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Call center analytics and how to use them to improve performance

Nurturing customer relationships comes down to anticipating needs and understanding behaviors. But intuition will only get you so far. To build a customer-first culture (and a successful business), you need to be able to uncover where your customer support efforts are falling short. 

Call center analytics make that possible. By turning raw interaction data into actionable insights, they can highlight gaps in agent training, reveal process bottlenecks, and show where your tech stack isn’t delivering.

Comprehensive call center analytics can:

• Make your call center more agile

• Improve sales team conversions

• Increase internal alignment

This in-depth guide explains what call center analytics are, where the data comes from, and the different types to consider. You’ll also learn why conversational analytics are so important and the challenges teams face, plus how the right software can solve them.

TL;DR:

Call center analytics transform raw interaction data into actionable insights that help improve agent performance, customer experience, and operational efficiency. By analyzing data from calls, chats, emails, IVRs, and CRM systems, businesses can identify training gaps, streamline workflows, and predict customer behavior.

The article covers seven core types of analytics:

1. Speech analytics: Detects sentiment, keywords, and compliance issues in voice calls.

2. Text analytics:Analyzes written interactions across channels to spot trends.

3. Predictive analytics: Forecasts call volume, agent performance, and churn risk.

4. Self-service analytics: Tracks FAQ, IVR, and help center usage to identify gaps.

5. Desktop/mobile analytics: Monitors agent activity and tool efficiency.

6. Cross-channel analytics: Unifies customer data across platforms for full journey visibility.

7. Interaction analytics: Merges voice/text/metadata to uncover pain points and improve FCR.

Why it matters: Analytics boost agility, drive alignment, enhance sales conversions, and elevate customer satisfaction. The article also breaks down common challenges (like data overload or fragmented journeys) and explains how tools like Aircall solve them with real-time insights, omnichannel integration, and smart reporting features.

Key takeaway: With the right analytics platform, call centers can move from reactive to proactive support, improving both agent productivity and customer loyalty.

What are call center analytics?

Call center analytics is the practice of gathering and analyzing data from every customer interaction, including calls, chats, emails, CRM logs to generate insights that improve performance and customer experience.

Why this matters:

Without analytics, leaders only see surface-level results and miss the reasons behind customer behavior. With the right insights, they can improve CSAT, lower effort scores and strengthen service-level performance.

Analytics also make it possible to cut handle times, refine services and reduce costs. For example, speech analytics alone can lift customer satisfaction by up to 10% while lowering operational costs by 20–30%.

Call center analytics vs. call center KPIs

Call center analytics and KPIs are often mentioned together, but they serve very different purposes. Below, we have broken down the difference:

• Call center analytics: The process of examining customer data to uncover patterns, trends and the root causes of issues that affect customer satisfaction and call center performance. This might include analyzing speech-to-text transcripts to detect frustration, reviewing call routing to spot bottlenecks or studying agent histories to identify training needs.

• Call center KPIs: The measurable benchmarks that track performance over time. Common examples include CSAT, FCR, call volume and AHT. KPIs provide a snapshot of how well the call center is meeting its targets.

Think of it this way: analytics is the engine under the hood, while KPIs are the dashboard indicators that show how the call center is running. For example, analytics might reveal that agents struggle with pricing questions, while KPIs such as FCR or CSAT would show the impact through lower satisfaction or higher callback rates.

Where does center analytics data come from?

Call center analytics draws from multiple data sources to create a complete picture of customer interactions and agent performance. Key inputs include:

• Transcriptions of voice calls (AI speech-to-text): Converts spoken conversations into text for keyword analysis, sentiment tracking, and compliance monitoring.

• CRM platforms: Stores customer profiles, interaction history, and purchase records to link behavior with outcomes.

• IVR logs: Captures caller pathways, menu selections, and points of abandonment for process optimization.

• Chat and email logs: Tracks text-based interactions to uncover common inquiries and measure response quality.

• Customer survey feedback (CSAT, CES, NPS): Provides direct customer sentiment data to evaluate satisfaction and effort levels.

• Helpdesk interactions and social messaging: Monitors tickets, DMs, and public posts to identify service trends and urgent issues.

• Event logs: Records system activities, outages, or workflow triggers to connect operational events with customer impact.

7 types of contact center analytics

Your customers aren’t just calling you; they interact with your team through several channels. This means you have numerous opportunities to collect data and collate actionable insights. The more data you collect, the more accurate your insights. 

Here are the seven most common types of call center analytics to help you better understand the customer experience and how to improve it.

1. Speech analytics

Speech analytics uses AI to analyze recorded or live calls, transcribing conversations and identifying keywords, tone and sentiment. By surfacing patterns in conversations and agent behavior, speech analytics makes it easier for managers to pinpoint both strengths and areas for improvement.

How it works: 

AI converts speech into text in real time or after a call. Natural language processing (NLP) then scans the conversation for sentiment shifts, compliance risks, and recurring themes. 

The result is a structured picture of customer emotion, agent behavior, and common issues that would otherwise be buried in hours of recordings.

Use cases:

• Use conversation date to refine call center scripts by uncovering phrases linked to higher conversions

• Apply sentiment insights to detect emerging customer issues before they escalate 

• Monitor compliance automatically by scanning for required disclosures or language 

• Leverage real-time call monitoring and whisper features to provide live coaching during calls

With voice analytics for call centers, teams can reduce handle times, increase CSAT, and avoid costly escalations. Also, real-time visibility into conversation tone enables managers to coach promptly, resulting in measurable performance improvements.

2. Text analytics

Like speech analytics, call center text analytics uses AI to detect keywords, tone, and patterns in customer conversations. The difference is that it focuses on text-based customer interactions, such as call transcripts, emails, chats, tickets, SMS messages, and social media interactions. 

How it works: 

AI applies NLP to scan text data across channels. It categorizes topics, flags tone shifts, and clusters common issues to reveal recurring themes.

Use cases:

• Analyze written interactions to spot trending support issues across channels

• Use text categorization to route support tickets by topic or urgency

• Apply insights from recurring phrases to improve chatbot and email templates

• Surface emerging themes across integrated tools like Intercom, Zendesk, or Help Scout 

By surfacing patterns in text, teams can respond faster, communicate more consistently and equip agents with clearer context. This reduces friction and increases customer satisfaction.

3. Predictive analytics

Predictive analytics uses machine learning to predict customer behavior, incoming call volume, and agent performance trends based on historical data. It also uses historical data to predict peak hours or seasonal spikes which helps teams plan resources more effectively.

How it works:

Algorithms train on past interaction data such as calls and tickets along with external variables like time of day or marketing campaigns. From these patterns, machine learning models can forecast demand and suggest “next best action” for agents or targeted coaching moments.

Use cases:

• Apply forecast data to schedule staff for expected peak periods 

• Use performance trend predictions to coach at-risk agents proactively

• Predict customer churn risk and trigger retention workflows

By anticipating demand rather than reacting to it, predictive analytics helps reduce staffing costs, improve agent performance and support stronger customer retention.

4. Self-service analytics

Self-service analytics mine data from self-service communication channels like IVR flows, FAQ pages, and knowledge base usage.This data highlights the most common keywords, questions and phrases customers look for to give teams a clear view of what information customers struggle to find on their own.

For example, if “How long does shipping take?” is the most visited page in your knowledge base, you can add shipping times directly to product pages to make the information more accessible. As a result, you’ll likely reduce the number of inbound calls you receive for common queries and customers have better experiences.

How it works:

This type of analytics tracks metrics such as search queries, frequent abandonment points, or drop-outs across help portals or IVR systems.

Use cases:

• Use search and query data to improve self-service channels so customers can resolve issues independently Apply data on IVR flows to redesign menu paths and reduce drop-offs

• Identify frequent searches to update FAQs to cover those questions 

• Pinpoint gaps in customer searches and expand the knowledge base where self-help falls short

By analyzing self-service data, teams can reduce contact volume, speed up resolutions and lower operational costs, while making it easier for customers to resolve issues on their own.

5. Desktop and mobile analytics

Call center desktop and mobile analytics track how agents use their devices, including screen activity, app switching, and idle time to uncover inefficiencies and potential security risks.

How it works:

In this type of analytics, interaction logs and usage patterns from agent desktops or mobile devices to reveal bottlenecks or inefficient tools.

Use cases:

• Analyze device data to spot agents relying on outdated or slow tools

• Review idle time patterns to identify when agents may need extra coaching or support

• Examine workflow data to uncover inefficient processes and areas where agents struggle

• Monitor real-time usage data to surface potential security risks

By analyzing desktop and mobile data, teams can streamline workflows, improve security and enhance training effectiveness. These gains contribute directly to improved call center productivity. 

6. Cross-channel analytics

Customers today expect a seamless omnichannel experience, and cross-channel analytics make this possible. Cross-channel analytics analyze data from all your customer support and sales channels to give you a complete picture of the customer journey. This helps you understand which communication platforms your customers prefer and how they use each platform differently. 

How it works:

Cross-channel analytics aggregate data from CRMs, helpdesks, messaging platforms and phone systems. They map customer behavior across channels to identify preferences and handoff points, showing you which channels customers rely on most and where their journeys tend to break down.

Use cases:

• Use channel preference data to route customers to the channels they are most comfortable with

• Apply journey insights to personalize interactions based on past behavior

• Analyze usage trends to adjust staffing across channels more effectively

With cross-channel analytics, leaders gain full visibility into customer journeys, which enables seamless experiences across platforms and eliminates support fragmentation. Aircall integrates with your CRM and helpdesk tools to help centralize all data which is an essential step in learning how to improve the data quality of your call center analytics.

7. Interaction analytics

Interaction analytics merges voice, text, and metadata to map the full customer journey. By analyzing conversations alongside details like call duration, hold time and messaging logs, it uncovers common pain points and the drivers behind escalations.

How it works:

Interaction analytics combines transcripts, metadata (call duration, hold time), and messaging logs to surface patterns in customer behavior. With Aircall, you can connect to omnichannel platforms to centralize this data in one place.

Use cases:

• Map gaps in the customer journey to improve First Call Resolution (FCR)

• Track touchpoints to identify recurring issues more accurately

• Refine call routing logic based on behavior

By analyzing every customer interaction in context, teams can streamline workflows, resolve issues faster and tailor support based on each customer’s history and preferences.

Why are call center analytics important?

Now that we’ve looked at the different types of call center analytics, you might be wondering why they matter. The answer is simple. Call center analytics are crucial because the stakes have never been higher: 73% of consumers will switch to a competitor after multiple bad experiences. 

Analytics help you spot service gaps, strengthen agent performance, and optimize workflows so your team can deliver seamless support at scale.

In short, analytics transform everyday interactions into insights that fuel better service and stronger business results. Here are the key reasons why they matter:

Improve call center agility

When call center agents face high call volumes and low staffing, customer needs can fall behind. This leads to long wait times, reduced resolution rates, and increased customer churn.

Call center analytics help you prevent this by predicting when you can expect high call volumes, such as during holidays or product launches, so you can adjust staffing in advance. 

They also reveal patterns in customer interactions and highlight gaps in systems or processes. For example, a surge in social media inquiries can prompt targeted training or resource shifts.

The result? Your call center team adapts faster, responds more effectively and meets both business and customer demands with greater efficiency.

Help your team align on strategy

Call center analytics make it easier to share information and insights across sales, marketing, product, revenue and customer support teams. This improves collaboration, aligns customer communication management strategies and ultimately creates better customer relationships.

As a result, your business intelligence grows and cross-team collaboration improves. 

Facilitate objective decision-making

Relying on gut instinct can lead to poor business decisions. Intuition does not explain why one choice outperforms another or how to achieve call center KPIs.

Investing in call center analytics builds a data-driven culture which makes the data accessible to everyone in your company. For example, call center managers can track agent productivity, identify performance gaps, and pinpoint where additional training is needed.

Improve your sales conversions

A good call center analytics tool won’t only improve efficiency in your call center; it can  proactively unlock ways to improve revenue. By analyzing behavior profiles, demographics, and purchase history, your team can predict customer interests and deliver personalized recommendations that increase sales.

Analytics also help you determine the most effective strategies for outbound calls. For example, data may show that calling leads in the afternoon yields higher conversion rates compared to the morning. This gives your team actionable insights to optimize performance.

Boost agent performance

Call center analytics offer unbiased data on where agents excel and where they may need support. They also enable objective identification of top performers using KPIs, such as hold times and first-call resolution rates for customer support agents or close rates and deal value for sales reps.

Enhance the customer experience

88% of customers say the experience a company provides is as important as its products or services. In competitive industries, exceptional service is often the key differentiator.

Call center analytics can do more than let you track customer complaints. They give you the data to anticipate customer needs. By analyzing purchase history and past you can segment your audiences and deliver more personalised experiences that strengthen loyalty across the entire journey.

Discover Aircall’s powerful analytics features for a call center that maximizes agent productivity and customer satisfaction. Try for free.

4 challenges of contact center analytics (and how the right software can solve them)

We’ve seen the business benefits of investing in call center analytics, but setting it up isn’t always simple. Analyzing call center data can be complicated without the right tools in place. Below are four common obstacles contact centers face and how software like Aircall can help turn raw data into actionable insights.

1. Data overload

Contact centers generate numerous metrics and data points, which makes it hard to see what truly matters. When there is too much data, valuable insights get buried and  performance improvements slow down.

Solution: Integrations with your existing tech stack 

Aircall integrates call center data with 200+ business tools, including solutions like Adepsi, ClicData, Datagrail, Daton, and Fivetran,.

Centralizing your data across your tech stack reduces overload, streamlines analysis, and lets you focus on the metrics that matter most to your business goals.

2. Delayed insights

Outdated data leads to slow responses and missed opportunities. Without real-time visibility, teams cannot spot shifts in customer needs or behavior quickly enough to adjust, which causes service levels to suffer.

Solution: Real-time activity feed 

Aircall’s Live Monitoring dashboard offers immediate insights into call center activity. You can track all inbound and outbound calls as they happen to get a live overview of team performance. This feature helps you make quick, informed adjustments in real time, right when they matter most.

3. Inadequate KPI tracking

When KPIs are not tracked effectively, it’s hard to know if you’re meeting your business targets. Limited or outdated data makes it difficult to measure agent performance, evaluate call outcomes, and see whether customers are truly satisfied with their experience.

Solution: Detailed reports

Aircall provides comprehensive reports on inbound calls, agent activity, call direction, and service-level adherence. Integration with your CRM and other tools, plus customizable dashboards, ensures metrics are tailored to your business goals.

4. Complex customer journeys

Customers interact across multiple channels, including phone, email, chat, and social media. Without a unified view of these interactions, support teams struggle to connect the dots and customers end up with a fragmented experience. 

Solution: Omnichannel monitoring 

Aircall’s omnichannel monitoring capabilities brings all customer interactions into one place. Integrating data from calls, emails, messages, and chats gives you a comprehensive view of each customer journey. With that visibility, your team can deliver a frictionless and connected experience for your customers.

Key features to look for in call center analytics software

When choosing call center analytics software, prioritize features that optimize performance, improve customer experience, and provide actionable insights. 

Here are some must-have features to look for:

Unlock the power of AI with Aircall to improve coaching, save time, and enhance the customer experience. Learn more.

How Aircall helps you turn call center analytics into actionable insights

88% of customer experience trendsetters consider personalization critical for meeting rising customer expectations. But without the right tools, all that customer data can’t translate into meaningful action.

Aircall’s call center analytics features give you the visibility to act in the moment. You can use Call Monitoring and Whispering to coach agents during live conversations, improving performance without disrupting the customer experience. AI-powered insights generate call summaries, detect sentiment, highlight trending topics, and automate CRM updates so every interaction turns into actionable intelligence.

With Aircall, you can track KPIs for individual agents and teams, filter call center data to surface the insights that matter, and measure progress toward your business goals. Customizable dashboards make it easy to spot opportunities and adjust strategy in real time.

Also, Aircall integrates with more than 200 CRM and business tools, creating a seamless omnichannel experience across phone, chat, email, and messaging. No matter how customers reach out, your team has the context to deliver consistent, personalized support.

Discover Aircall’s powerful analytics features for a call center that maximizes agent productivity and customer satisfaction. Try for free.

Frequently asked questions

How do you analyze data from a call center?

To analyze data and ensure quality assurance in a call center, managers track metrics like handle time, customer satisfaction, and call resolution rates. Analytics software gathers this data from interactions and integrates it with other systems to reveal patterns and areas for improvement.

With real-time monitoring, predictive insights, and detailed reports, managers can make data-driven decisions to enhance service quality and streamline operations.

What are KPIs for call centers?

KPIs (Key Performance Indicators) for call centers measure success in areas like efficiency and customer satisfaction. Common call center KPIs include average call time, conversion rate, missed call rate, first-call resolution, and customer satisfaction. These metrics help call centers assess agent performance, improve CX, and achieve operational goals.

What are call data analytics?

Call data analytics examine customer interaction data, including metrics like call duration, volume, and resolution times, to understand performance and customer behavior. Analytics also use advanced technology for sentiment analysis and predictive insights. This analysis helps call centers refine service, improve processes, and achieve business goals.

Published on December 30, 2024.