Optimizing Debt Collection to Maximize Client Lifetime Value

In many telecommunications markets, a large portion of customers subscribe to postpaid plans. Consequently, client debt can frequently become a key challenge. Having a relevant portion of its client base in arrears, a leading telecommunications provider partnered with LTP to improve its debt collection process. Customizable payment arrangements were at the core of that process, serving as the most relevant tool in the debt collection strategy. These arrangements can be tailored using three main levers: the write-off (a percentage of the total debt that is forgiven), the initial payment amount (a percentage of the remaining debt after the write-off), and the number of installments over which the remaining debt is paid.

Initially, these payment terms were defined manually and empirically by customer service agents during direct negotiations with clients. While agents were well-trained to focus on maximizing immediate debt recovery, their decision-making process was largely intuition-based, lacking a structured and analytical foundation. Additionally, their approach usually overlooked the long-term impact of payment terms on the client’s lifetime value. As a result, some agreements could lead to short-term recovery at the expense of customer loyalty, future revenue, or increased risk of re-delinquency.

Therefore, the company wanted to overhaul the approach to the definition of payment terms, while sustaining the payment arrangements as a core tool. The key requirement was to steer the new approach towards the maximization of each client’s lifetime value, encompassing debt recovery, churn prevention and future revenue. Therefore, transforming debt collection from a purely financial process into a strategic customer management function.

To address the double challenge of suboptimal debt recovery and neglected client lifetime value, the proposed solution was a data-driven personalization framework for customizing payment arrangements, guided by a newly defined performance indicator: Client Economic Value (CEV). The CEV assessed the value generated by a client over the long term, as a percentage of its total potential value (defined as the sum of the outstanding debt and of the expected future fees, if churn is avoided and scheduled payments deadlines are met). Unlike traditional metrics focused solely on short-term debt recovery, the CEV balanced the conflicting objectives of debt collection and customer value maximization.

At the core of the solution was a predictive modeling approach that estimated the post-arrangement CEV based on individual client characteristics – such as total debt, recent payment behavior, number of prior payment agreements and tenure – and payment terms. These predictions fed into a prescriptive engine that recommended the ideal payment arrangement for each client, optimizing the write-off amount, the initial payment, and the number of installments. That recommendation was then embedded into the front-end employed by customer service agents.

However, a significant constraint in building the predictive model was the lack of variability in historical payment arrangement data, given that most arrangements had been manually defined within narrow boundaries. To overcome this, a design of experiments (DoE) was carried out. This structured experimentation process randomly introduced variability in the arrangement configurations offered to clients in debt, in a safe and controlled fashion, enabling the company to observe how different payment terms affect both debt recovery and the CEV across client segments. By actively exploring the space of potential arrangement configurations, the DoE allowed for robust model training and better generalization across client profiles.

As a result, the predictive model served as a solid foundation for the recommendation of personalized payment arrangements, dynamically balancing short-term debt recovery with long-term client value. The system learns which combinations of payment terms yield the highest CEV for different client archetypes, guiding and empowering customer service agents to offer the most effective plans, by aligning their efforts with broader business objectives.

This solution effectively transformed debt collection into a strategic, data-informed process. By shifting from one-size-fits-all negotiation to analytically optimized personalization of payment arrangements, the company significantly improved both financial outcomes and customer retention. Looking solely at the verified effect on client churn and loyalty, the solution had a positive impact of around 2% in total annual revenue. Improved financial outcomes also derive from higher average repayment rates and lower variability in agent performance.

In summary, the implemented approach didn’t just improve collections - it drove a strategic shift toward customer-centric value recovery, turning debt management from an operational function into a lever for growth, loyalty, and profitability. The new Client Economic Value (CEV) also played a role by itself, by serving as a unifying objective that bridged the gap between finance, customer service, and marketing teams. In other words, it enabled a stronger cross-functional alignment, ensuring that all departments were working towards a balanced outcome that considered both financial health and customer experience.

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