Looking back in order to leap forward!

Harnessing the Power of Data

Nimble is gathering vast amounts of customer, agent, and collection data — and uncovering powerful patterns using artificial intelligence (AI) and predictive analytics. These insights are shaping smarter business decisions, revealing opportunities for efficiency and growth.

Predictive analytics has become a major force across industries. Just as algorithms can match individuals on dating platforms, they can also identify patterns in agent performance and customer behaviour.

Data-Driven Recruitment and Performance

At Nimble, agents are the heart of the collections business. Using analytics, the company is redefining recruitment and performance management — moving away from intuition toward measurable, data-backed decisions.

Harnessing the Power of Data

The Agent Early Performance Predictor model identifies what data points are most predictive of future performance. By analysing patterns at 30, 60, and 90 days after employment, it can accurately forecast an agent’s performance at six months. This enables:

• Early identification of rising stars,
• Targeted performance-improvement interventions, and
• Tailored coaching around top-performer behaviours.

Voice Analytics and AI Screening

Nimble also uses speech recognition to unlock insights hidden within audio recordings. By converting unstructured call data into structured text, the system can analyse thousands of interactions for patterns and outcomes. For instance, the model can distinguish voicemail messages from live calls by detecting phrases like

“The person you have called is not available...” versus “Hello” (followed by silence).

This enables 40%–60% of outbound calls identified as voicemail to be automatically filtered out, allowing agents to focus on live conversations and maximise productive talk time.

Predicting Commitment Strength

Another challenge is understanding promises to pay (PTPs) — commitments customers make during calls to pay by a certain date. Not all promises are kept.

Nimble’s Promise to Pay Strength Model analyses collections data to determine the likelihood of a PTP being honoured. It examines behavioural and contextual features from each call, classifying them into two categories:

• Factors under the agent’s control (e.g., tone, timing, call duration)
• Factors outside the agent’s control (e.g., customer income patterns)

By applying these insights, Nimble increases PTP fulfilment rates through personalised agent coaching and smarter customer segmentation.

The Road Ahead

These examples illustrate how Nimble is transforming both structured and unstructured data into actionable intelligence. By embedding AI-driven insights into daily operations, Nimble is reimagining the future of debt collection — smarter, faster, and more human.

Written by: Ingrid de Leeuw – Executive