Maximize Customer Lifetime Value at each stage of the Customer Journey
Case Study for Financial Services Industry
The Financial Services Organization
A large financial services organization supporting repayment, settlement, and consumer payment plans manages thousands of active payors each month across a diverse portfolio. While the business plays a critical role in helping consumers stay on track with their financial obligations, it also faces significant operational and revenue risk when payors fall behind or fail to meet agreed payment terms.​
Historically, the organization relied on a rigid, rule-based approach to manage repayment risk. A payor was only classified as “broken” after 45 days without a payment — a threshold that signaled a problem after it had already occurred. This reactive model left little opportunity to intervene early, often resulting in lost repayment value, fewer options to reinstate payment arrangements, and increased operational strain on servicing teams.
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While the organization had access to large volumes of customer and payment data, it lacked the ability to translate those signals into early, actionable insight. As a result, outreach efforts were often too late, too broad, or misaligned with which payors were still recoverable.
The Challenge
The core challenge was to move from a reactive, rule-based process to a more predictive and proactive approach to repayment risk. The organization needed a way to identify early signals that a payor was likely to break — well before the 45-day threshold — and to distinguish between customers who required immediate intervention and those who could self-correct.
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By predicting repayment risk earlier and prioritizing outreach toward payors who were struggling but still recoverable, the organization aimed to protect repayment value, reduce operational burden, and create more effective, timely engagement strategies at scale.
