific profile and interaction history. It then automatically generated personalized retention offers, communication scripts for relationship managers, or targeted marketing messages designed to address potential pain points and reinforce the value proposition for that specific customer.
• Integrated Workflow and Monitoring: The solution provided dashboards (potentially using Power BI, though not explicitly mentioned in the summary, it fits the Azure ecosystem) for bank staff to monitor churn risk across segments, track the effectiveness of retention campaigns, and understand the key drivers of churn identified by the ML models. Alerts were triggered for high-risk customers, prompting timely intervention through the CRM.

The Impact: Reduced Churn, Enhanced Satisfaction, and Smarter Campaigns

The integrated predictive and generative AI solution delivered significant improvements for the regional bank:

By identifying at-risk customers early and engaging them with relevant, personalized offers generated by AI, the bank successfully reduced its churn rate significantly. Customers responded positively to the more tailored interactions, leading to improved satisfaction and loyalty. Furthermore, the insights derived from the churn models allowed the bank to refine its product offerings and marketing strategies, leading to more effective resource allocation and higher overall campaign ROI. The ability to proactively address potential issues before they led to churn marked a strategic shift from reactive damage control to proactive relationship management.

Technologies Used: Azure Machine Learning (Azure ML), Azure SQL Database, Azure OpenAI Service, Azure Functions, CRM Integration