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Trust, Quality, and Expectations

Conversation Analytics uses AI to interpret human conversations. AI results are powerful, but they are probabilistic—meaning you should plan for validation, calibration, and continuous improvement.

This page sets expectations and describes practical ways to build trust in insight outputs.

AI outputs are structured, but not absolute truth

AI insights (scores, topics, classifications, extracted entities) are generated by analyzing the text of a conversation. Results can vary based on:

  • transcript quality (especially for calls)
  • missing context (e.g., a follow-up happened outside the conversation)
  • ambiguous phrasing
  • business-specific definitions (what “resolved” means to you)

The goal is to produce useful, actionable signals, not perfect judgments in every individual case.

Why explanations matter

MiaRec commonly pairs a value with an explanation that:

  • references evidence from the conversation
  • summarizes why the score/category was chosen
  • helps supervisors and QA reviewers confirm correctness quickly

This supports: - faster human review - more transparent coaching - easier tuning when you need to adjust definitions

A simple approach that works well for most organizations:

  1. Start with a small set of insights
  2. Example: summarization + sentiment + CSAT
  3. Sample and review results
  4. review a handful of conversations per team/queue
  5. check both the value and explanation
  6. Calibrate definitions
  7. adjust thresholds, allowed values, or insight rules if needed
  8. Roll out gradually
  9. expand to more teams once you trust consistency

Handling “unknown” or insufficient evidence

Some conversations do not contain enough evidence to compute an insight reliably (too short, unclear, missing transcript). When this happens, it’s often better to return:

  • Unknown / Not enough evidence
  • or omit a value (depending on your configuration)

This prevents misleading analytics.

Tips for consistent, trustworthy insights

Even without deep AI expertise, administrators can improve consistency by:

  • keeping scoring scales and definitions explicit (e.g., CSAT 1–5)
  • using clearly defined dropdown options for classifications
  • setting filters to exclude conversations that should not be analyzed (e.g., < 15 seconds)
  • validating before enabling insights broadly

Where to find configuration steps

This document is conceptual by design. For step-by-step configuration and testing workflows, see:

  • Conversation Analytics – Administration Guide (tenant configuration)
  • Conversation Analytics – Platform Setup & Operations Guide (platform/operator configuration)

Next: Key concepts

If you want a deeper understanding of the core building blocks: