Three key concepts to a powerful attrition model

by Marla Sferra-Pieton and Joan Clark, Alkami

FACT: Financial Institutions are losing customers and revenue because they don’t have an ongoing strategy to manage attrition that is based on data.¹

Research shows that by employing predictive analytics and arming relationship managers with data-driven insights and enablers, financial institutions can reduce total attrition by 20% to 30%—a result that would nearly double most banks’ average revenue growth.²

In the first blog of our Attrition series, Slam the Door on Attrition, we stated that not all attrition models are created equal. Attrition models that dig deep into the Financial Institution’s (FI’s) full universe of customer data – both product utilization and everyday purchase spend – help an FI identify the early warning signs that a customer is reducing their commitment to the institution. Models that leverage a customer’s spend transactions, held-away payment activity, banking behaviors, and product mix consider the full 360-degree view of the customer in evaluating their likelihood to attrit. The uniqueness of the transaction data – such as identifying customers making micro-deposits into Chime, or a drop off in automatic withdrawals of car insurance payments, or an increase in the number of monthly payments made to competing institutions – combined with product data is the predictive modeling approach FI’s need now.

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