The Churn & FCR Lifeline: Predictive AI Strategies for Banks and Contact Centers

The Ultimate Debate on Customer Retention and Operational Efficiency

Are you a bank manager, contact center leader, or data strategist struggling with customer attrition? The banking sector is evolving rapidly, influenced by challenging consumer preferences and a competitive market. Customer Churn poses unique challenges and seriously affects business operations when customers discontinue their relationship with a bank.

This podcast dives into two critical case studies—one focused on predicting bank customer churn using AI/ML for customers leaving credit card services, and the other on forecasting First Call Resolution (FCR) likelihood. FCR is a crucial operational KPI related to cost effectiveness and client retention.

What You Will Learn:

• Model Showdown: We analyze the performance and predictability of 3 different AI/ML algorithms—specifically comparing Random Forest, XGBoost Model, and AdaBoost Model—for their effectiveness in predicting both customer churn and FCR likelihood. In the FCR prediction, the XGBoost model showed much better predictive performance.

• Data Strategy: Discover which data features are essential for predictive models, including transactional data (volume/value), relationship features (age of account, demographics), and crucial engagement features like First Call Resolution (FCR) outcomes. We also examine the use of advanced inputs, such as Speech-to-Text conversion and summaries from customer communication channels (phone calls, emails, live chat).

• The Proactive Playbook: Learn how proactive identification of customers at attrition-risk can lead to better customer retention through timely services and offerings. We dissect the operationalization process, including how even resource-constrained firms can use low-cost data (like unresolved tickets or late payments) and leverage affordable no-code ML platforms (such as BigML or RapidMiner) to deploy effective predictive models.

• FCR Deep Dive: Understand how a predictive model can forecast FCR likelihood in real time, directing agent and supervisor efforts proactively to prevent costly repeat contacts and enhance customer satisfaction.