It is no secret that customer churn costs banks a significant amount of lost revenue. The primary issues with current customer retention methods are that they are often inefficient, reactive, and impersonal. 41% of financial services customers who churned mentioned a lack of personalization as a big driver for leaving (BCG). Loyalty programs are well suited for a segment of customers but are costly at scale. In addition, non-targeted outreach can overwhelm customers—especially when not personalized—and promotions can be inefficient when given to customers who were going to stay anyway.
AI enables your relationship managers to understand the health of their relationships with customers by predicting which customers are likely to churn within a specified period of time. Supplementing this prediction with data on the customer’s value will allow your relationship managers to prioritize their resources on customers who are most vulnerable (most likely to leave) yet are also the most valuable to retain. Additionally, for every customer, AI will help your relationship managers understand the key contributing factors to churn and provide them with suggestions for re-engaging with those customers by offering the products they are most likely to purchase. This combination of predictions offers you a holistic view on your customers to serve them in the most intimate but efficient way possible.
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