Contact Center Case Study:
Medicare Supplement Insurance Provider
Takeaway: A hidden inefficiency was costing this insurer thousands of agent hours and hundreds of thousands of dollars per month. Sturdy Statistics transformed unstructured transcripts into structured insights that immediately revealed the root cause.
The Problem: Unexplained Long Calls That Didn’t Convert
A Medicare supplement insurance provider noticed an unusual pattern in its sales calls. Typically, sales calls in this industry follow one of two paths:
- Short calls (<15 minutes) that don’t result in a sale, or
- Long calls (>45 minutes) that end in an enrollment.
You do not expect someone to spend 45 minutes on the phone with an insurance company unless he or she has serious intent. However, this provider noticed a troubling third category of calls: long calls that didn’t convert. These wasted thousands of agent hours per month and cost the company hundreds of thousands of dollars in marketing spend. Despite extensive analysis of call metadata — landing pages, marketing campaigns, geographic data, and browser cookies — the company could not determine why these calls were failing.
How Sturdy Statistics Uncovered the Hidden Cause
We ingested one year of call transcripts alongside metadata and automatically structured each conversation into distinct topics. A simple regression analysis surfaced a rare but critical pattern in failed calls — conversations about union and veteran benefits. These topics:
- Appeared only briefly, and in a small subset of calls,
- Almost always led to abrupt call drop-offs with no sale, and
- Were too infrequent for manual analysis with random sampling to detect.
The Fix: Adjusting the Sales Process to Prevent Lost Time
When we shared this insight, the company’s leadership immediately recognized the issue: callers with certain benefits were ineligible for some supplement insurance plans. They realized this ineligibility was being discovered too late in the process, after a long enrollment attempt.
With this data, the company revised its sales workflow to screen for ineligible callers earlier in the conversation. Following the intervention, the company used Sturdy Statistics to continuously monitor for unqualified callers and to refine its sales script.
Impact:
Sturdy Statistics instantly highlighted an inefficiency that manual review had missed, enabling the company to streamline sales calls, cut costs, and improve overall efficiency.
What will Sturdy Statistics help you discover in your data?