Listener QM
Quality Monitoring Automated and augmented: AI evaluates; your experts make the final decision.
Listener QM automatically evaluates your voice, email, and chat interactions using your own evaluation criteria, then flags the exchanges that require human review for your evaluators. You decide the scope: a targeted sample, a campaign, or up to 100% of interactions when the situation warrants it.
Quality Monitoring Automatic or augmented? Most often, both.
Both modes use the same evaluation grids. They can be activated together or separately, depending on your volume, your risks, and your evaluators’ availability.
Auto-QM
AI evaluates interactions based on your criteria, without human intervention, within the scope you have defined.
- Presence of key phrases, absence of prohibited words
- Compliance with procedures and scripts on a large scale
- Changes in Sentiment at Key Moments During the Exchange
- More interactions evaluated, without additional resources
- The volume exceeds what your teams can handle
- Are you tracking compliance metrics, prohibited words, or key phrases?
- Your criteria are consistent and well-defined
Enhanced QM
AI performs preliminary analysis and prioritization. Your evaluators retain control over the grade and can focus their time on what matters.
- Identify the sections that require human attention
- Pre-fills the grid with context-based suggestions
- The evaluator reviews, corrects, and provides feedback: saving time with every evaluation
- Human precision and AI coverage for sensitive assessments
- Nuance Matters: Complex Relationships, Claims, and Complaints
- Your grids require just as much context as they do keywords
- Do you want to move toward greater automation, step by step?
Automation handles the volume, while humans focus on the cases that warrant their attention.
Manual, augmented or automated Quality Monitoring: how to choose?
The choice depends on your volumes, your risks and your evaluators’ time. Manual QM supports coaching, augmented QM keeps the evaluator on nuanced cases, and automated QM covers volume. All three use the same evaluation forms and, most often, are combined.
| Criterion | Manual QMThe evaluator listens and scores | Augmented QMAI + evaluator | Auto-QMFully automatic |
|---|---|---|---|
| Who proposes the score? | The evaluator or the supervisor, using your evaluation form. | AI pre-fills the evaluation form with suggestions in context. | AI, based on your criteria and with the same evaluation form. |
| Who validates? | The evaluator. The agent can self-assess and dispute the score. | The evaluator validates, corrects and comments: they keep control of the score. | No score-by-score validation. Combined with augmented QM, risky or ambiguous interactions are escalated to an evaluator. |
| How are interactions selected? | By sampling, according to your rules: team, agent, contact reason, channel, period, with quotas per evaluator. | AI identifies and prioritizes interactions that require human review. | The whole scope you define: a targeted sample, a thematic campaign, or up to 100%. |
| When to choose it? | Individual coaching, new hires, sensitive contact reasons, calibration between evaluators. | When nuance matters: complex relationships, insurance claims, complaints, context-rich evaluation forms. | When volume exceeds what your teams can listen to: compliance, forbidden words, key phrases, stable criteria. |
| Limits to keep in mind | Covers only a sample and depends on available listening time. | The volume handled remains tied to your evaluators’ time. | Each automated evaluation has a cost, and criteria must be stable and well defined. |
To go further: the Listener QM Quality Monitoring software for manual evaluation; on this page, augmented QM, Auto-QM and the question of volume and budget.
Four detection categories, tailored to your business rules.
Active from the moment of deployment, then tailored to your industry, your scripts, and your requirements. Each criterion in your checklists can be based on one or more of them.
Lexical field & prohibited words
Non-compliant terms, missing required phrases, and risky wording in all your conversations.
Atmosphere & Mood
Tension-filled moments, moments of satisfaction or dissatisfaction, and shifts in tone as the conversation progresses.
Legal Risk
Statements involving legal or regulatory risk: unauthorized commitments, off-script promises, or disclosure of confidential information.
Compliance & Procedures
Compliance with the required steps in your scripts: introduction, identification, processing, conclusion, and legal notices.
Your rubrics remain yours: customize your evaluation criteria. And for your voice bots and chatbots: evaluate AI agents using the same rubric.
Evaluate 100% of interactions? A legitimate question, not just a knee-jerk reaction.
Every automated assessment comes at a cost. Assessing everything is only useful if the results are used to make decisions. Listener QM helps you focus your resources where they make a difference and adjust them as you go.
Monitor each agent regularly
Quotas by agent, by team, or by time period, using a representative sample. The foundation of coaching.
Explore a Specific Topic
A reason, an offer, a risk, a time frame: the entire relevant process is evaluated until everything is clear.
Go up to 100%
When regulatory requirements or volume warrant it: compliance, regulated sales, sensitive activities.
To determine the right scale: How to set up an AI- Quality Monitoring .
Behind the scenes: conversation analysis.
Transcription, sentiment analysis, categorization, summarization, translation: Cross-Mining converts every audio or text conversation into data that automated and augmented QM can directly process, in more than 30 languages.
Speech to text
Automatic transcription of your recordings, tailored to your audio quality.
DiscoverSentiment Analysis
Tone, rhythm, and emotional intensity at every key moment of the conversation.
DiscoverCategorization
The key phrases and expressions that are detected feed into your QM themes.
DiscoverSummaries
A summary of each conversation, useful for debriefing and for the supervisor.
DiscoverTranslation
Audit your international interactions in the language of your choice.
DiscoverA grade doesn't help anyone improve. A debriefing does.
Each assessment can trigger an AI-assisted debriefing, a corrective action, or targeted training, directly from Listener QM. The supervisor doesn’t need to open any additional tools.
An LLM can read a conversation. A " Quality Monitoring" is a method.
AI models are advancing rapidly, and Listener QM relies on them. What they cannot do on their own is organize the assessment over time: what to assess, how much, using what criteria, and what to do with the results.
That’s the role of the software at Quality Monitoring Listener QM . Read: The business layer of Quality Monitoring in the age of LLMs.
Quality Monitoring Automated: Your Questions.
What is automatic Quality Monitoring , and how does it differ from manual monitoring?
Automated " Quality Monitoring " uses conversation analysis (including Speech Analytics) to systematically evaluate the quality of interactions on a large scale, whereas manual monitoring relies on sporadic listening. Augmented QM combines the two: AI prepares the data, and the evaluator makes the decision. Read the full answer
What are the benefits for a contact center?
More interactions reviewed, criteria applied consistently across the board, risks identified earlier, and reviewers refocused on cases that require their expertise. Read the full response
What results can you expect, and how do you measure ROI?
Return on investment is measured using several categories of metrics: perceived quality, compliance, the effectiveness of quality teams, and advisor development. We’ll work with you to tailor the details during a demo, based on your business. Read the full answer
How Can You Ensure the Success of an Automated Quality Monitoring ation Project?
Success depends as much on the approach as on the technology: clear guidelines, a limited scope at the outset, project sizes tailored to the budget, and teams involved from the very beginning. Read the full answer
Will AI replace quality assessors?
No. Automation should enhance your ability to listen, not diminish it. Evaluators spend less time searching for the right exchanges and more time analyzing, debriefing, and helping people improve. Read the full answer
What impact will this have on advisors and teams? How should we handle onboarding?
The introduction of automated grading can be a cause for concern. Transparency regarding the criteria, the ability to understand and challenge a grade, and a focus on coaching make all the difference. Read the full response
What compliance (GDPR) and ethical issues should be taken into account?
Personal data, anonymization, team communication, and transparency of criteria: these issues are addressed from the very start of the project. Read the full response
Can we also evaluate our chatbots and AI agents?
Yes, using the same rubric as your advisors. Listener QM then provides actionable feedback to the bot and its knowledge base. Learn more about AI agent evaluation
What are the upcoming trends?
Conversation analysis is evolving rapidly alongside advances in AI: evaluation of AI agents, assisted debriefing, and the selection of sovereign models. Read the full answer
Want to try it out?
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