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Customer Support and CX

Conversation Analytics helps CX and support teams understand customer needs at scale and focus coaching and process improvements where they matter most.

Problems this solves

Support organizations often struggle with:

  • inconsistent visibility into why customers are contacting you
  • slow identification of emerging issues (“we didn’t notice until it became a crisis”)
  • manual QA and limited sampling
  • difficulty linking coaching to measurable outcomes

How Conversation Analytics helps

Common CX workflows include:

1) Track CX metrics over time

Examples: - CSAT distribution and average - churn risk trends - issue resolution rates (where enabled)

Dashboards make it easy to see: - what’s improving vs getting worse - which teams/queues need attention

2) Drill down into dissatisfied conversations

Example workflow: 1. Open a CSAT dashboard 2. Click the “Dissatisfied” bucket 3. Review conversations and explanations 4. Identify common drivers (topics, process failures, policy issues) 5. Take action (coaching, documentation updates, escalation playbooks)

3) Identify top issues and escalation drivers

Topic and reason/outcome insights can highlight: - top drivers of contact volume - spikes in certain issues - common escalation reasons

What to look for

When using CX insights, teams often focus on:

  • trend changes (week-over-week shifts)
  • concentration (a few issues driving many low scores)
  • coaching opportunities (patterns of missed empathy or resolution steps)
  • systemic issues (policy/process gaps)