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Capabilities at a Glance

MiaRec Conversation Analytics can generate a wide range of AI-powered insights from conversations. Insights can be prebuilt (ready to enable) or custom (defined by your organization).

Note: Exact capabilities depend on your deployment and enabled channels/features.

Conversation understanding

These insights help users quickly understand what happened and why.

  • Conversation (Call) summarization
  • concise summary
  • key points and next actions
  • Topics
  • what was discussed (multi-label, trendable)
  • Sentiment
  • overall sentiment and/or sentiment over time
  • Reason and outcome
  • why the customer contacted you (reason)
  • what happened (outcome / resolution)

Customer experience insights (CX)

These insights help quantify customer experience and spot problems early.

Common examples include:

  • CSAT (Customer Satisfaction) – 1–5 score with explanation
  • NPS / NES (where enabled)
  • Top issues reported
  • Issue resolution
  • Escalation reason
  • Churn risk (risk scoring, categories, or rationale)

Sales insights

These insights help sales teams understand pipeline signals and coaching opportunities.

Examples include:

  • Lead score
  • Lead stage
  • Deal amount (where expressed in the conversation)
  • Competitors mentioned
  • Top objections
  • Pain points
  • Urgency level
  • Next actions
  • Sales lost reason
  • Missed opportunity indicators

Quality assurance (Auto QA)

Auto QA supports structured evaluation against a rubric/scorecard, such as:

  • compliance / required statements
  • script adherence
  • empathy and communication skills
  • resolution behavior

Auto QA outputs can be used for:

  • QA dashboards (distribution, trends)
  • coaching workflows
  • audit support (where enabled)

Custom insights for your industry

Beyond prebuilt insights, you can create tenant-specific insights such as:

  • Hospitality:
  • Room reservation start date
  • Total nights
  • VIP status
  • Healthcare:
  • Appointment date/time
  • Insurance type
  • Follow-up requirements
  • Field services:
  • Service address
  • SLA window
  • Parts required

The key idea is consistent:

Define what you want to extract → store it in a structured field → use it in dashboards/search.

What an “insight” looks like in MiaRec

An insight typically includes:

  • a structured value (number, date, category, text)
  • an explanation (short rationale grounded in the conversation)

This pattern supports both: - analytics (dashboards, filtering, reporting) - human review (trust, QA, coaching)

To understand how insights are produced and stored, see: - Custom Fields and Metrics - AI Tasks and Prompts