Move cursor | Click to ripple
Industries / Telecommunications

Churn shows up on the call before it shows up in the report.

In telecom, the retention moment, the repeat truck roll, and the revenue leak all happen mid-conversation. QEval scores every call for save behavior, critical-alert language, and the handle-time drivers, not the 2 percent a team can review by hand.

$2.94M
Savings in 180 days
At a Tier-1 US telecom operator
100%
Of calls scored
4.3M interactions, not a sample
-9.8%
Average handle time
From 14:20 to 12:56
+22%
First-contact resolution
At a major broadband provider
The shape of the problem

What a telecom call actually carries.

Every support and retention call is a churn decision, a revenue moment, and an operational signal at once. A 2 percent sample sees almost none of it.

01

You cannot sample a churn problem

The save call nobody reviewed is the customer who left. Cancel intent and competitor mentions are on the call in real words, and a sample misses most of them.

02

Critical-alert language hides in volume

Threats to cancel, outage anger, and complaint signals are the calls that matter most, and the ones least likely to land in a hand-reviewed sample.

03

Handle time and truck rolls have drivers on the call

Repeat calls, long silences, and avoidable transfers drive cost. Without reading every call, you fix the symptom and miss the cause.

04

Save behavior is uneven

The best retention reps do something specific at a specific moment. You cannot coach what you cannot see across every call.

On one call

What QEval hears on a single retention call.

One routine billing-and-cancel call. Tap a flag to see exactly where on the call it happened. This is the difference between a 2 percent sample and 100 percent coverage.

TranscriptBilling and retention
Agent

Thanks for calling, this is Sam. I see you are calling about your bill again.

Customer

Yes. My bill went up forty dollars and nobody can tell me why. I am about ready to cancel and switch providers.

Agent

Let me put you on a brief hold while I check.

Agent

Thanks for holding. The promotional rate ended, that is the increase.

Customer

Nobody told me it would end. This is the second time I have called about this.

Agent

I understand. I can transfer you to the retention team.

Agent

Actually, I can apply a loyalty credit and a new promotion right now if you would like to stay.

C
Predicted CSAT
Low
Churn risk
High
Save behavior55
Resolution64
Empathy72
Efficiency60
AI Coach

Recognize a cancel threat as the moment to act, attempt the save before offering a transfer, and set an expectation before every hold.

Illustrative call. Not a real customer interaction.
What changes when QEval is applied

The same calls, finally read in full.

Layer 1 - Compliance

Recorded-line and consent on every call

Recorded-line notice and required disclosures are checked across 100 percent of interactions at 95%+ recall, with the transcript moment attached as evidence.

Compliance and redaction →
Layer 2 - Customer

Cancel intent and complaint risk, while you can act

Competitor mentions, cancel intent, and repeat contacts are scored on every call and surfaced before the customer leaves, not after the disconnect.

Customer intelligence →
Layer 3 - Revenue

Save and upgrade behavior, scored and timed

QEval reads whether a save was attempted and an offer was well timed. A major broadband provider documented $9M in value, and one operator surfaced $762K in revenue leakage to recover.

Revenue intelligence →
Layer 4 - Operational

Why handle time and truck rolls climb

AHT, hold-time, and transfer drivers are clustered automatically. The Tier-1 operator cut AHT 9.8% and hold time 24%, and a broadband provider cut truck rolls 12%.

Auto QA →
Layer 5 - Training

Retention coaching tied to the moment

When a save is missed, the coaching points to the second it happened. The Tier-1 operator lifted its QEval QA score 15.5 points coaching exactly this.

Coaching and performance →
Data layer

Your data never leaves to an outside model

PII and account data are redacted before any model runs, and the Mixture-of-Experts model is built and run in-house. Nothing trains a third-party AI.

Security and trust →
Sector case study

A Tier-1 US telecom operator ran QEval across 4.3 million interactions from 1,378 agents, scoring 100 percent of them instead of the sample its team could review by hand. QEval cut the critical-alert rate from 32% to 15%, lifted the QEval QA score 15.5 points from 47.5% to 63%, and clustered the handle-time and hold-time drivers behind the numbers.

Average handle time fell from 14:20 to 12:56, hold time dropped 24%, and transfers fell 5 points. The documented result was $2.94M in savings in 180 days, with another $762K in revenue leakage surfaced for recovery. A separate major broadband and fiber provider documented $9M in value, a 30% capacity gain, and a 22-point lift in first-contact resolution. Outcomes attribute to the organizations, anonymized at their request.

$2.94M
180-day savings
-9.8%
Average handle time
32 to 15%
Critical-alert rate
$9M
At a broadband provider
Compliance and regulatory notes

What QEval helps you evidence.

QEval documents adherence across every interaction. It surfaces and records; it is the evidence layer your compliance team uses, not a substitute for legal counsel.

TCPA

Recorded-line notice and consent language flagged when missing, across every call rather than a sample.

Slamming and cramming

Unauthorized plan changes or charges raised in the conversation are detected and routed with the transcript moment attached.

FCC onshoring proposal

The 2026 proposal on English proficiency and offshore-handling disclosure is a proposed rule. QEval scores language clarity and disclosures so you can see where you would stand. See Compliance Watch.

Truth-in-billing

Rate, fee, and promotion-end disclosures scored for clarity, so billing confusion surfaces before it becomes a complaint.

Accessibility

Relay and TTY handling and reasonable-accommodation language monitored across interactions.

Complaint handling

Complaint signals detected and routed in real time, with a full audit trail from score to the exact words that triggered it.

SOC 2 Type IIISO 27001ISO 42001PCI DSS Level 1HIPAAGDPRCCPA

QEval surfaces, scores, and documents adherence to your policies and applicable regulations. It does not provide legal advice, and the items named here are illustrative of what QEval helps you monitor, not a complete compliance checklist.

Built on the Six Layers

Telecom lives in customer, revenue, and operational intelligence.

Quality and compliance is the floor. The value compounds in churn, save behavior, and the handle-time drivers, all read from the same calls.

L1 Quality and Compliance L2 Customer Intelligence L3 Revenue Intelligence L4 Operational Intelligence
See all Six Layers →
For telecom

Questions ops and retention ask.

Can QEval score save and retention behavior specifically?

Yes. Save attempts, offer timing, and cancel intent are scored on 100% of calls. One Tier-1 operator lifted its QEval QA score 15.5 points by coaching exactly these behaviors, tied to the moment they happened.

Does this help with handle time and truck rolls?

Yes. Handle-time, hold-time, and transfer drivers are clustered automatically. The Tier-1 operator cut AHT 9.8% and hold time 24%, and a major broadband provider cut truck rolls 12% and lifted first-contact resolution 22 points.

We already have QA from our CCaaS. Why add QEval?

CCaaS-native QA typically scores a small sample at 65 to 70% accuracy. QEval scores 100% of calls at the 94%+ contractual SLA and reads beyond compliance into churn, save behavior, and operational drivers, where most of the value sits.

How fast can this be live?

30-day deployment, contractual. The Tier-1 operator documented $2.94M in savings in 180 days, and the documented customer-average is ROI in 120 days, with 60-day exit rights.

Telecommunications

Bring us a week of your save calls.

We will score a real sample of your retention, billing, and support calls and show you the churn signals, the handle-time drivers, and the revenue your current program is not catching.

Contractual commitments

Four numbers no peer publishes.

94%+
Accuracy SLA
Written into the master agreement
30 days
Deployment
Money-back guarantee
60 days
Exit clause
Cancel with notice, no penalty
120 days
ROI
Documented customer-average outcome