Customer Churn Analytics
Bank of Ireland observed rising credit card customer attrition impacting portfolio profitability. Decision Studio designed an end-to-end churn prediction and intervention framework, enabling early identification of high-risk customers up to 90 days before churn and achieving a 15–20% reduction in monthly churn rates.
The key challenge was to identify early churn signals from highly fragmented data sources including transaction history, customer demographics, digital behaviour, complaints, customer service interactions, and marketing engagement data. Additional complexity arose from data quality issues, regulatory constraints, and the need to create explainable outputs that relationship managers and marketing teams could easily act upon.
Decision Studio led the end-to-end analytical solution design, integrating data from core banking systems, CRM, call centre platforms, and digital channels into a central analytical data model. Behavioural features were engineered such as spend trend decline, transaction frequency change, repayment patterns, customer service contact frequency, and campaign response history. Using these features, predictive churn models were developed and validated, segmenting customers by churn risk and lifetime value. Interactive dashboards and actionable alert mechanisms were designed that enabled marketing and customer engagement teams to trigger targeted retention strategies such as personalised offers, fee waivers, and proactive outreach.
15–20%
The solution enabled early identification of high-risk customers up to 90 days before churn, leading to a 15–20% reduction in monthly churn rates and a significant uplift in campaign conversion rates. The bank achieved measurable improvements in customer retention, marketing ROI, and overall portfolio profitability. Additionally, the analytical framework became a reusable asset for other retail banking products, supporting broader customer engagement and growth strategies.
