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A-B: Glossary

What is After-Call Work (ACW)?

After-Call Work (ACW) is the time right after a customer interaction when an agent finishes tasks like updating records, writing notes, and scheduling follow-ups.

During this time, the agent isn’t available for new calls. Also called wrap-up time, ACW helps keep customer info accurate and service running smoothly.

To learn more about ACW, see our article “The Complete Guide to After Call Work: Definition & Top Tips to Reduce It.”

What Are Agent Analytics?

Agent analytics is the practice of evaluating the performance and behavior of both human and AI agents to identify opportunities for improvement.

It often combines customer interaction data from calls and chats, QA scores, and sentiment or speech analytics to create a continuous feedback loop for both individual agents and the overall operation.

How Agent Analytics Are Measured

They use different types of data to show how well customer service agents are doing their jobs. These scores are usually shown in dashboards or scorecards to track progress and compare results over time. Below are different groups of metrics for measuring agent performance:

Efficiency Metrics

These show how quickly and effectively agents handle customer issues. Common examples include Average Handle Time (AHT), First Contact Resolution (FCR), and how much of their time is spent helping customers versus waiting for the next task.

Workload and Volume Metrics

These track how many calls, chats, or emails agents handle, how often customers hang up before getting help, and how many open cases or tickets agents are working on.

Quality and Customer Experience Metrics

These focus on how well agents are helping customers. They include scores from customer surveys (like CSAT or NPS) and quality checks (either by managers or AI) that look for things like following scripts, being polite, and giving correct answers.

Business Results Metrics

These connect agent work to business goals, such as sales conversion rates, cost per customer interaction, and how well agents retain customers.

AI Agent Metrics

For virtual agents, key stats include how often they finish tasks without help, how accurate their answers are, how fast they respond, and how satisfied users are after interacting with them.

How to Use Agent Analytics

Define 5–10 key metrics (like CSAT, FCR, and AHT), assign weights based on business goals, and track scores in automated dashboards. Use these scorecards in one-on-one coaching sessions, reviewing performance with real call or chat examples and setting clear improvement goals. Team leads can roll up agent data to spot trends, guide group coaching, and reward top-performing teams while keeping the focus on growth rather than surveillance.

What is Automated Quality Management (AQM)

Automated Quality Management (AQM) uses AI, machine learning, and analytics to monitor, evaluate, and improve customer interactions in contact centers, replacing manual sampling with comprehensive analysis across channels like calls, chats, and emails.

How Success of AQM Is Measured

It’s measured through a mix of performance, cost, and customer impact metrics. Core KPIs include average QA scores, compliance rates, and first contact resolution (FCR), which reflect improvements in agent effectiveness and consistency.

ROI-focused indicators track cost savings from reduced manual QA, faster evaluation times, and agent performance gains after coaching. Customer satisfaction (CSAT), error rates, and system reliability also factor in, with most organizations targeting a 6–12 month payback period.

Training and Resources Needed for AQM

Training for AQM systems is role-based and includes short sessions for agents and supervisors, with more advanced training for admins. Teams need time to set up workflows, prepare data, and stay engaged during rollout, supported by vendor tools and internal resources.

Ongoing training, hands-on practice, and clear documentation help staff stay up to date and use the system effectively.

Tools and Platforms for Using AQM

See our article “5 Best Automated Quality Management Software” for a guide to AQM systems.

What is AutoQA?

AutoQA uses AI to automatically review and score customer interactions like calls, chats, and support tickets. It replaces manual reviews by analyzing all interactions for quality, helping teams improve agent performance and customer service faster and more consistently

Benefits of AutoQA Over Manual QA

AutoQA is faster and more efficient than manual QA because it automatically reviews all customer interactions, not just a small sample. It provides consistent, reliable scoring without human error and scales easily for large or growing support teams. AutoQA also saves money over time, delivers real-time feedback, and connects with other tools to improve coaching and quality without extra manual work.

How AutoQA Measures Agent and Customer Experience

AutoQA measures agent and customer experience by automatically reviewing every interaction across calls, chats, and tickets.

It evaluates agent performance using multiple metrics such as adherence to internal processes, compliance, empathy, accuracy, and resolution effectiveness, providing immediate scoring and feedback

On the customer experience side, it tracks customer sentiment (positive or negative), customer effort, repeat issues (like reopen rates), and emotions detected during interactions. It also measures first-contact resolution and overall customer satisfaction indicators like CSAT and Net Promoter Score (NPS) by aggregating data from conversations and surveys.

What is Average Handle Time (AHT)?

Average Handle Time (AHT) measures how long a customer interaction lasts, including talk time, hold time, and after-call work. It's used to track agent efficiency and is calculated by dividing the total time spent on calls and follow-up tasks by the number of calls handled.

The formula to calculate AHT is:

AHT = (Total Talk Time + Total Hold Time + Total After-Call Work Time) / Total Number of Calls

What’s Considered a Good or Standard AHT for a Contact Center?

A good or standard AHT for a contact center generally hovers around six minutes per call or interaction. This six-minute benchmark strikes a balance between operational efficiency and providing quality service, allowing agents enough time to thoroughly resolve customer issues without causing excessive delays. However, AHT varies a lot by industry due to differences in the complexity of customer interactions.

How to Optimize or Reduce AHT Without Sacrificing Customer Service Quality

To reduce AHT without hurting service quality, contact centers should train agents to handle calls clearly, quickly, and with empathy. They can use tools like CRM integrations, AI chatbots, and screen pops to make agents’ jobs easier and faster.

Streamlining call processes and using skill-based routing also cuts down on delays. Regularly reviewing performance helps teams spot problems early and keep improving without losing their personal touch.

What is Average Speed of Answer (ASA)?

Average Speed of Answer (ASA) measures how long, on average, a caller waits in line before speaking to a live agent. It doesn’t count time spent in automated menus.

ASA shows how quickly a team responds to customers and is calculated by dividing the total wait time of all answered calls by the number of those calls. Lower ASA means faster answers and better customer experience.

Why ASA is Important

ASA shows how fast customers reach a live agent, shaping their first impression. A shorter ASA means less waiting, leading to happier customers and better loyalty. It also helps contact centers manage agent workloads, avoid burnout, and control costs. Tracking ASA over time supports smarter planning and service improvements.

A high ASA means longer wait times, which can frustrate customers and lead to more people hanging up before getting help. This hurts satisfaction and loyalty. A low ASA connects callers to agents quickly, showing the company values their time.

It helps improve service levels, reduce call abandonment, and boost overall satisfaction. Managing ASA well shows strong operations and supports customer retention and business success.

How a Call Center Can Improve Its ASA

Call centers can reduce ASA by improving staffing, using better technology, and streamlining processes.

• Schedule enough agents based on call volume, train them well, and keep experienced staff to handle calls faster.
• Use smart call routing, advanced IVR, and callback options to shorten wait times. Give agents tools and dashboards to stay informed and work efficiently.
• Connect systems (like CRM and helpdesk) to cut down on time switching between tools. Use automation for simple tasks and focus agents on solving issues on the first call.
  

What is Automatic Speech Recognition (ASR)?

Automatic Speech Recognition (ASR) is a technology that turns spoken words into written text. It listens to speech, understands it using AI, and types it out in real time, even if there are different accents or background noise.

ASR is used in software like voice assistants, call centers, and transcription tools to help people talk to machines naturally.

To learn more about ASR, see our article “What Is Automatic Speech Recognition (ASR)?”

What is Agent Quality Score?

An agent quality score is a rating used in contact centers to measure how well a customer service agent does their job. It combines feedback from supervisors, customers, and sometimes the agent themselves to assess skills like communication, product knowledge, and following company rules. This score helps teams know where agents can improve and supports decisions about training, recognition, and promotions.

How Agent Quality Score is Calculated

The agent quality score is calculated using a weighted formula that combines different views of an agent’s performance. Typically, 50% comes from a manager’s review, 30% from customer feedback, and 20% from the agent’s self-assessment. Each part is scored, averaged, and combined to create a final score out of 100, giving a full picture of how the agent is doing and where they can improve.

Specific Skills and Behaviors That Are Evaluated in Agent Quality Score

The skills and behaviors in an agent quality score usually cover how clearly and professionally the agent communicates, how accurately they use product knowledge, and how well they solve problems.

Reviewers also look at whether the agent follows required scripts and guidelines, engages customers with empathy, and handles calls efficiently using metrics like FCR, AHT, and CSAT. Together, these qualitative and quantitative factors create a balanced view of both the technical and interpersonal sides of the agent’s performance.

How Agent Quality Scores Are Used to Drive Continuous Performance Improvement

Agent quality scores help managers spot where agents need training by highlighting patterns in low performance areas like communication, compliance, or product knowledge. By reviewing these scores, teams can create focused coaching plans, give helpful feedback, and track progress over time. They also help identify broader issues affecting multiple agents, leading to better training programs and process improvements that support both agent growth and customer service quality.

What is a Bot?

A customer service chatbot uses AI to chat with customers and help them with common questions or issues. It can work on websites or apps, give instant support any time of day, and send more complicated problems to a human when needed. These bots can also use customer data to make answers more helpful and personal.

Traditional Bots vs AI Virtual Agent

A traditional chatbot often follows rigid scripts and keyword rules, which often leads to frustrating, robotic conversations that break when users go off-script. Level AI’s Virtual Agent is built on natural language understanding and semantic intelligence, allowing it to understand intent, emotions, and context in a natural, human-like way.

It doesn’t just answer questions, it can take real action, like updating orders or CRM systems, and works across voice and chat with shared context. AI Virtual Agent also learns from every interaction to keep improving automatically, helping brands boost satisfaction while lowering costs and escalations.

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C: Glossary

What is Call Abandon Rate?

Call abandon rate shows the percentage of callers who hang up before speaking with a live agent. It's a sign that customers may be frustrated by long wait times or poor service. A high rate often points to staffing or process issues, while a low rate means faster, more effective support.

Factors That Affect Call Abandon Rate

Factors that influence call abandon rates in contact centers include long wait times (often caused by understaffing or high call volume), confusing phone menus (IVR), poor call routing, technical issues, and lack of clear wait time updates. Improving staffing, simplifying systems, and better managing customer expectations can help reduce abandonment and improve service.

How Call Centers Can Reduce Call Abandon Rates

Call centers can lower abandon rates by using tools and strategies that reduce wait times and improve the caller experience. This includes offering callbacks, using smarter call routing (like skill-based routing or advanced IVR), and giving estimated wait times or queue positions. They can also spread agent workload across channels like chat or SMS, use AI tools to handle common questions, and keep callers engaged while on hold. Regularly analyzing call data helps identify and fix problem areas, leading to better service and fewer hang-ups.

What Mistakes Do Call Centers Make When Measuring Abandon Rate?

Call centers often misinterpret abandon rate by counting all hang-ups, including accidental ones, which inflates the number. They may also look at this metric in isolation, without considering related KPIs like wait time or customer satisfaction.

Common errors include using the wrong formula, ignoring where in the call journey abandonment happens, and overlooking issues like IVR frustration or poor staffing. To get meaningful insights, it's important to measure abandon rate accurately, connect it to other performance data, and fix the root causes driving callers to hang up.

What is a Call Center Audit?

A call center audit is a regular checkup of how well a call center follows its rules, handles customer issues, and meets quality standards. It helps improve agent performance, customer satisfaction, and overall efficiency by reviewing calls, procedures, and results.

Audits may be done by internal teams or outside experts to find areas that need better training or process improvements.

What Are the Metrics for Evaluating Call Center Performance?

Key metrics in a call center audit measure efficiency, service quality, and customer satisfaction to highlight where improvements are needed. These include:

• Average Handle Time (AHT): How long an interaction takes, including talk time and after‑call work.
• First Call Resolution (FCR): The share of issues solved on the first attempt.
• Customer Satisfaction (CSAT): Direct feedback on service quality.
• Call Abandonment Rate: How often callers hang up before reaching an agent.
• Service Level: How quickly calls are answered compared to a set target.
• Quality Scores: Ratings based on call reviews that check professionalism, compliance, and problem‑solving.
• Schedule Adherence: Whether agents follow their assigned schedules.
• Average Speed of Answer (ASA): How long callers wait before an agent picks up.

How Call Center Audits Help Improve Customer Experience

Call center audits help improve customer experience by identifying what causes bad interactions and fixing those issues through better processes and agent coaching. By reviewing calls and spotting problems like poor communication or long wait times, audits lead to faster resolutions, more consistent service, and higher satisfaction scores like CSAT and NPS.

They also ensure agents follow rules and provide reliable service, making each customer interaction smoother and more effective.

The Role of Call Monitoring and Recorded Calls Analysis in Audits

Call monitoring and recorded call analysis are key parts of call center audits because they show how well agents follow scripts, solve problems, and meet company rules. By reviewing these calls, teams can measure performance, find training needs, and spot issues in workflows or compliance. Automated tools help review more calls quickly, making it easier to give feedback and improve both service quality and customer satisfaction.

What is Call Center Coaching?

Call center coaching is an ongoing process where supervisors or coaches give agents personalized feedback to help them improve their skills and customer interactions. It focuses on behaviors like listening, empathy, and problem-solving, and happens through regular reviews or training sessions.

The goal is to help agents grow and deliver better service.

How Often Call Center Coaching Sessions Should Be Conducted

Call center coaching should match each agent’s experience and performance level. One approach that’s commonly used is:

• New agents (0–90 days): daily check-ins at first, then 2–3 times a week to build skills and confidence.
• Developing agents (3–12 months): weekly short sessions with monthly progress reviews.
• Experienced agents (1+ years): Coaching every two weeks, plus quarterly development sessions.
• High performers: Monthly sessions focused on leadership and mentoring.
• Struggling agents: Daily coaching until improvement, then gradually return to a standard schedule.

This mix of regular coaching and real-time support helps agents grow and deliver better service.

To learn more about call center coaching, see our article “6 Best Practices for Call Center Coaching.”

What is Call Center Efficiency?

Call center efficiency is how well a call center uses its agents, time, and tools to handle customer issues quickly and with low cost. It focuses on solving problems fast, keeping wait times short, and making the most of available staff. Common ways to measure efficiency include First Call Resolution (FCR), Average Handle Time (AHT), onhold time, and how many calls are answered within a set time.

To learn more about call center efficiency, see our article “7 Ways to Boost Call Center Efficiency in 2025.”

What is Call Center Shrinkage?

Call center shrinkage is the percentage of time agents are scheduled to work but aren’t available to take customer calls. This includes things like breaks, meetings, sick days, or training. It helps managers understand how many agents are truly available and affects how they plan staffing.

To learn more about call center shrinkage, see our article “What is Call Center Shrinkage?”

What is Call Disposition?

Call disposition is part of after-call work and is a tag or label that describes what happened on a phone call, such as whether it was answered, went to voicemail, or needs follow-up.

Agents choose it from a preset list after each call to help track outcomes, prioritize next steps, and improve call center performance. Software like Level AI that use AI speech analytics choose dispositioning labels for agents automatically.

Why Call Disposition is Important

Call disposition makes it easier for agents to log the outcome of each call quickly and clearly, helping improve customer service and reduce manual work. It also supports better follow-up, faster call routing, and more accurate records.

For managers, call disposition data reveals trends in agent performance and customer issues, guiding better decisions about training, staffing, and policies. It also helps with compliance, reporting, and cutting costs by reducing repeat calls and unnecessary follow-ups.

How Call Dispositions Are Used

Call dispositions help agents quickly label the outcome of each call, like “Voicemail left,” “Interested,” or “Sale closed,” so teams can manage follow-ups, track progress, and report on results. Agents usually pick from a preset list after the call, but some systems assign dispositions automatically using dialer feedback or AI.

In sales, they show where leads are in the funnel and help plan next steps. In support, they reduce note-taking, track outcomes, and support compliance rules like Do Not Call lists. Different industries use them for tasks like appointment scheduling (healthcare), order updates (e-commerce), or client tracking (real estate).

Dispositions also link with CRM systems to trigger follow-ups, update records, and improve service speed. By organizing call results, they support better planning, faster responses, and stronger customer relationships.

Challenges and Best Practices

Managing call dispositions well is important for improving call center efficiency and customer experience. Common challenges include vague labels, missed follow-ups, manual entry mistakes, outdated categories, and lack of context for future agents.

To address these, use clear, outcome-based labels and automate follow-up steps when possible. Ask agents to add short notes explaining the disposition, and keep lists simple and up to date. Train agents well, align categories across teams, and connect dispositions to your CRM to keep everything in sync. Reviewing and refining the system regularly ensures it stays useful and accurate.

What is Contact Center as a Service (CCSaaS)?

Contact Center as a Service (CCaaS) is cloud-based software that helps businesses handle customer service across phone, chat, email, and other channels. It’s managed by a third-party provider, so companies don’t need to own or maintain the hardware.

CCaaS makes it easier to scale, support remote teams, and use tools like AI and analytics to improve service.

What Functionalities and Features Are Typically Included in CCSaaS?

A basic Contact Center as a Service (CCaaS) package usually includes the following key features:

• Call Routing & IVR: Automatically directs calls or messages to the right agent using smart routing and menu options.
• Multichannel Support: Lets agents handle calls, chats, emails, texts, and social media in one place.
• CRM Integration: Connects to customer databases so agents can see customer history and offer personalized help.
• AI Tools: Includes chatbots for self-service, real-time agent assist, and analytics to improve service.
• Workforce Management: Helps schedule and track agent productivity.
• Analytics & Reporting: Gives managers real-time data to improve team performance.
• Unified Workspace: Combines all tools in one screen for faster, easier support.

Other common tools include call queues, voicemail, monitoring, and remote team support.

What Are Typical Use Cases and Business Benefits Compared to Traditional Contact Centers?

CCaaS helps businesses manage customer service across voice, chat, email, SMS, and more. Common uses include AI-enhanced calling, virtual agents for self-service, voice recognition IVRs for faster call routing, and tools to scale support during busy times. It also enables automation to summarize calls and improve agent efficiency, along with real-time analytics for better decision-making.

CCaaS reduces costs by removing the need for in-house systems and large support teams. It’s faster to set up, easier to update, and supports remote teams. Businesses gain better customer service through AI, quicker response times, and the flexibility to handle more calls without big upfront costs.

For example, tourism agencies use CCaaS to manage travel surges, telecom providers scale with growing demand, and insurance firms speed up claims through automation.

What is Conversational AI?

Conversational AI is software that understands and responds to human language in a natural way, using text or voice. It combines tools like natural language processing and machine learning to recognize intent and carry on useful, two-way conversations.

Unlike basic chatbots, it can learn and improve over time to feel more like talking to a real person.

How Conversational AI Works

Conversational AI works through a system of connected parts that help it understand and respond to human language. It starts with a text or voice input, which the AI analyzes using natural language processing to identify intent and key information. A dialogue manager then guides the conversation and uses AI models to generate helpful, natural replies.

The system can connect to tools like CRMs or databases to pull in personalized info and complete tasks. It also learns from past conversations to improve over time, using feedback and safeguards to stay accurate and responsible. This setup helps it manage longer conversations and adapt to different user needs across channels.

Challenges and Limitations Users Might Face With Conversational AI Systems

Users of conversational AI often run into challenges like:

• Robotic responses that lack the natural flow and emotion of human conversation.
• Poor memory and context handling, making the AI repeat itself or give off-topic answers.
• Misunderstood questions, especially when users are vague or ask something complex.
• Limited emotional awareness, so the AI may miss signs of frustration or urgency.
• Shallow knowledge, which makes it hard for bots to handle detailed or unusual requests.
• Struggles with language and culture, including slang, dialects, or cultural norms.
• Tough system integration, where connecting to tools like CRMs causes issues.
• Slowdowns during heavy use, leading to lower-quality replies.
• Bias in training data, which can lead to unfair or inconsistent responses.
• Privacy risks, as keeping user data safe and following regulations can be difficult.

Level AI’s Virtual Agent solves common chatbot problems by holding natural conversations, remembering context, handling complex tasks, working across systems, and keeping user data safe, all while improving the customer experience.

What is Conversational AI Design?

Conversational AI design is the process of planning how people talk with AI systems like chatbots or voice assistants. It combines language, design, and tech to make conversations clear, helpful, and natural, helping users reach their goals with less confusion.

Good design focuses on smooth dialogue, handling mistakes, and knowing when to hand support cases off to a human.

How to Approach Designing a Conversational Flow That Meets Both User Needs and Business Goals

To build a conversation flow that’s clear, helpful, and aligned with user and business goals, follow these steps:

• Review past chats or user data to spot common questions and pain points. Focus on high-impact interactions that matter most.
• Map out the full conversation, including main goals, backups, and alternate paths. Use clear decision points and simple choices to keep users from feeling overwhelmed.
• Keep it short: aim for under five questions in a row. Use buttons or quick replies when possible to guide users smoothly.
• Track user context with session variables and entity recognition so the flow adapts to shifts or interruptions naturally.
• Make transitions between stages clear, especially when moving from collecting info to taking action or escalating to a person.
• Plan for unclear inputs with helpful suggestions instead of ending the chat suddenly.
• Test often with real users. Watch for confusion, drop-offs, or wrong responses, and improve the flow based on feedback.
• Use your brand voice and look for signs of frustration, adding empathy and human help when needed.
• Use AI tools to speed up design and improve flows using past chat data.

Level AI’s agentic Virtual Agent supports these best practices through its fast and intuitive setup. During configuration, teams define goals, connect data sources, and build action flows using simple prompts, with no complex engineering needed.

AI Virtual Agent then uses advanced AI to understand user intent, manage context, adjust tone, and continuously improve through full interaction monitoring.

Key Metrics for Measuring Conversational AI Performance

To track how well a conversational AI system works and how users feel about it, focus on:

• Resolution Rate and Containment Rate: show how often the AI solves issues without human help.
• Intent Accuracy and Goal Completion Rate (GCR): measure how well the AI understands users and helps them reach their goals.
• First Contact Resolution (FCR) and Average Handling Time (AHT): Reflect efficiency and speed of issue resolution.
• CSAT and NPS: Indicate user satisfaction and willingness to recommend.
• Escalation Rate: Highlights how often the AI passes conversations to humans, revealing its limits.
• Sentiment Scores: Track emotional tone to assess empathy and experience quality.
• Engagement and Cost Metrics: Include session duration, bounce rate, and cost per interaction to evaluate usage and ROI.

Together, these metrics help assess performance, user experience, and business value, guiding ongoing improvements.

What is Customer Satisfaction Score (CSAT)?

CSAT measures how happy customers are with a product, service, or support interaction. It’s based on survey responses, usually asking customers to rate their experience from 1 to 5.

The score is the percentage of people who give a high rating (like 4 or 5), showing how well a company is meeting customer expectations.

How Is CSAT Measured and Calculated?

CSAT is measured by asking customers to rate their satisfaction, usually on a 1 to 5 scale, where 1 means very unsatisfied and 5 means very satisfied.

The CSAT calculation focuses on the proportion of customers who respond with the highest satisfaction levels, usually ratings of 4 or 5 on a 5-point scale. To calculate the CSAT score, the number of satisfied customers (those who gave a 4 or 5) is divided by the total number of survey responses, then multiplied by 100 to express it as a percentage:

CSAT = (Number of satisfied customers (4 or 5)Total number of responses) 100

For example, if 150 out of 200 respondents rate their satisfaction as 4 or 5, the CSAT score would be 75%, indicating that 75% of customers are satisfied. The CSAT score can also be calculated by averaging all numeric scores when a more granular average is preferred.

Typical Examples of CSAT Survey Questions

CSAT surveys usually ask customers to rate their satisfaction with a product, service, or overall experience. Common questions include:

• How satisfied are you with our product or service?
• How would you rate your overall experience?
• How likely are you to recommend us?
• Did our service meet your expectations?
• What can we do better?

To learn about key metrics to track for improving CSAT scores, see our article “3 Key Metrics QA Managers Should Track to Improve CSAT Scores.”

What is Call Center Compliance?

Call center compliance means following laws, regulations, and company rules to handle customer calls and data safely and legally. It includes protecting privacy, getting proper consent, being honest, and keeping good records.

The goal is to avoid legal trouble, build trust, and treat customers fairly.

Best Practices for Protecting Customer Data and Ensuring Privacy

Steps for keeping customer data safe and building trust include:

• Encrypting data during storage and transmission to prevent unauthorized access.
• Limiting access through role-based controls, and updating permissions when roles change.
• Masking sensitive info (like credit card numbers) and using secure tools like IVR for payments.
• Running regular backups, audits, and security checks to stay compliant with laws like GDPR and PCI DSS.
• Using strong cyber and physical security, including passwords, firewalls, and staff training.
• Setting clear rules for how long data is kept and securely deleting it when no longer needed.
• Promoting a culture of data safety by monitoring activity, encouraging reporting, and following strict conduct policies.

Technologies and Processes That Help Monitor and Enforce Compliance

Tools like AI-powered conversational intelligence software, real-time agent assist, and modern call center recording solutions help call centers stay compliant during every customer interaction. These systems use Natural Language Processing (NLP) to automatically analyze all calls, flag compliance risks (like missing disclosures), and give agents live prompts and checklists to stay on track during calls.

They also create automated QA scorecards for fair evaluations, help managers spot patterns of non-compliance, and send real-time alerts to reduce legal risks. By handling routine reviews, these tools free up QA teams for more strategic work.

Key technologies include:

• AI call analysis using NLP
• Real-time prompts and dynamic checklists
• Automated QA and compliance dashboards
• Trend detection across agents and teams
• Instant alerts for risky behavior

Common Compliance Challenges Faced by Call Centers

Call centers face several compliance challenges, including navigating complex and changing laws like TCPA and GDPR, getting proper customer consent, protecting data privacy, and managing outdated or disconnected compliance systems. Other issues include poor agent training, tech integration gaps, and limited monitoring.

To address these, contact centers can use centralized compliance platforms, update agent training regularly, automate key processes, and run frequent audits. They should also keep customer contact lists clean, consult legal experts, and strengthen data security.

What is Customer Effort Score (CES)?

Customer Effort Score (CES) measures how easy it is for a customer to solve a problem or complete a task with a company.

It’s usually based on a survey question like “How easy was it to resolve your issue?” and helps identify where the customer experience feels frustrating or complicated. Companies use CES to reduce friction and improve loyalty.

How CES is Calculated

Customer Effort Score is calculated by averaging the numeric responses from customers to a survey question that measures how much effort they had to expend to accomplish a task with a company. The standard formula is:

CES = (Sum of all customer effort scores) ÷ (Total number of respondents).

For example, if you collect scores from customers on a scale of 1 to 7 (where 1 is very difficult and 7 is very easy), you add all the scores and divide by the number of responses to get an average CES.

This average reflects how effortless customers perceive their interactions. Some companies use a 5-point or 10-point scale instead. Alternatively, if the survey uses emoticons or agree/disagree statements, CES can be calculated by subtracting the percentage of negative responses from the percentage of positive responses, ignoring neutral ones.

How CES Can Be Used to Identify Friction Points in Customer Interactions

CES helps companies find where customers struggle by measuring how much effort it takes to complete tasks like purchases or support requests. Low CES scores at certain points, like checkout or onboarding, highlight friction that causes frustration.

By tracking CES across the customer journey, businesses can spot “effort hotspots” and make changes like simplifying steps, speeding up response times, or improving tools for support teams. Regularly monitoring CES helps reduce effort, improve satisfaction, and lower the chances of customer churn.

Common Reasons Given by Customers for Low CES Scores

Customers often give low effort score ratings when interactions are frustrating or time-consuming. Common issues include confusing processes, long wait times, unhelpful support, poor self-service options, and inconsistent information.

Returns or post-purchase tasks that require too many steps, or having to repeat issues across channels, also raise effort. These problems increase customer frustration and hurt satisfaction and loyalty.

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D-O: Glossary

What is Deflection Rate?

Deflection Rate measures how often customers solve their own issues using self-service tools like FAQs, chatbots, or help articles, without needing a human agent.

It shows how well these tools reduce support volume and speed up resolutions. A high deflection rate can lower costs and boost efficiency, as long as customers still get accurate and helpful answers.

How Deflection Rate is Calculated

Deflection rate is obtained by dividing the number of customer issues resolved through self-service channels by the total number of customer inquiries or support requests, then multiplying the result by 100 to get a percentage. The formula is:

Deflection Rate (%) = (Self-Service Resolutions / Total Customer Inquiries) × 100

The data needed includes:

• Total number of customer inquiries or support requests within a defined period.
• The number of those inquiries successfully resolved through self-service tools such as FAQs, chatbots, IVR systems, or knowledge bases without needing to escalate to live agents.

For example, if a contact center receives 10,000 inquiries in a month and 3,500 are resolved through self-service, the deflection rate would be (3,500 / 10,000) × 100 = 35%. This means 35% of issues were deflected from live agents, reducing agent workload and operational cost.

Why Deflection Rate is Important

Deflection rate matters because it helps support teams handle more customers without needing more staff. By letting customers solve simple issues on their own (through FAQs, chatbots, or help articles) companies save on costs and improve agent productivity.

It also leads to faster answers for customers and helps businesses scale support while keeping service quality high.

How to Improve Deflection Rate

To improve deflection rate, customer service teams should focus on better self-service tools and smart use of AI. Effective strategies include:

• Using AI chatbots trained on real customer questions to give quick, accurate answers.
• Keeping help articles, FAQs, and guides clear, organized, and up to date.
• Sending proactive updates through email, text, or app notifications to solve issues before customers reach out.
• Building customer forums where users can help each other.
• Training agents to guide customers to self-service options during calls or chats.
• Equipping agents with tools that suggest self-service links in real time.
• Regularly reviewing customer feedback and self-help content to spot gaps and improve resources.

These steps make support faster and easier for customers while reducing agent workload and costs.

Common Pitfalls or Challenges in Implementing Deflection Strategies

Common issues with deflection include outdated or hard-to-use self-service content, overly complex phone menus, and limited chatbot capabilities that leave questions unanswered.

When companies wait for customers to reach out instead of being proactive, or when support channels don’t share information, it can lead to frustration and repeat contacts.

It's also important to balance deflection with solving the actual problem. Just avoiding a live agent doesn’t mean the issue was resolved. Keeping tools updated, easy to use, and integrated across channels is key to making deflection helpful rather than harmful.

What is Escalation Rate?

Escalation rate is the percentage of support requests that need to be passed from front-line agents to higher-level teams. It shows how well first-level support handles issues and can highlight problems like complex products or lack of training. A high escalation rate often signals areas where support processes need improvement.

How to Calculate Escalation Rate

It’s the percentage of support tickets or customer issues that are escalated from the first level of support to higher-tier support. The formula is:

Escalation Rate = (Number of Escalated TicketsTotal Number of Support Tickets) x 100

For example, if out of 1,000 total tickets, 50 are escalated to higher support tiers, the escalation rate would be (50 / 1000) × 100 = 5%. This calculation helps organizations monitor how often frontline support cannot resolve issues independently and must escalate them for further assistance, providing insights into agent training, product complexity, and process effectiveness.

Causes of High Escalation Rates in Customer Support

High escalation rates in customer support often come from a few main issues:

• Complex or faulty products that frontline agents can't easily resolve
• Poor agent training and unclear escalation processes
• Weak self-service tools like FAQs that leave agents without answers
• Slow or unclear communication during escalations
• Not enough staff at higher support levels, creating delays
• Bots or automation that can’t handle complex cases
• Ongoing problems like confusing policies or product bugs

Reducing escalations means improving training, setting clear processes, strengthening self-service tools, fixing root issues, and keeping customers informed throughout the support process.

How to Build an Escalation Process That Minimizes Delays and Maintains Customer Communication

To create an effective escalation process that avoids delays and keeps customers informed:

• Set up a clear framework that defines when to escalate, who handles each level, and how quickly issues should be resolved. Let frontline staff escalate without waiting for extra approval to speed things up.
• Use clear, empathetic communication across channels like phone, email, and chat, and give customers real-time updates on their cases. Set response time goals (SLAs), and use automation to flag and route overdue cases.
• Match escalation paths to issue type and urgency, and use AI to detect complex problems early and send them to the right team. Train staff to spot frustration and respond with care, and always follow up to confirm the issue is fully resolved.

What is First Call Resolution (FCR)?

First Call Resolution (FCR) is a measure of how often customer issues are fully resolved during the first interaction, without needing a follow-up.

It shows how effectively a support team can solve problems right away, which helps improve customer satisfaction and reduce repeat contacts. FCR applies across all channels, like calls, chat, or email, and is a key indicator of service quality.

How is FCR Calculated?

FCR is calculated as follows:

FCR = (Number of issues resolved on first contactTotal number of customer contacts) x 100

This formula gives the percentage of customer issues that were fully resolved during the initial interaction without the need for follow-up contacts. For example, if a contact center resolved 800 cases out of 1,000 on the first contact, the FCR rate would be (800 1,000) x 100 = 80%.

FCR can be measured across channels and time periods, and some companies use customer surveys or time-based checks to confirm if an issue was truly resolved. Tracking FCR helps evaluate how effective and satisfying customer support is.

What Is Considered a Good FCR Rate?

A good FCR rate typically falls between 70% and 79%, meaning most customer issues are resolved on the first contact. An FCR rate of 80% or higher is considered excellent, while anything below 70% usually signals a need for improvement.

These benchmarks help contact centers measure how effectively they’re resolving problems and supporting customers.

How Companies Can Improve Their FCR Rate

Companies can improve FCR by training agents well, giving them the tools and authority to solve problems, and using intelligent routing to match customers with the right experts.

Real-time AI support, access to customer history, and self-service tools help agents work faster and more effectively. Regularly reviewing FCR data and encouraging teamwork also helps identify ways to improve service and fix issues faster.

What is First Response Time (FRT)?

First Response Time (FRT) is the time between when a customer reaches out and when they get a real reply from a support agent, not just an automated message.

It’s a key way to measure how quickly a support team begins helping customers and is often tied to customer satisfaction and service expectations. Lower FRT typically means customers feel heard faster, which improves their overall experience.

How is First Response Time (FRT) Calculated?

FRT is calculated by measuring the time elapsed between when a customer submits an inquiry or request and when a customer service agent provides the first substantive response. The formula is:

FRT = Time of First Response – Time of Initial Inquiry

For example, if a customer sends a message at 9:00 AM and an agent replies at 10:00 AM, the FRT is 1 hour.

How to Improve FRT

To reduce FRT, support teams can use automation to route inquiries faster, offer self-service tools to deflect simple questions, and set up tiered support to handle issues more efficiently. Training agents, offering multi-channel support, and using chatbots for quick replies also help speed up responses. Regularly tracking performance and acting on customer feedback ensures continuous improvement.

How Off-Hours Support Models Affect Calculation for FRT

FRT is affected by whether a company uses off-hours or follow-the-sun support. In off-hours models, FRT only counts time during business hours, so nights and weekends are excluded. In follow-the-sun support, teams across time zones provide 24/7 help, so FRT includes all hours and is expected to be faster, improving global response times and customer satisfaction.

What is Inferred Customer Satisfaction (iCSAT)?

Inferred Customer Satisfaction (iCSAT) is a proprietary, holistic score used by Level AI to measure customer satisfaction by analyzing conversations using generative AI, combining three core signals:

• Customer sentiment (emotional tone)
• Customer effort to resolve the issue
• Issue resolution status

This score is measured on a scale from 1 (very dissatisfied) to 5 (very satisfied). By blending these elements, iCSAT provides a holistic and accurate view of the customer's true experience, overcoming the limitations and biases often found in traditional post-interaction surveys

How Inferred Customer Satisfaction (iCSAT) Is Calculated

The iCSAT score is derived by combining and blending three core signals into a comprehensive measure of the customer experience:

1. Sentiment Score (emotional tone): this detects the emotional tone and shifts in customer feelings throughout a conversation.
2. Customer Effort Score (CES): This measures the amount of effort customers put into resolving their issues. Factors considered include repetitions, hold times, and the number of transfers (a lower effort score indicates a better experience).
3. Resolution Score (issue status): This evaluates whether the customer's issue was successfully addressed, either fully or partially, by analyzing conversation outcomes and final interaction states.

Does iCSAT Analyze All Interactions?

iCSAT is based on the analysis of 100% of customer interactions, as it collects and analyzes unfiltered feedback directly from conversations (calls, chats, emails, etc.) rather than relying on a small, potentially biased subset of responses from traditional post-interaction surveys.

How iCSAT Differs From Traditional CSAT Surveys

Unlike traditional surveys that rely on a small number of post-interaction responses, iCSAT captures real-time sentiment, effort, and resolution signals from every interaction, giving a broader, unbiased view of customer experience.

This approach helps teams uncover root causes of dissatisfaction and track trends without needing to ask customers directly.

How iCSAT Can Be Used to Identify Root Causes of Dissatisfaction

iCSAT breaks each customer interaction into three signals, Sentiment Score, Customer Effort Score, and Resolution Score, and blends them using AI. Because it’s a composite score, teams can look at each component to understand exactly why a customer was unhappy instead of relying on a single survey metric.

A low Sentiment Score points to emotional issues, such as frustration or feeling unheard. A low score on Customer Effort shows the customer had to work too hard, often due to transfers, long hold times, or confusing processes. A low Resolution Score means the problem wasn’t fully solved, which may signal knowledge gaps or system limitations.

By reviewing these signals, teams can pinpoint coaching needs, find broken processes, and uncover unmet customer needs. iCSAT also highlights specific conversations for review, helping leaders understand common situations that reduce satisfaction and take targeted action.

What is Intent Detection?

Intent detection is the process of figuring out what a person wants to do based on what they type or say. It helps systems like chatbots and virtual assistants understand the meaning behind words using AI and natural language processing, so they can respond in the right way. This makes conversations faster, clearer, and more helpful.

How Intent Detection Works

Intent detection analyzes what someone types or says to figure out what they want. First, systems collect examples of questions and label them by intent. Then, the text is cleaned and key information is pulled out. Using machine learning, a model is trained to recognize different intents. When a new message comes in, the model predicts what the user wants and triggers the right response. The system keeps improving by learning from new data over time.

Level AI uses Natural Language Understanding (NLU) and semantic intelligence to detect intent by interpreting the meaning and context behind customer and agent messages, beyond simple keyword matching.

Its Scenario Engine classifies these intents as “scenarios” (e.g., billing issue, refund request) and tags them in conversations, enabling real-time assistance, automatic categorization, and searchable insights. Users can customize these scenarios with their own examples, helping the system learn and adapt to specific business needs.

Why Intent Detection is Important for Improving Customer Experience and Automation

Intent detection helps automated systems quickly understand what customers need, leading to faster, more accurate, and personalized responses. This reduces wait times, cuts down on mistakes, and lets human agents handle more complex issues.

It also improves customer engagement, supports better decisions with data, and helps businesses save money while keeping customers happy and loyal.

Common Challenges in Intent Detection

Intent detection can struggle with misspelled or vague messages, or when users ask for multiple things at once. It's also hard for systems to keep up when people change topics mid-conversation.

As businesses grow, adding more intents and users can slow down performance and lower accuracy. Other issues include overlapping or unclear intents, not enough training data, and trouble remembering earlier parts of long chats.

Solving these problems takes strong design, real-world data, context-aware models, and regular updates.

What is a Knowledge Graph

A knowledge graph is a type of knowledge base that organizes information by linking related things, like people, places, or ideas, into a web of connected data.

Each item is a point (or "node"), and the lines between them show how they’re related. This structure makes it easier for both people and computers to understand, search, and learn from the information.

Challenges That Knowledge Graph Are Intended to Solve

A knowledge graph is designed to handle complex and changing relationships between data points better than traditional databases. Instead of rigid tables, it uses connected nodes and relationships, making it easier to model real-world situations and adapt as data evolves. This makes knowledge graphs ideal for tasks like semantic search, recommendations, fraud detection, and data integration.

Compared to traditional databases, knowledge graphs:

• Handle multi-step relationships without slow join operations
• Combine data from many sources into one connected view
• Support reasoning to uncover new insights
• Adapt to new data types and relationships easily
• Improve AI and search by understanding how things are connected

In short, knowledge graphs are a better fit when data is relationship-heavy, constantly changing, or needs deep understanding.

Data Sources and Integration in the Knowledge Graph

Knowledge graphs pull data from many different sources, like databases, spreadsheets, APIs, and even text or social media, and turn it into a single connected structure. They do this by cleaning the data, matching similar items, resolving conflicts, and using a shared framework (ontology) to define entities and their relationships.

This approach creates a unified view of information that supports complex searches, easy updates, and flexible expansion as new data comes in

What is Natural Language Processing (NLP)?

Natural Language Processing (NLP) is a type of artificial intelligence that helps computers understand and work with human language.

It allows machines to do things like recognize speech, analyze text, translate languages, and respond to questions. NLP makes it easier for people to interact with computers using everyday language.

How NLP Works

NLP in customer service automation helps systems understand and respond to text or voice messages from customers. It cleans up input, figures out what the customer wants, pulls out key details (like product names), and detects emotions. Using this info, it can respond automatically, help agents with suggestions, or escalate issues to humans when needed.

This technology powers chatbots, voice assistants, and ticket systems, allowing 24/7 support for common questions. It also helps predict problems like customer churn and can improve over time through learning. As a result, companies respond faster, reduce agent workload, and improve customer satisfaction, often with strong returns on investment.

How NLP Handles Language Complexity

NLP uses advanced techniques to handle the complexity of human language. These include breaking text into smaller parts, fixing typos, analyzing grammar and meaning, and using context to understand intent.

Machine learning helps the system learn from data, adapt to new terms, and improve accuracy over time. While these methods handle many language challenges, understanding deep context and rare expressions is still being improved.

What is Natural Language Understanding (NLU)?

NLU is a type of AI that helps customer service tools like chatbots understand what people really mean when they speak or type, even if it’s informal or unclear.

It identifies intent, context, and emotion to deliver more accurate, helpful, and natural responses, reducing the need for human help and improving customer experience.

How does NLU Compare with NLP?

NLP turns human language into a format machines can read by breaking down sentences, identifying keywords, and translating text. NLU is a part of NLP that focuses on grasping what a person actually means, including their intent, emotion, and context.

While NLP handles the structure of language, NLU helps chatbots and virtual agents understand informal or vague language and respond naturally. Together, they allow automated systems to deliver faster, more accurate, and more human-like customer service.

To learn more about natural language understanding, see our article “What Is Natural Language Understanding (NLU)?”

What is On-Hold Time?

On-hold time is the amount of time a caller spends waiting when an agent puts them on hold during a call.

This usually happens when the agent needs to find information or ask someone else for help. It's an important metric because long holds can lead to lower customer satisfaction.

How Long Is an Acceptable On-Hold Time in Customer Service?

A good on-hold time in customer service is usually under two minutes. Longer holds often frustrate customers, and many hang up if the wait goes past 2 minutes and 30 seconds. To avoid this, agents should check in every few minutes or offer a callback if more time is needed.

How On-Hold Time Affects Customer Experience

Long hold times can frustrate customers and hurt their experience, often making them feel ignored and less satisfied. This can lead to lower loyalty, more abandoned calls, and damage to the brand’s reputation.

Shorter hold times show efficient service, improve satisfaction, and boost loyalty. Using strategies like agent updates, callbacks, or other support channels can help reduce hold times and improve both customer experience and efficiency.

Strategies to Reduce On-Hold Times

To reduce on-hold time in customer service, companies can give agents faster access to information, improve call routing, and offer callbacks instead of making customers wait.

Using tools like chatbots, staffing based on call volume, and training agents well helps also. Monitoring performance and keeping customers informed during busy times can further improve wait times and overall satisfaction.

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Q-V: Glossary

What is Quality Assurance?

Quality assurance (QA) is the process of making sure a product or service meets quality standards by checking and improving how it's made or delivered.

It focuses on preventing problems before they happen and helps ensure consistent, reliable results that meet customer expectations.

Metrics Used to Measure and Analyze Quality Assurance Performance

In customer service, quality assurance (QA) performance is measured using key metrics like:

• Customer Satisfaction (CSAT): How happy customers are, based on post-interaction survey scores.
• First Response Time (FRT): How quickly the team replies to a customer’s first message.
• Resolution Rate: The percentage of issues fixed on the first try, without needing follow-up.
• Customer Effort Score (CES): How easy it is for customers to get help.
• QA Score: An internal rating of how well agents follow rules, communicate, and provide accurate info.
• Average Handle Time (AHT): How long it takes to finish a customer interaction.

QA tools include scorecards, surveys, call monitoring, and analytics. These metrics help teams spot trends, fix issues, and improve the customer experience over time.

How QA Contributes to Customer Satisfaction and Brand Trust

Quality assurance ensures that products and services are consistent and meet expectations, preventing problems, reducing complaints, and improving how agents perform through regular checks and training.

By fixing issues early and improving service quality, QA creates better customer experiences, leading to loyalty and a stronger brand reputation.

What is Quality Management?

Quality management is the process of setting standards, measuring performance, and improving how support teams serve customers.

It helps businesses deliver better experiences by tracking feedback, training agents, and using data to fix problems and raise service quality over time. The goal is to keep customers happy and loyal by consistently meeting their expectations.

Critical Components of Quality Management in Customer Support

Good quality management includes setting clear service standards, using structured processes, and training agents regularly. It also relies on tools like call analytics and customer feedback to track performance and guide coaching.

A strong quality program focuses on continuous improvement, customer needs, leadership support, and teamwork across departments to deliver consistent, high-quality service.

How Customer Service Teams Ensure Consistency and Continuous Improvement in Service Quality

Customer service teams keep service quality high by setting clear standards, training agents regularly, and using customer feedback to improve. They rely on tools like CRMs and knowledge bases to provide consistent answers, and they review agent performance often to coach and improve.

With a strong focus on customers, standardized workflows, and data-driven decisions, these teams ensure reliable, helpful service that gets better over time.

Common Challenges in Scaling and Maintaining Quality Management

As customer service teams grow, common challenges include overwhelmed staff, inconsistent service from new hires, and outdated systems that can’t keep up with rising demand. Teams may also face communication issues, struggle to offer personalized support at scale, and find it hard to balance speed with quality.

Keeping employees trained and engaged, and updating goals and metrics as needs evolve, are also key hurdles. Solving these problems takes consistent planning, better tools, strong training, and a focus on continuous improvement.

What is Quality Monitoring?

Quality monitoring in customer service means checking how well agents handle customer conversations. It helps make sure agents follow company standards, solve issues correctly, and treat customers with care. By reviewing calls or chats, teams can find areas for improvement, provide coaching, and boost overall customer satisfaction.

How Companies Use Quality Monitoring to Improve Agent Performance

Companies regularly review customer interactions and give targeted feedback to their agents. They track metrics like resolution rates, customer satisfaction, and communication skills to guide coaching and recognize top performers.

Many also use software like Level AI’s customer support quality assurance tools to automatically score interactions, helping identify issues and ensure fair evaluations. This ongoing process of feedback, goal-setting, and coaching drives better service and improves both agent performance and customer satisfaction.

Best Practices for Implementing Quality Monitoring

Effective quality monitoring relies on a few key practices:

• Record all interactions (whether on voice or text) and analyze these to fairly represent agent performance.
• Use a single quality scorecard across all channels (calls, chats, emails, etc.) that ties back to business goals.
• Monitor in real time to catch and fix problems quickly, improving metrics like First Call Resolution.
• Let agents review their own performance to support growth and accountability.
• Include customer feedback and sentiment analysis to link agent behavior to satisfaction and loyalty.
• Use AI to analyze all interactions, spot patterns, and give clear performance insights.
• Hold calibration sessions to keep scoring fair and consistent among reviewers.
• Give agents timely, clear feedback and coaching.
• Encourage a team-wide focus on improving quality and customer experience.

Common Challenges or Pitfalls in Quality Monitoring

These include relying too much on checklists that miss important details like tone or empathy, and using outdated or limited technology that can’t handle all interactions. Many teams still spend time manually reviewing calls (rather than relying on contact center automation tools), which slows down feedback and makes it hard to scale.

Other issues include inconsistent scoring, ignoring customer satisfaction, poor agent training, missed sales chances, and risks with data security. Fixing these problems means using better tools like AI, setting clear standards, and staying focused on improving the customer experience.

What is Real-Time Agent Assist?

Real-time agent assist is an AI tool that helps customer service agents during live calls or chats by giving instant suggestions, answers, and reminders.

It listens to the conversation and quickly shows helpful tips, so agents can respond faster, stay accurate, and follow company rules. This boosts agent performance, shortens call times, and improves the customer experience in real time.

How Real-Time Agent Assist Works During Live Customer Interactions

Real-time agent assist uses AI to listen to live customer conversations and instantly give helpful suggestions to the agent. It starts by turning spoken words into text using automatic speech recognition with very little delay. Then, AI tools analyze the text to understand the customer’s needs, emotions, and context.

Based on this, the system shows the agent suggested replies, knowledge articles, compliance alerts, and next steps, right on their screen. It keeps updating throughout the conversation, helping the agent solve problems quickly, follow rules, and stay focused. In short, it acts like a live digital assistant, guiding agents in the moment to deliver better service.

How Real-Time Agent Assist Improves Agent Performance

Real-time agent assist’s support lowers stress of agents and boosts their confidence(especially for new agents) and cuts down on training time. It also improves customer satisfaction by making service more accurate, personal, and consistent. As a result, contact centers see faster calls, fewer repeat issues, and lower costs.

Typical Use Cases and Examples of Effective Real-Time Agent Assist

Real-time agent assist is used in contact centers to help agents work faster and more accurately during live customer interactions. Some examples include:

• Helps agents handle more calls by quickly showing answers, scripts, and steps to follow, especially useful during peak seasons.
• In banking or telecom, it shows recent transactions or fixes for common problems, making responses quicker and more accurate.
• Gives real-time coaching and replies to handle objections and boost conversions during live sales calls.
• Flags missing disclosures or policy steps as the agent talks, helping avoid compliance mistakes.
• Guides new agents through calls step-by-step, reducing training time and building confidence.
• Automatically writes summaries and updates systems, saving time on follow-ups.

These use cases show how agent assist improves speed, quality, and customer experience across industries.

What is Real-Time Speech Analytics?

Real-time speech analytics listens to live customer calls, instantly turning speech into insights using AI.

It helps detect emotions, keywords, and issues in real time, so supervisors can step in quickly, guide agents, or flag problems as they happen. This leads to better customer service and faster issue resolution.

Benefits and Uses of Real-Time Speech Analytics for Call Centers

It gives agents live guidance during calls, improving how they handle customer issues, objections, and sales opportunities. It detects fraud risks, customer emotions, and key phrases instantly, allowing for quick action and better service.

Real-time speech analytics also creates call summaries, boosts agent productivity, shortens resolution times, and helps managers adjust staffing and training in real time, all leading to lower costs, higher sales, and a better customer experience.

How to Implement Real-Time Speech Analytics

To use real-time speech analytics effectively in call centers, start by setting clear goals like improving satisfaction or ensuring compliance. Choose a reliable, real-time system that works with your current tools and supports features like sentiment analysis and keyword alerts.

Use cloud-based tech for scale, and train staff to act on insights during calls. Automate alerts for issues like negative sentiment or compliance risks, and keep improving the system based on regular feedback and performance checks.

Challenges of Real-Time Speech Analytics

Real-time speech analytics faces several challenges. These include protecting sensitive voice data under laws like GDPR and HIPAA, which requires consent, encryption, and data anonymization. It also needs fast, stable networks to avoid delays and ensure accurate transcription.

Background noise, poor audio quality, and varied accents can reduce accuracy, but can be improved with advanced noise filters and adaptive models. Integration with CRM or contact center tools can be complex, and scaling to handle many calls requires strong system design.

Staff may resist workflow changes, and high costs for advanced, real-time features can be a barrier. Solving these issues takes strong tech, privacy safeguards, and good planning.

What is Resolution Rate?

Resolution rate shows how many customer issues are fully solved out of all those received in a set period.

It helps teams understand how well they are resolving problems, no matter how many interactions it takes. Unlike first contact resolution, it measures whether the issue was eventually fixed, not just if it was handled on the first try.

How Resolution Rate is Calculated

Resolution rate in customer service is calculated as the percentage of customer issues or tickets that are successfully resolved out of the total number of issues received. The formula is:

Resolution rate (%) = (Number of issues resolvedTotal number of issues received) x 100

This means you divide the count of resolved tickets by the total tickets received in a set period, then multiply by 100 to get a percentage. "Resolved" typically means the issue was fully addressed without needing further follow-up from the customer or support team. This metric shows how effectively the customer service team closes customer cases over time.

It’s important to distinguish resolution rate from First Call Resolution Rate (FCR), which measures only the issues resolved during the first interaction. Resolution rate accounts for all issues resolved eventually, regardless of how many contacts it took.

What’s Considered a Good or Benchmark Resolution Rate in Customer Service?

A good resolution rate in customer service is typically above 70%. Rates between 70–79% are solid, 80% or higher is world-class, and over 90% is excellent.

Benchmarks vary by industry; tech support may aim for 65–70% due to complexity, while retail tends to be higher. Higher resolution rates often lead to better customer satisfaction and loyalty.

How to Improve Resolution Rate

Customer service teams can raise their resolution rate by training agents well, giving them easy access to customer history and internal knowledge, and using tools like CRMs and chatbots.

Offering strong self-service options helps customers solve simple issues on their own, while freeing agents to focus on complex cases. Teams should also track performance, fix common problems, and give agents the power to solve issues quickly, all of which lead to faster resolutions and happier customers.

What is Retrieval Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) is an AI method used in customer service to improve chatbot and virtual assistant responses.

It pulls up-to-date information from company documents (like FAQs or manuals) and uses that to help a language model create accurate, personalized answers. This makes support faster, more helpful, and more aligned with the company’s current knowledge.

How Rag Improves the Accuracy and Relevance of Responses

RAG improves customer service by combining real-time data retrieval with AI-generated responses. Instead of relying only on pre-trained knowledge, RAG pulls up-to-date, company-specific info, like FAQs, manuals, and customer histories, to create accurate, personalized answers.

This helps chatbots give better support by:

• Automatically finding the right information for each question
• Filtering and ranking content to match the customer’s context
• Reducing errors and outdated replies
• Handling more complex requests without needing a human agent

RAG systems have been shown to speed up response times, increase satisfaction, and make replies more accurate and brand-aligned.

Technical Challenges in Implementing RAG

The biggest technical challenges of using RAG include speed, scale, integration, and data quality. Delays can happen when searching large databases, especially with lots of users or big data sets.

Making RAG work well also requires strong infrastructure, clean and updated content, and smooth connections between systems. Teams must carefully tune the system and constantly maintain it to avoid inaccurate or confusing answers.

Best Practices for Integrating RAG with Existing Customer Support Platforms and Workflows

To successfully use RAG in customer support:

• Gather FAQs, manuals, and past chats, then convert them into searchable formats for fast retrieval.
• Choose and fine-tune language models that balance speed and accuracy for your use case.
• Embed RAG into agent desktops or deploy in chatbots across web and messaging platforms to support or automate service.
• Use caching and lightweight models to reduce response times and scale efficiently.
• Regularly check performance, gather feedback, and set up backups that route to humans when needed.
• Update the knowledge base often to ensure answers stay current.
• Design intuitive interfaces that make AI suggestions easy for agents to use and helpful for customers.

What is a Scorecard?

A scorecard is a tool that tracks how well agents and teams are performing using key metrics like response time, resolution rate, and customer satisfaction.

It helps managers spot trends, measure progress, and improve service by showing what’s working and what needs attention. Scorecards are often used as dashboards or evaluation forms to guide coaching and decision-making.

Metrics to Include on a Customer Service Scorecard

Key metrics and rating categories to include on a scorecard are:

• Customer Satisfaction (CSAT): Measures how satisfied customers are with a specific interaction or service element, typically via a rating scale (e.g., 1 to 5).
• Customer Effort Score (CES): Evaluates how easy it is for customers to get their issues resolved, highlighting friction in the experience.
• First Call Resolution (FCR): The percentage of issues resolved in the first interaction without follow-up needed.
• First Response Time (FRT): Measures how quickly the support team responds initially to a customer inquiry.
• Average Handle Time (AHT): The average time spent resolving a customer query or handling their call/chat.
• Tone and Empathy: Often assessed qualitatively, rating the emotional engagement and personalization in interactions.
• Service Level Agreement (SLA) Compliance: Percentage of issues handled within agreed timeframes.

To learn more about scorecards and best practices for these, see our article “Best Practices for QA Scorecards in Customer Service for Collections Industry.”

What is Screen Recording?

Screen recording captures video of what a customer service agent does on their computer during calls or chats.

It helps managers see how agents work, find mistakes or slowdowns, and use that information to coach agents, improve processes, and boost the customer experience.

How Screen Recording Enhances Quality Assurance

Screen recording gives QA teams a full view of how agents handle customer interactions by showing what they do on-screen, not just what they say. It helps identify slowdowns, mistakes, or missed steps by showing how agents use tools and follow processes.

When synced with call audio and transcripts, screen recordings make it easier to coach agents, fix workflow issues, ensure compliance, and improve customer service.

Best Practices for Protecting Sensitive Customer Data

To protect sensitive customer data and stay compliant with laws like PCI DSS and HIPAA, companies should encrypt recordings, limit access with tools like 2FA and SSO, and blur private details like credit card numbers.

They should also get clear user consent, log all access, and use secure cloud storage. Regular training, privacy checks, and vendor audits help ensure ongoing security and compliance.

How Screen Recordings Be Utilized for Training and Onboarding New Agents

Screen recordings are a compelling training tool for new agents, showing real examples of customer interactions to help them learn faster and more confidently. Watching experienced agents in action helps new hires understand systems, tools, and best practices better than manuals or lectures alone.

Trainers can use these recordings to highlight what works, correct common mistakes, and offer personalized feedback, making onboarding more effective, interactive, and scalable.

What Screen Recording Tools Should I Use?

To learn about recommended tools and solutions, see our article on the “6 Best Call Center Recording Solutions.”

What is Semantic Intelligence?

Semantic intelligence is an AI capability that understands the meaning and intent behind what customers say, not just the words.

It helps support teams by identifying things like complaints or cancellations in real time, guiding agents with relevant prompts, and analyzing all interactions to improve quality and customer experience.

To learn more about semantic intelligence in the context of customer service, see our article “How Semantic Intelligence Can Transform Customer Experience for Modern Contact Centers.”

What is Sentiment Analysis?

Sentiment analysis is a way for businesses to use AI and language tools to figure out if a customer sounds happy, upset, or neutral during chats, emails, or calls.

It helps teams spot problems early and improve how they respond, leading to better service and happier customers.

To learn more about sentiment analysis, see our article “An In-Depth Guide to Call Center Sentiment Analysis.”

What is Speech Analytics?

Speech analytics uses AI to turn spoken conversations into text and analyze what was said, along with how it was said, to find patterns, customer sentiment, and key issues.

It helps teams improve agent performance, spot compliance risks, and make faster decisions that lead to better service and happier customers.

To learn more about speech analytics, see our article “What Is Speech Analytics and How It Can Supercharge Your Contact Center.”

What is Shrinkage?

Shrinkage is the percentage of time agents are scheduled to work but aren’t available to handle customer interactions.

This includes things like breaks, training, sick days, or technical issues. It’s a key metric for planning staffing levels and maintaining good service quality.

To learn more about shrinkage, see our article “What Is Call Center Shrinkage?”

What is Voice Analytics?

Voice analytics is AI-powered technology that records and analyzes phone conversations in contact centers.

It not only transcribes speech but also detects tone, emotion, and intent to better understand customer sentiment and agent performance. This helps improve service quality, compliance, and decision-making.

How Voice Analytics Improves First Call Resolution

Call center voice analytics reduces wait times and solves issues faster by handling routine questions through voicebots, routing calls to the right agent, and analyzing calls in real time to fix problems quickly.

It also improves IVR menus and uses smart callbacks, leading to shorter hold times and more issues resolved on the first try.

Challenges in Integrating Voice Analytics With Legacy Systems

Legacy support systems often lack real-time APIs and use outdated tech, making it hard to connect modern voice analytics tools. Data is buried in old interfaces, authentication is complex, and inconsistent database fields create issues for AI.

These systems may also be too slow for real-time use, requiring workarounds like middleware, caching, or treating them as read-only. Training and change management are also needed to help teams adapt.

How Voice Analytics Handles Emotional Cues and Personalization

Voice analytics uses AI to detect emotions like frustration or urgency by analyzing tone, pitch, and speech patterns. It gives agents real-time alerts and coaching to respond with empathy and adjust their approach.

By connecting with customer history from CRM systems, it also helps personalize each interaction, reduce escalations, and improve satisfaction.

What is Voice of the Customer?

Voice of the Customer (VoC) is the process of collecting and analyzing feedback to understand what customers think, feel, and need.

It includes direct input like surveys and reviews, as well as behavior like usage patterns. Companies use VoC to improve service, boost satisfaction, and align better with customer expectations.

To learn more about voice of the customer, see our article “What is VoC (Voice of the Customer)?”