Smart Collections & Debt Management Services | Propensity to Pay Analysis Solutions for E&U Companies

Data-Driven Debt Management: Turning Insights into Actionable Recovery Strategies The Case for Intelligence-Led Collections Rising energy prices, evolving payment behaviors, and regulatory scrutiny demand a smarter approach to arrears. Data-driven debt management transforms raw information into precise actions that reduce days sales outstanding, protect vulnerable customers, and preserve lifetime value. Modern programs blend analytics, behavioral science, and automation to create recovery pathways that are fair, efficient, and scalable—exactly what collection services for e&u companies must deliver. Building a Trusted Data Foundation Effective strategies start with clean, connected data. Utilities should unify billing, smart meter, CRM, complaint, and field service records into a standardized model with strong data quality checks. Master data management and clear ownership ensure every account, premise, and meter event is reconciled. With this foundation, analysts can profile delinquency patterns, detect anomalies, and build features that meaningfully predict payment outcomes. Segmenting Customers by Risk and Intent Not all arrears are alike. Behavioral segmentation distinguishes temporary hardship from chronic delinquency and fraud risk. Dimensions such as tenure, tariff type, consumption volatility, historic promise-to-pay adherence, and digital engagement reveal intent and capacity to pay. Each segment then receives a different mix of outreach, payment options, and timelines—reducing unnecessary pressure on low-risk customers while focusing effort where it matters. Predictive Modeling that Prioritizes Action Machine learning models estimate the probability and timing of self-cure, response to payment plans, or likelihood of disconnection. Uplift models go a step further, ranking customers by expected improvement if a specific intervention is applied. This allows operations to prioritize high-impact actions—such as flexible installment plans for nearterm payers or escalation for accounts with persistent non-payment—optimizing agent time and cash recovery. Omnichannel Orchestration with Sensitivity

Analytics-driven playbooks coordinate emails, SMS, IVR, chat, and agent calls to meet customers on their preferred channels at the right moments. Content and tone are adapted to the customer’s segment and recent behavior. For example, high-propensity payers receive gentle reminders and quick digital payment links, while at-risk households are offered hardship support and energy efficiency guidance. Every touchpoint is logged, enabling rapid testing and improvement. Field Operations and Prepayment Levers For cases requiring in-person activity, route optimization models reduce truck rolls by clustering visits by geography, meter type, and predicted outcome. Where policy permits, prepayment meters can be introduced responsibly, with analytics identifying customers for whom prepay will improve budgeting and reduce arrears. Smart meter events—like abnormal consumption after disconnection—feed risk signals back into the strategy. Measuring What Matters Clear KPIs drive accountability. Core measures include right-party contact rate, promiseto-pay kept rate, roll-rate reduction across delinquency buckets, cure rate by segment, and cost to collect per dollar recovered. Leading indicators—such as digital adoption and average time-to-payment after outreach—signal whether playbooks are working before month-end results arrive. Dashboards expose performance by cohort, channel, and agent to inform daily decisions. Governance, Fairness, and Compliance by Design Strong governance ensures models are explainable, auditable, and free from discriminatory bias. Champion–challenger frameworks validate that new strategies improve outcomes without unintended harm. Consent management, secure data handling, and transparent customer communications align with regulations and build public trust—critical for essential service providers. From Insight to Continuous Improvement Data-driven debt management is a living system. Weekly test-and-learn cycles refine contact cadence, offers, and scripts based on experimental evidence. As macro conditions shift—weather events, tariff changes, or seasonal usage—forecasting models adapt recovery targets and capacity plans. By institutionalizing these feedback loops, utilities turn insights into consistent, humane, and high-yield recovery strategies that strengthen both cash flow and customer relationships.