Call Center Performance Management Software: Top 7 Options

Call center performance management software helps contact centers improve agent performance and overall operations by automatically tracking and analyzing key metrics.

Managing call center performance involves using real-time dashboards, call monitoring, and coaching tools to spot issues and take action quickly. This makes it easier to review agent interactions, provide helpful feedback, and keep customer service consistent across every channel.

In this article, we present a list of KPIs to track performance in call centers, share factors to consider when choosing a solution, and provide a list of the top seven tools for you to choose from:

1. Level AI: AI-Led Coaching & Performance Insights at Scale
2. Enthu.AI: Streamline Appointment Scheduling
3. AmplifAI: Gamify CS Performance Optimization
4. MaestroQA: Automate Coaching Workflows
5. Convin.ai: Automate Learning & Development
6. Zendesk: Workforce Management
7. CallMiner: Improve Frontline Agent Experience

KPIs for Measuring Call Center Performance

You typically measure contact center performance using:

• Customer satisfaction score (CSAT), typically on a 1-5 scale.
• Net promoter score (NPS), or customers’ willingness to recommend your company to others, on a scale from 1 to 10.
• Average handle time (AHT) measures the average duration of a customer service interaction, including conversation time, hold time, and after-call work.
• First response time (FRT) shows how long it takes for a customer to be connected with an agent.
• Total resolution time measures how long it takes agents to resolve customer issues on average.
• Agent utilization rate tracks how much time agents spend actively handling customer calls throughout their workday.
• Finally, after-call work time (ACW) shows how long it takes an agent to wrap up after-call tasks and prepare for the next call.

Overindexing on efficiency-focused KPIs (like AHT or agent utilization rate) risks lowering CSAT scores and the customer experience, as agents may prioritize speed over quality in their interactions.

At the same time, if a call center is understaffed, there’s not much even the most dedicated agent can do about long wait times. So managers may need to look beyond agent coaching to improve performance.

You can also:

• Improve agents’ productivity through the use of contact center automation tools for non-customer-facing tasks, allowing agents to focus more on customer interactions.
• Improve agents’ skillset through training, coaching, and real-time support, so they can provide high-quality service.
• Reduce contact volume by expanding customer self-service options and deploying AI agents that resolve common issues without human intervention.

To turn strategies into systematic action, we recommend performance management software that can analyze interaction data at scale and support continuous coaching and process improvement, as we describe below.

What to Look for in Performance Management Software

Useful performance management tools not only collect data but also help you understand what’s happening in your calls and uncover trends that affect customer experience. Consider key features that:

Capture and Analyze Data Accurately

The platform should capture what customers and agents are saying and feeling with a high level of accuracy to better inform coaching programs and other opportunities.

Since inaccurate or incomplete data about call drivers and sentiment can lead to inaccurate evaluations and low-quality feedback, we recommend looking into AI-driven solutions for customer analytics use cases, rather than relying on products utilizing rule-based algorithms and keyword matching.

Help You Reach Higher Operational Efficiency

The solution should also help you improve agent productivity by automating after-call tasks or using AI to generate follow-up emails or call summaries.

Some additional features that promote call center efficiency include real-time agent support for better first-call resolution rates or average handling times, automated QA, and AI agents to automate repetitive tasks or handle simple customer queries.

Coach Agents Effectively

The platform should also let you evaluate agent performance according to your organization’s rubrics and provide easy access to coaching tools.

This means offering ways to initiate coaching sessions from within dashboards or to auto-save call recordings for later coaching.

Report Across Data and Tool Silos

The ability to track data from across many different platforms and sources (such as CRM or QA systems and ticket tools) allows you to uncover deeper insights about your organization’s performance, like:

• Why is there a sudden spike in customer calls on a certain topic?
• Are there any seasonal trends we can proactively address to avoid hiring more agents during peak times?
• Are there any differences between customer CSAT scores depending on specific call topics or across agents? What are the root causes of those differences?

By connecting the dots across systems by using tools like call center system tracking software, you move from simply reporting metrics to understanding what’s really driving them.

With those factors in mind, let’s look at seven performance management solutions that can help you put them into action, beginning with our own solution, Level AI.

7 Call Center Performance Management Solutions

1. Level AI: Best for AI-Led Coaching and Performance Insights at Scale

Level AI is a QA and performance management platform that analyzes 100% of customer interactions to deliver actionable insights, objective performance scores, and targeted coaching at scale.

Our solution records all support interactions, captures agents’ screens, and highlights topics discussed during those calls. It also surfaces relevant KPIs and metrics related to those interactions.

This makes it easier for managers and coaches to review performance, identify improvement areas, and support agent growth. Just as importantly, Level AI gives managers a complete view of call trends and performance data, helping them uncover opportunities to improve call center operations.

Uncovering True Customer Intent for Accurate Call Analysis

Level AI relies on natural language understanding and semantic intelligence to analyze conversations and categorize call drivers.

These topics don’t need to be phrased in exact terms. Level AI’s Scenario Engine interprets meaning and context to identify intents with near-human accuracy.

For example, customers don’t need to say “quality issue” if they’re complaining about issues with a product, since the Scenario Engine understands context and captures intent to categorize it accurately.

The Scenario Engine features common customer support scenarios out of the box, but you can also define your own industry- or company-specific scenarios.

While it’s important to capture specific topics that are brought up in conversations, it’s just as important to understand how customers feel about their interactions, as described below.

Customer Sentiment Analysis Beyond Simply Positive or Negative

Identifying feelings expressed during a conversation as simply positive or negative isn’t enough to capture the emotional state of customers — or how agents can influence that.

That’s why detecting distinct emotions is important, and it’s why our platform recognizes more emotions than any other solution on the market:

• Happiness
• Admiration
• Gratitude
• Worry
• Disapproval
• Annoyance
• Disappointment
• Anger

Level AI also indicates where such emotions were expressed in conversational transcripts using sentiment tags to help QA teams and managers find calls matching a certain sentiment.

A conversation’s Sentiment Score denotes the call’s overall direction and intensity of customer sentiment; the higher the score, the more positive the interaction:

Sentiment Score is calculated by individually weighing emotions expressed during the conversation: those occurring closer to the end of the call are weighted more heavily, since they reflect a customer’s lasting impressions and feelings about how well the agent resolved their issue.

This helps QA staff and call center managers more accurately track and measure the impact of agent performance on the customer experience, and quickly identify opportunities for improvement.

Automated Scoring and Reviews

Traditionally, call center staff listened through calls and manually graded how well agents did, which was time-consuming and limited the number of interactions that could be reviewed (usually 1–2% of calls).

While random sampling provides broader insights with minimal resources, it sometimes introduces subjectivity and biases in scoring, such as favoring more recent calls, overemphasizing good interactions, or reflecting the evaluator’s personal interpretations rather than consistent standards.

Instead of hoping that you get an accurate enough picture of agent performance based on a limited number of reviewed calls, you can use solutions that can provide a complete coverage of all calls and score their performance automatically, like Level AI.

Instant Agent Performance Evaluation

One way that Level AI auto-scores calls is with InstaScore, which evaluates agents’ performance based on your organization’s rubrics.

An InstaScore value is assigned to each interaction and is expressed as a single percentage value that measures how well an agent (for example, depending on your rubrics):

• Completed required actions, such as asking for specific information
• Maintained composure throughout the call
• Performed with respect to certain defined metrics, like first response time, total silence percentage, or agent overtalk.

InstaScore also highlights relevant moments that influenced the score, providing you with additional context. You can use this information to decide if you need to address specific performance issues with agents, together, or individually.

Accurately auto-scoring agent conversations as part of performance management doesn’t just help improve the customer experience; it can also result in higher agent satisfaction. One of our customers was able to reduce employee attrition by 30%.

Real-Time Call Monitoring

While evaluating agent performance and catching critical issues is important, it’s even more useful to do so as calls are happening. Call quality monitoring tools like Level AI’s Real-Time Manager Assist let supervisors monitor active calls, detect coaching opportunities instantly, and intervene when it matters most, before a poor customer experience unfolds:

Managers who are too busy to use the dashboard can also set up notifications alerting them about low customer sentiment, poor agent performance, unusually long calls, and more.

See our latest article on how to monitor call center performance.

Agent Coaching Tools

As we’ve discussed, coaching is a key area for improving agent performance, but coaching workflows are often fragmented in performance management software. This forces coaches and managers to switch between dashboards and reports when identifying issues, notifying agents, or initiating coaching sessions.

Level AI integrates coaching functionality directly into many of its dashboards, allowing you to initiate coaching sessions after reviewing data, for example, about a particular call.

You can also set up a Conversation Library to share examples of high-quality calls that follow best practices or calls that you want to review with teams or specific agents.

Our platform also offers dedicated coaching dashboards to do things like track team and agent stats after coaching sessions.

Such dashboards show:

• Coaching stats, like the total number of sessions, average number of sessions per agent, and so on.
• Status of upcoming, current, and past coaching sessions.
• Lists of action items for coaching sessions.
• Trends showing the effect of coaching sessions on agent performance, and more.

Improving Agent Productivity Through (AI-Based) Automation

Agents are susceptible to stress, especially during peak call volume times. This includes switching between tasks and completing post-call work, which can be distracting. It can also decrease utilization rates, leading to longer wait times for customers.

Level AI provides features for reducing both agents’ cognitive load and stress, which we describe below.

Automated Call Categorization and Summaries

To reduce the amount of after-call work, our software automates dispositioning by automatically categorizing calls. This saves agents from having to manually classify conversations, often by searching through lists or trees of categories.

The platform also summarizes calls by describing what they were about, actions taken, whether they were resolved, and any required next steps.

Real-Time Support for Agents During Customer Calls

One of the biggest challenges for agents is maintaining the flow of conversation while finding answers. Oftentimes, agents put customers on hold, and this can be stressful as well.

Level AI’s Real-Time Agent Assist detects topics being discussed in real time and shows the appropriate answers on screen. These consist of relevant tips and information such as:

• A main feed with action hints, warnings, and FAQs
• Resources from ticketing and knowledge systems
• Search functionality (AgentGPT) to help agents find additional information without long holds or long periods of dead time

The latter feature automatically fills in the search bar with search queries that match customer topics and intent, and displays (and summarizes) information from internal sources.

For one of Level AI’s customers, Real-Time Agent Assist led to a 13% decrease in AHT overall and a 23% reduction in call hold time during peak hours.

Reporting on Call Center Performance KPIs and Analyzing Emerging Trends

What if you could identify trends in the way your agents handle refunds and save over $30 million in under a year?

Or if you could deflect calls to reduce your call center workload and save over $3 million annually, just by adding a self-service link on your customer support portal?

And if you could identify specific product features that cause issues and share them with the product team to improve customer satisfaction with the product?

All of this is possible if you can identify patterns that hold back your call center performance.

Detecting Trends in Customer Satisfaction and Conversation Topics

To give managers an accurate picture of changes in customer satisfaction and query topics, Level AI’s Voice of the Customer Insights captures trends in how customers mention recurring topics, as well as key performance indicators like CSAT, CES, and NPS.

It also provides Level AI’s proprietary inferred customer satisfaction score, or iCSAT, which gives a holistic view of satisfaction that surveys often miss. It’s based on customer sentiment, customer effort, and resolution status, and evaluates call center performance comprehensively without waiting for customer feedback or relying on small data samples to evaluate overall CSAT.

Our voice of customer software also identifies recurring topics of customer complaints or other issues, such as their experience using a product or service. You can filter data by time, channel, and categories to zoom in on specific issues.

Exploring Previously Uncovered Opportunities

A common challenge for call center managers is having to work with incomplete data because of disparate external sources, thanks to organizational or technological silos, which forces them to comb through different sources.

Our Reporting and Analytics integrates with external CRMs, survey tools, and many other types of platforms, so that you can build custom filters to explore trends.

For example, the dashboards help find correlations between call topics and low CSAT, AHT, or call escalations.

You can also use call center real-time reporting to explore specific topic breakdowns to find customer issues that need to be shared with other teams, such as software issues, checkout glitches, or complaints about a new supplier.

Or, you can uncover queries that are taking up a lot of agents’ time but could be resolved by adding a checkout link or a new topic to a customer-facing knowledge base.

To see how you can use Level AI to improve your call center management performance, reach out to schedule your demo today.

2. Enthu.AI for Streamlining Appointment Scheduling

Enthu.AI supports teams in customer support and sales across healthcare, utilities, insurance, and financial industries. It includes agent training, quality assurance, and sales performance optimization features.

Additionally, Enthu.AI offers scheduling capabilities to help reduce wait times for customers who need to book appointments, such as with healthcare providers.

Enthu.AI offers a 14-day free trial, but doesn’t provide any pricing information on the website.

3. AmplifAI for Gamifying CS Performance Optimization

AmplifAI supports companies in retail, healthcare, tourism and hospitality, communications, and others.

Its features assist with coaching, analytics, and process optimization. AmplifAI also has gamification features to encourage agents to improve their performance and celebrate their achievements.

These features also offer leaderboards, goal trackers, and games.

There’s no pricing information available on the AmplifAI website.

4. MaestroQA for Assisting with Coaching Workflows

MaestroQA is a basic quality assurance tool designed to assist coaching for SMBs in various industries. However, it offers underpowered AI capabilities that restrict its capabilities for automation and overall performance.

For example, in contrast to Level AI, it offers rudimentary sentiment detection and doesn’t pick up on emotions. Also, you can use it in coaching workflows, but we find that it doesn’t accurately pinpoint where agents did well or not, due to its limited natural language understanding.

You can request pricing through the MaestroQA website.

5. Convin for Learning and Development

Covin.ai is designed to support enterprise-level call centers to automate some aspects of coaching, improve call center operations efficiency, and support sales and customer support teams. It works with fintech, healthcare, hospitality, and BPO industries.

Covin.ai can be used to create an asynchronous learning center for both onboarding and ongoing learning, including via an app.

The website shows available plans (CX Suite, Real-Time Suite, and more), but doesn’t include specific pricing information on the website.

6. Zendesk for Workforce Management

Zendesk's customer service platform supports companies in telecommunications, retail, education, manufacturing, healthcare, and financial services. It provides several AI-supported features, including AI agents and automations.

Additionally, it can help call center managers avoid workforce management inefficiencies. It offers features like staffing needs forecasting, automatic agent scheduling, and real-time agent activity tracking.

Zendesk pricing starts at $19 per agent per month (billed annually) for the lowest-tier plan.

7. CallMiner for Assisting Frontline Agents

CallMiner contact center software provides customer support and sales teams with guidance and feedback. But unlike Level AI, its prompts are fixed and don’t adapt to the flow of conversation.

While users are generally positive about its reporting capabilities, a clunky interface and inaccuracies in both transcript and speech analytics are sometimes mentioned.

The CallMiner website doesn’t provide any pricing information at the time of writing.