Skip to content

Executive Overview

MiaRec Conversation Analytics turns customer conversations into structured, searchable insights that help teams improve customer experience, coach agents, reduce risk, and grow revenue.

Today, many deployments start with voice calls (speech). MiaRec is designed to expand into omni‑channel analysis so the same insight framework can apply to chats, emails, and tickets when those channels are enabled in your environment.

What Conversation Analytics delivers

Conversation Analytics helps you answer questions like:

  • Which conversations are “at risk” and need attention right now?
  • Why are customers dissatisfied, and what topics are trending?
  • Which objections and competitors are coming up most often in sales calls?
  • Are agents following the right process and meeting quality standards?
  • What changed this week, and where should we focus coaching?

It does this by generating AI-powered insights from each conversation and making them available in:

  • Conversation details (per-conversation insights + explanations)
  • Dashboards (trends, averages, distributions, drilldowns)
  • Search & filters (e.g., “CSAT < 3”, “Competitor mentioned = Yes”)
  • Reports/exports (where enabled)

Who benefits

Conversation Analytics is commonly used by:

  • Customer Support & CX leaders: reduce escalations, understand top drivers of dissatisfaction, track CX metrics.
  • Supervisors & team leads: coach agents using conversation-level evidence and explanations.
  • QA teams: scale evaluations with automated QA and structured scorecards.
  • Sales leaders: identify objections, competitor mentions, deal signals, and next actions.
  • Operations & compliance: spot risk patterns, ensure policy adherence, and support audits (when enabled).

What makes MiaRec different

1) Insights are configurable (not one-size-fits-all)

MiaRec supports both:

  • Prebuilt insights (ready to enable)
  • Custom insights you define for your business (e.g., Hospitality: reservation dates, nights, VIP status)

2) Insights come with explanations

In addition to a structured value (score/category/date/text), MiaRec can capture an explanation that helps humans understand why the AI produced that result.

This is especially useful for:

  • QA reviews and coaching
  • debugging or tuning insight definitions
  • building trust with stakeholders

3) Insights flow through the same system surfaces

Once an insight is stored in a structured way, it can be consistently used across:

  • dashboards (including clickable buckets for drilldown)
  • conversation details
  • advanced search and filtering

Where to go next

If you want a quick mental model of how it works end-to-end, start here:

If you are an administrator looking for setup steps and configuration, see the Conversation Analytics – Administration Guide (separate document).