Deferred Resolution Issues: Why Measuring Correctly Matters for Customer Experience 📊

In many industries, a customer’s problem can be solved there and then during the call. A billing dispute is corrected, an identity is verified, or an appointment is confirmed. These are immediate-resolution issues — and when the call ends, both the customer and the business know the matter is resolved.

But what about situations where the issue cannot be fixed on the call itself?

Think of property management: a tenant calls to report a leak, a heating outage, or a faulty fire alarm. The agent logs the details and schedules an engineer. The customer might feel reassured that help is coming — but from their perspective, the problem is not yet resolved. These are deferred-resolution issues.

Why Deferred Resolution Issues Are Easy to Misclassify 🔍

Measuring issue resolution sounds simple: was the customer’s problem fixed or not? But in practice, it’s easy for analytics systems — and even some QA processes — to get this wrong:

• Politeness bias: Customers often say “thank you” or express relief when their issue is logged. Language models can mistake this for resolution.
• Definition drift: “Addressed” (agent acknowledged and logged the issue) is not the same as “resolved” (problem fixed and confirmed).
• Deferred closure: When the fix happens later (engineer visit, claims team review, back-office approval), the conversation itself ends without resolution — yet the outcome is still critical to measure.

If these cases are incorrectly scored as resolved, businesses end up with inflated resolution metrics, blind spots in customer pain points, and missed opportunities to improve operations.

How LLMs Can Help — With Guardrails 🛡️

Large Language Models (LLMs) can analyze transcripts at scale, spotting whether issues were resolved from the customer’s perspective. But they need careful prompting and design to avoid the pitfalls above. At AutoInsights, we use a stricter definition of resolution:

• Yes = The customer’s issue was fixed in-call and explicitly confirmed as resolved.
• No = The issue was only logged, escalated, or deferred — regardless of customer politeness or relief.

By enforcing this clear distinction, LLMs can avoid false positives and give businesses a truer picture of whether customers leave calls with their issues actually resolved.

Why Human-in-the-Loop Reviews Still Matter 👥

Even with advanced AI, HITL (Human-in-the-Loop) reviews are essential. Humans catch edge cases, test whether prompts are working as intended, and ensure the AI doesn’t conflate “customer satisfaction with the process” with “customer satisfaction with the outcome.” In our own testing, HITL reviews flagged deferred-resolution calls that were incorrectly scored as resolved, prompting us to refine the model.

This cycle — AI analysis + HITL validation — ensures metrics remain trustworthy across different industries and call types.

Getting It Right Matters

• Customer experience: Misclassified “resolved” issues mask the frustration customers feel when problems drag on.
• Operational insights: Understanding the volume of unresolved calls helps organizations improve escalation paths and service delivery.
• Trust in analytics: Executives and managers need to trust that their metrics reflect reality, not politeness or sentiment bias.

Final Thought

Not all issues end when the call does. Businesses that distinguish between addressed and resolved will be better equipped to improve both customer satisfaction and operational efficiency. With the right mix of LLMs for scale and HITL for validation, organizations can avoid blind spots and truly measure issue resolution from the customer’s perspective.