The Challenge

In collections operations, prioritization is everything. With limited team capacity and varying case complexity, the question is always: which cases should get attention first, and how do we ensure nothing critical slips through?

Traditional approaches relied heavily on simple rules—days past due, outstanding amount—but these didn't capture the nuance that experienced operators intuitively understood.

The Approach

I worked with rule-based classification logic to improve how cases were prioritized. The goal wasn't to build sophisticated models, but to codify operational knowledge into systematic rules that could be applied consistently.

Rule-Based Classification

Working with the team, I documented the factors that actually influenced case outcomes: customer behavior patterns, timing signals, case characteristics that experienced operators knew mattered. These became the basis for classification rules that could prioritize work more intelligently than simple sorting.

Experimentation with Embedded Analytics

I also experimented with speech and text analytics capabilities embedded in our BI tools. The goal was to explore whether interaction data could surface additional prioritization signals—patterns in customer communication that might indicate likelihood of resolution or escalation risk.

This was experimentation, not production deployment. I worked with existing tooling to understand what was possible, feeding insights back into operational discussions about where to invest further.

The Outcome

Improved SLA adherence and more consistent prioritization across the team. The classification rules meant that even newer team members could make prioritization decisions that aligned with operational best practices.

The analytics experimentation didn't lead to immediate production changes, but it informed conversations about future capabilities and helped build organizational understanding of what these tools could (and couldn't) do.

What I Learned

There's a gap between what's technically possible and what's operationally valuable. My role isn't to push for the most advanced solution—it's to find the right level of sophistication for the context. Sometimes that's rule-based logic that everyone can understand. Sometimes it's experimentation that shapes future direction without disrupting current operations.

The key is knowing which situation you're in.