Coach, not report.
Most platforms hand a supervisor a score and a dashboard. QEval® turns the score into the next best action, the right training, a documented coaching session, and a measured behavior change, then attributes that change to the business outcome it moved. One engine runs the whole loop.
Why coaching rarely changes behavior.
Four structural reasons, and none of them are about supervisors trying harder. Coaches coach every day. The work around the conversation is what breaks.
It sees almost nothing
Manual QA reviews only one to two percent of interactions, so almost everything goes uncoached.
It coaches to the wrong number
QA scores often do not move with CSAT, so coaching aimed at the score does not move the outcome.
The follow-up is what slips
Coaching runs in a daily loop, but the follow-up rarely happens, so the behavior is never re-checked and the loop stays open.
No one can see if it worked
Without before-and-after on the same behavior, a coach has no read on whether a session moved anything.
Coaches coach every day. The work is everything around it.
The conversation is the easy part. The hours are spent figuring out who to coach, what to coach, and what it is worth, and then the follow-up slips. QEval® serves the three answers a coach hunts for, each with a predicted impact.
Scan dashboards, pull reports, fall back on gut feel.
An impact-ranked coaching queue, ready when the supervisor logs in.
Listen through calls hoping to find the moment that matters.
The exact behavior and the transcript moment that shows the gap.
No way to know what a session will move before you run it.
A predicted impact on the agent's outcomes, before you coach.
And the follow-up never slips. Coaching runs in a daily loop, and the step that usually breaks is the follow-up. QEval® logs every action, holds the agent's acknowledgement, re-scores the same behavior, and closes the loop, so nothing coached is left unchecked.
Watch one score become a measured behavior change.
This is the loop, running on one agent. It starts on its own. Click any step to jump to it. The data here is a sample, the workflow is the product.
QEval® scored 100% of Nicole's interactions and ranked the gaps by impact
The skill-gap expert sub-models do not pick the lowest score. After-call documentation scores lower (70), but empathy in de-escalation moves more CSAT and retention for a retention agent, so it is ranked first.
One recommendation, tied to a specific moment, with its predicted impact
The right learning attaches itself, automatically, from the scored gap
No manual course hunting. The gap routes the agent to the matching QEval® LMS module, sized to fit between calls.
One-Click Coaching builds the session in seconds, and the agent signs off
The session is created with the agenda already filled from the transcript moment. The supervisor coaches one skill. The agent acknowledges. The action plan is logged.
Reinforced on the same skill, and engagement is tied to progress, not raw rank
QEval® re-scores the same behavior, prompts on login when she is below benchmark, and the gamification layer celebrates the improvement and her peers, not just the top of a leaderboard.
The behavior changed. Now QEval® shows which business outcome it moved.
Before-and-after measurement closes the loop. Then the change is attributed up through the Six Layers, so improvement reads as a dollar, not a feeling. The loop repeats on the next gap.
How a score becomes a behavior change.
The HI Model is QEval®'s behavior-change loop. AI delivers the data. Human Intelligence drives the change. Coaching is triggered by evidence, a specific transcript moment, not by the calendar. That is the documented source of the 300% lift in coaching frequency.
Delivers the data
- Scores 100% of calls, chats, and emails, objectively
- Finds the patterns a supervisor cannot see at scale
- Predicts trajectories before issues escalate
Drives the change
- Coaching conversations that turn insight into behavior
- Goals and development plans set with the agent
- Recognition and culture that make the change stick
The AI does not replace the coach. It hands the coach the one thing worth coaching, with the evidence already attached, so the human time goes to the conversation instead of the prep.
Six capabilities, one scoring engine.
Competitors sell these as separate modules bolted onto a score from somewhere else. In QEval® they are one system, fed by the same evidence that produced the score.
The single highest-impact move, per role
One recommendation for each person, with a predicted impact attached.
The right learning, assigned by the gap
The scored gap routes the agent to the matching QEval® LMS module, and completion is correlated back to performance.
Prep, deliver, document, follow up
Sessions built in seconds with the agenda pre-filled from the evidence. Acknowledgement and follow-up are built in.
Coaching guidance, written for the supervisor
Per-agent guidance with the example calls and the expected lift. Documented 40% faster improvement cycles.
Engagement tied to improvement, not raw rank
Leaderboards, goals, and peer recognition that reward progress, not volume. Documented 60% higher engagement.
See the trend, and ask in plain English
Trends flagged before they escalate, so you coach on a pattern, not one noisy month. Ask the data in plain language.
Coaching anchored to the exact turn that triggered it.
Other platforms cite evidence in the abstract. QEval® points at the literal moment, coaches it, and then shows the same agent's next calls improving on that behavior.
On turn 3 of CALL_7712, Nicole moved to the offer before acknowledging why the customer wanted to cancel. QEval® flagged the turn, the supervisor coached the empathy opening, and the next ten retention calls led with acknowledgement first.
Everyone sees their next best action.
The same scored interaction produces a different recommendation for each role, so the agent, the supervisor, the manager, and the executive all act on the same truth.
Behavioral focus
The one habit to work on this week, the matching learning, and progress against the goal set in coaching.
Coaching targets
Who to coach, on what, and the estimated impact, with the session prepped from the evidence.
Process issues
The patterns no single supervisor sees, quantified by business value across the team.
Strategic read
Where coaching is moving outcomes and where it is not, tied to the Six Layers of value.
Everyone ties coaching to outcomes. We show the chain.
The whole category stops at "coaching tied to outcomes." QEval® shows which outcome each behavior change moves, across the Six Layers of Intelligence. A QA platform captures the first layer. A performance management platform captures all six.
A QA platform captures the $1.2M slice. Coaching that only moves the QA score leaves the other 82% on the table. QEval® attributes the behavior change all the way up.
It schedules sessions. It does not close the chain.
Most CCaaS and workforce tools bolt a coaching module onto a score, then re-measure the same score. The loop is operational, never financial.
- Coaching is a separate module sitting on top of scores produced somewhere else, so the evidence and the action drift apart.
- Gamification, learning, and coaching are different SKUs that do not share a definition of good.
- The "closed loop" re-measures the same KPI it started with. No one shows the dollar the behavior moved.
- Coaching counts as activity. Four times more sessions can still be four times more busywork.
- One scoring engine drives the next best action, the training, the coaching, and the gamification from the same evidence.
- The score, the learning, and the recognition all share one definition of good behavior.
- The HI Model attributes the change up through the Six Layers, so improvement reads as a business outcome.
- Frequency is tied to evidence, so more coaching means more behavior change. It works for human and AI agents on one scorecard.
The coaching loop was not designed in a software lab. It was built and run inside a live contact center of more than 4,000 agents, against real scorecards, real attrition math, and the real problem of coaching at scale. Verint and NICE are software-first vendors. ETS Labs is an operator that built QEval® inside that operation and put a 94%+ accuracy SLA behind it.
Measured inside a real operation.
These are observed outcomes from QEval® production deployments, not projections.
higher agent engagement where the gamification layer ran, tied to improvement and peer recognition rather than raw rank.
increase in agent retention. With replacement cost at $10K to $20K per agent, the coaching loop pays for itself on attrition alone.
CSAT recovery, part of $6.5M in annual value, of which 82% came from layers beyond QA.
Outcomes from QEval® production deployments. The Fortune-500 automotive figures are anonymized at the customer's request.
Coaching, answered straight.
How is this different from the coaching our CCaaS already ships?
Bundled coaching modules sit on top of a score produced elsewhere and re-measure that same score. In QEval®, one engine produces the score and drives the next best action, the training, the coaching, and the gamification from the same evidence, then attributes the behavior change up through the Six Layers. The difference is not a feature. It is whether the loop closes on a business outcome or just on the metric it started with.
Does more coaching just create more busywork for supervisors?
It would, if frequency were the goal. QEval® triggers coaching on evidence, the specific transcript moment that shows the highest-impact gap, so the supervisor coaches one thing that matters with the prep already done. The documented 300% lift in coaching frequency is matched by 40% faster improvement cycles, which is the test of whether frequency turned into change.
Will gamification just demoralize the bottom of the leaderboard?
That is the failure mode of gamifying volume and ranking publicly, and it is well documented. QEval®'s engagement layer celebrates improvement and peer recognition, sets goals against each agent's own baseline, and ties to the same scored behaviors as coaching. The point is to reinforce the right behavior, not to crown a winner.
Can you coach AI agents the same way?
Yes. The same scoring engine evaluates human and AI agents on one scorecard, so an AI agent's conversations are scored, the gaps are surfaced, and corrections feed back to the team that owns the agent. The loop is vendor-neutral across human and AI agents.
What does the coaching loop actually do to attrition?
Dissatisfaction with the coaching relationship is the number one driver of agent attrition, and replacing one agent can cost anywhere from $10,000 to $20,000. QEval® documents a 40% increase in retention where the loop runs. Better, more consistent coaching is the cheapest retention program a contact center has.
Stop reporting. Start coaching.
Bring one week of your interactions. We will score them, surface the highest-impact gap on a real agent, and show you the coaching action and the outcome it moves, in 30 minutes.