Two use cases — same platform
An in-house customer service team — 15 agents, 300 calls/day
The manager listens to 5 random calls per week for coaching. He doesn't know which call reasons recur most often, or which agents have the most customer tension.
After 3 months of VoiceIQ + TextIQ + PredictIQ analysis:
- 'Refused refund' represents 34% of Monday calls — never quantified before
- Agent 7 has a detected tension rate 3× above average — calls last 40% longer
- Customers who called more than twice in the month have 78% churn probability
- 3-month trend: escalation rate dropped from 23% to 11% after targeted training and refund policy revision
An outsourced call center managing 3 clients — 50 agents, 1,500 calls/day
The director must report on call quality to 3 clients but has no means to listen manually. SLAs are at risk without precise data.
After 6 months of VoiceIQ + TextIQ analysis:
- Client A: 67% of escalations occur between 5pm and 7pm — team reinforcement recommended
- Client B: current script generates detected frustration from minute 2 — reformulation proposed
- Client C: French-speaking agents have 2× fewer escalations than English-speaking agents on this account — best practice transfer initiated
- 6-month trend: average quality score up from 61% to 83% — SLAs met across all 3 clients
