The complete guide to call center analytics software for 2026

Your contact center handles thousands of conversations every week—phone calls, chats, emails—and buried in all that noise are the answers to your biggest questions. Which issues drive customers to reach out again and again? Where do agents struggle and succeed? 

The problem is that you can’t manually review every interaction, so you’re stuck sampling a fraction of conversations, making decisions based on gut feelings with a sprinkling of limited data mixed in. 

Call center analytics software changes this—turning unstructured conversation data into actionable insights about what drives call volume and where to focus your efforts for maximum impact.

In this guide, you learn what call center analytics software actually does and a breakdown of the top tools reshaping the space in 2026. Plus, you get a practical framework for evaluating which solution best fits your company’s needs.

Key insights

• The best analytics platforms connect conversation data to the customer experience, helping you understand what agents say and why customers contacted you in the first place

• Look for contact center analytics platforms with pretrained models to get results in weeks, not months

• Choose a solution that handles human and AI agent conversations equally well, since AI usage is becoming increasingly common in call center operations

What is call center analytics software? 

Call center analytics software uses AI and natural language processing (NLP) to analyze customer conversations across phone, chat, email, or SMS. Instead of manually sampling a small fraction of calls and hoping you catch the important parts, the software automatically reviews all of your conversations—flagging trends and spotting coaching opportunities you’d otherwise miss. 

Call center analytics software lets you see how every interaction measures up to your quality criteria, track sentiment in real time, and surface emerging issues before they become major problems.

Here’s how these platforms work: 

• Collects data across channels: voice, chat, and email conversations flow into a single platform for clear visibility

• Analyzes conversations automatically: speech analytics and NLP detect sentiment like frustration or excitement, and identify patterns that show why customers contact you and how well your team responds

• Predicts what’s coming next: predictive analytics gives you early warning signs of customer churn and helps you prepare your agents for the types of customer experience issues that are on the rise 

📣 In case you missed it: Contentsquare acquired call center analytics platform Loris AI to help you bridge traditional online journeys with human and AI-powered conversations. Watch the video below to get to know Contentsquare’s Conversation Intelligence, powered by Loris.

Top 5 call center analytics platforms in 2026 

The call center analytics platform you choose depends on your priorities—whether that’s a focus on the broader customer experience or on quality monitoring. 

Here are 5 stand-out tools, each with distinct strengths for different use cases.

1. Contentsquare’s Conversation Intelligence, powered by Loris

Conversation Intelligence analyzes 100% of interactions across phone, chat, and email using pretrained AI models built specifically for customer service conversations—not generic language models that miss the nuances of support interactions. Conversation Intelligence’s capabilities include: 

• Surveyless Voice of Customer (VoC): no low-response-rate surveys here. Get insights from actual customer service interactions, including contact drivers and customer sentiment.

• Auto Quality Assurance (QA): consistently assess every interaction against your quality criteria and track policy adherence over time 

• AI Agent Analytics: understand bot performance with key metrics about their strengths and gaps. Track auto-resolution rates, call transfers, and abandonment patterns—and then use those insights to improve prompts and training.

Conversation Intelligence delivers measurable improvements. Loris customers have achieved a 30% improvement in average handle time, a 20% increase in CSAT, and a 35% reduction in appeasement spend.

💡 Pro tip: take your analysis one step further by using Conversation Intelligence alongside Contentsquare’s other digital analytics tools—think session replays that show you customers’ frustrated clicks or heatmaps that show you where users engage. 

You get a complete view of the customer experience, from the friction point on your checkout page that’s driving calls to the user experience (UX) issues that agents keep having to explain manually. 

Use Contentsquare Heatmaps to get important context about user journeys to use alongside Conversation Intelligence

2. CallMiner 

CallMiner has spent decades specializing in voice analytics. It can track sentiment and keywords—as well as alert agents and supervisors to issues.

Features to expect with CallMiner include:

• Real-time alerting: support agents in the moment by spotting upsell opportunities or escalation needs

• Risk reduction: remove personally identifiable information (PII) from call recordings 

• Generative AI assistance: find agent and call information faster with contact summaries

Beyond these features, CallMiner works well for enterprises with expansive automation and outreach needs. For example, it offers complex AI agentic workflow automation and automated customer outreach.

3. Qualtrics CX for Contact Centers

Qualtrics unifies feedback from contact center interactions into a single dashboard, giving you a comprehensive view of customer sentiment across all channels. The platform combines traditional survey data—like CSAT and first-call resolution—with AI-powered analytics to help you spot dips in satisfaction before they become critical. 

Qualtrics CX for Contact Centers offers capabilities like: 

• Automated quality management: use a selection of pre-built AI models to evaluate script adherence and empathy, with automatic compliance monitoring

• Predictive churn modeling: flag customers at high risk of leaving so you can intervene as soon as possible

• AI-powered coaching: generate personalized improvement recommendations for each agent based on their interaction history with customers

Qualtrics CX for Contact Centers is part of a bigger suite of products, offering tools for surveying customers and employees. It works well for large enterprises needing a call center analytics tool that works well with expansive internal and external survey tools.

👉 Did you know? Qualtrics is even better when integrated with Contentsquare. When reviewing customer feedback in Qualtrics, click a button to view the relevant session replay in Contentsquare and get the complete context of the customer journey. In Contentsquare, you can also create a segment for a specific Qualtrics feedback score to learn how satisfied customers might behave differently.

4. Level AI

Level AI uses LLMs adapted to call centers to improve operational efficiency and agent performance. Some of Level AI’s standout features include:

• Automated conversation summaries: get an instant overview of customer interactions to spot customer issues and agent coaching opportunities

• Centralized coaching management: manage all coaching sessions in a single place with a one-click feature sharing

• Custom reporting and analytics: create reports based on your company’s goals and integrate insights with your other analytics tools for a more complete picture of your progress

Level AI’s LLM-based approach to conversation analysis works well for companies looking to speed up quality assessments with features like AI-powered summarization.

5. Observe.ai

Observe.ai combines AI agents for customer interactions with post-interaction analytics for quality monitoring and coaching.

Some of Observe.ai’s top capabilities include:

• Multi-modal analysis: synchronize inputs like audio, transcripts, and screen recordings to see interactions from your agents’ perspectives

• Voice-first AI agents: route conversations with natural-sounding AI agents that can transfer calls to a human with full context

• Manual QA acceleration: spot key moments in conversations faster so humans can complete complex evaluations faster 

Observe.ai works well for companies in highly regulated industries, due to its detailed compliance-monitoring features, as well as those needing screen-level visibility into agent workflows. For example, in financial services, the platform flags calls where a rep struggles to explain loan options.

💡 Pro tip: get even more benefits out of your call center analytics platform by combining conversational intelligence with journey analysis. 

According to McKinsey, 71% of customers switch between channels mid-journey—starting on your website, then calling support, and then heading to mobile to complete their purchase. With each new contact, valuable time and context get lost, frustrating agents and customers alike. 

When you connect conversation data to digital journey analytics, you see the full story. For example, Contentsquare’s Journey Analysis tool shows you what users did on your site before calling support. (Did they get stuck on a form? Search unsuccessfully for help?) 

Then, Conversation Intelligence shows what they told your agent and why they reached out. Together, these valuable insights help you fix the root cause—a confusing checkout flow, for example—so fewer customers need to call in the first place.

Conversation Intelligence shows you why customers reached out and how that affected their experience

How to evaluate call center analytics software

Not every contact center has the same priorities—a financial services team will likely weigh compliance differently than a retail brand focused on reducing handle time. 

Before you spend time conducting product demos, get clear on what success looks like for your organization. Then, use these criteria to find a product that works for you: 

• Check channel coverage: confirm the platform supports the mix of channels—such as voice, chat, and email—that your customers use the most

• Review the platform’s track record: some AI models are new, while others have been tested and proven extensively. (Loris has delivered billions of predictions in more than 500 customer service interactions!)

• Verify integrations: make sure the tool connects to your customer relationship management (CRM) platform and ticketing tools without requiring heavy custom integrations

• Assess time to value: look for platforms with pretrained models that give you insights in weeks (not months of custom development and training)

• Evaluate insight quality: ask for examples of how the platform handles the features you’ll use the most, such as sentiment analysis, root-cause analysis, or agent performance

Conversation Intelligence lets you break down agent performance by both the agent’s actions and how customers reacted

Customer conversations = your competitive advantage

Contact center analytics show you exactly how things are going for your call center agents—and how you can help improve the experience for both agents and customers. 

The result? Faster resolutions, data-driven decisions, better coaching, higher customer satisfaction, and lower costs. 

FAQs about call center analytics software