Conversational Analytics Software: Top Picks for 2025

The blog highlights the importance of conversational analytics software, which uses AI technologies like NLP and Machine Learning to analyze customer interactions in real-time. It emphasizes how businesses can gain actionable insights from voice and text data, improving customer service, agent performance, and decision-making. The blog also reviews top platforms such as OvalEdge, which integrates data governance with conversational analytics to drive operational efficiency and smarter decision-making.

Every missed customer call, chat, or email could be hiding valuable insights that could boost your team’s performance, and you might not even know it yet.

Far too many support, sales, and contact-center teams still rely on outdated methods like manual data checks, static dashboards, or basic keyword searches, which only skim the surface of what’s truly happening in their business data. These methods miss the depth of insights needed to drive better decision-making.

That’s why conversational analytics software matters now. Instead of analyzing customer conversations, these tools allow teams to perform analytics through natural-language conversations with AI. Using advanced technologies such as Natural Language Processing (NLP) and Machine Learning (ML), conversational analytics platforms let users ask questions in simple language and instantly receive data-backed insights. 

In fact, according to Grand View Research, the global conversational AI market is expected to grow from USD 14.3 billion in 2025 to USD 41.39 billion by 2030, reflecting strong demand for tools that allow people to access insights through intuitive, conversational interfaces.

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In this blog, we’ll explore how conversational analytics software works, dive into the key technologies that power these tools, and look at some of the top platforms in 2025, such as OvalEdge, that can help teams turn natural-language questions into actionable business intelligence.

How conversational analytics software works

Conversational analytics software enables users to explore and analyze data simply by asking questions in natural language. Instead of relying on dashboards or writing SQL queries, users can type or speak their questions, and the system interprets the intent, retrieves the right data, and presents insights instantly.

These platforms use AI and NLP to understand user queries, map them to governed data sources, identify relevant metrics, and generate accurate insights. This empowers teams to improve decision-making, accelerate workflows, and democratize access to analytics across the business. Key benefits include real-time querying, self-service insights, and simplifying analytics for non-technical users.

Conversational analytics software supports various industries, including operations, sales, customer service, and analytics teams, by making data insight accessible and easy to interact with.

Core technologies: NLP, ML, voice & chat analysis

Conversational analytics software relies on several key technologies to interpret questions and generate accurate insights. The primary technologies powering these tools include Natural Language Processing (NLP), Machine Learning (ML), and conversational interfaces (voice or chat-based input).

Each of these technologies plays a unique role in enabling data exploration through simple language:

• Natural Language Processing (NLP): Enables the software to understand, interpret, and process human language. With advances in AI-driven sentiment analysis, a recent study reported a sentiment classification accuracy of 89.7% on large e-commerce conversation datasets, surpassing traditional methods in accuracy.

• Machine Learning (ML): Helps identify patterns, auto-generate insights, recommend relevant metrics, and continuously improve query understanding over time.

• Voice & Chat Analytics: Allow users to interact with the system using conversational formats, such as typing or speaking questions, making analytics accessible even to non-technical users.

Together, these technologies enable conversational analytics platforms to process user intent and deliver accurate insights in an intuitive, human-centric way. 

Data‑sources and integration

One of the significant advantages of conversational analytics platforms is their ability to connect with a wide range of enterprise data sources. Whether the data lives in CRM systems, databases, warehouses, operational tools, or BI platforms, conversational analytics tools consolidate and query all of it through a unified interface.

This ensures that no insight remains siloed and allows businesses to gain a holistic view of their operations.

According to McKinsey’s 2025 report, 88% of organizations are already using AI in at least one business function, with sales, customer service, and analytics being among the top adopters of conversational AI technology.

For example, a business user might ask, “What were our top-performing regions last quarter?” and the system automatically pulls data from multiple sources, applies governance rules, and returns an accurate answer, without manual data gathering or cross-platform checks.

From raw conversation to actionable insight: Key steps

The power of conversational analytics lies not in analyzing customer conversations but in transforming user-initiated conversational queries into actionable insights. Here are the key steps involved in the process:

• Understand the query: The system interprets the user’s natural-language question.

• Map to data: It identifies the correct datasets, metrics, and definitions using governance rules.

• Process & analyze: NLP and ML models determine the appropriate analysis or visualization.

• Visualize insights: Dashboards, charts, or written explanations present insights clearly.

• Act on insights: Teams use these insights to improve decision-making, optimize workflows, or automate routine analytics tasks.

In this way, conversational analytics software turns natural-language questions into strategic insights that drive operational efficiency, faster decisions, and better business outcomes.

Top 6 conversational analytics tools & platforms in 2025

1. OvalEdge

OvalEdge is an AI-powered data governance and analytics platform that simplifies data management while enabling businesses to leverage conversational analytics. With its unique askEdgi feature, users can query governed enterprise data in plain language, removing the need for complex coding. This makes it easy for both technical and non-technical users to access, explore, and analyze their data.

OvalEdge combines powerful data governance with conversational, agentic analytics, allowing users to interact directly with their data and generate insights without switching tools or writing queries. In addition to askEdgi, the platform offers a suite of governance, cataloging, and automation features that streamline data discovery, analysis, and action

Its AI-driven approach simplifies analytics and drives smarter decision-making with real-time insights. By integrating data governance with conversational analytics, OvalEdge enables businesses to break down silos and gain a unified, comprehensive view of their operations. Whether improving self-service analytics or managing data governance, OvalEdge offers a complete solution to enhance data intelligence and operational efficiency.

Key strengths:

• Natural language querying: OvalEdge's "askEdgi" allows users to interact with data using natural language, making analytics accessible to everyone, regardless of technical background.

• Integration capabilities: It integrates with a wide variety of data sources, including CRM systems, databases, and analytics tools, providing a unified view of all enterprise data.

• Self-service analytics: Empower your team by allowing non-technical users to generate insights independently, reducing reliance on data experts and improving agility.

• AI-driven insights: OvalEdge’s AI-powered features deliver advanced analytics that help businesses uncover hidden trends and patterns, enhancing decision-making processes.

• Comprehensive data governance: The platform ensures data consistency, quality, and security across the organization, helping businesses maintain full control over their data assets.

• Instant insight generation: By enabling fast, conversational analysis through askEdgi, OvalEdge helps teams act quickly on insights and make timely operational decisions.

• Customizable dashboards: OvalEdge offers customizable dashboards that allow users to view and interact with their data in the most relevant and actionable format for their needs.

• Scalable for growth: Whether you’re a small team or a large enterprise, OvalEdge scales effortlessly to meet evolving data and analytics needs, ensuring long-term value.

Best fit: OvalEdge is a perfect fit for enterprises or organizations looking for a unified platform that combines governed data, self-service analytics, and conversational (natural-language) analytics. Its ability to streamline data processes and provide actionable insights makes it ideal for businesses aiming to improve decision-making and operational efficiency.

If you're ready to see how OvalEdge can transform your data management and analytics, book a free demo today and experience the power of AI-driven insights.

2. Tellius

Tellius is an AI-powered analytics platform that enables users to explore data, run analysis, and uncover insights through natural-language conversations. Instead of relying on complex dashboards or SQL queries, users can ask questions directly in plain English and receive clear explanations, visualizations, and actionable insights. Its focus on automated insight discovery and guided NLQ makes Tellius a strong fit for organizations looking to modernize how teams interact with their data.

Key strengths:

• Tellius offers natural-language querying, allowing users to ask questions conversationally and receive instant data insights.

• Its AI engine automates insight discovery by highlighting trends, anomalies, and drivers behind key metrics.

• The platform supports a wide range of data sources, delivering a unified and governed analytics layer.

• Users benefit from guided suggestions, helping them explore data they may not know to ask about.

• Built-in explainability ensures transparency by showing how answers were generated and which data was used.

Best fit: Tellius is ideal for organizations seeking a conversational, AI-driven analytics solution that empowers both technical and non-technical users to conduct meaningful exploration without writing queries or relying on analysts.

3. Zoho Analytics

Zoho Analytics is a self-service BI and analytics platform that enables conversational analytics through its AI assistant, Zia. Users can interact with Zia in natural language to generate reports, dashboards, and insights without requiring technical expertise. The platform combines automated analytics with a strong visualization engine, making it suitable for teams looking to simplify and scale data access across the business.

Key strengths:

• Zia, Zoho’s conversational AI assistant, allows users to ask data questions and receive visual insights instantly.

• The platform integrates with hundreds of data sources, ensuring consistent and centralized data access.

• Its automated insights feature highlights trends, correlations, and anomalies without manual analysis.

• Zoho Analytics includes robust collaboration tools, enabling teams to share insights and work together seamlessly.

• Advanced customization options allow users to tailor dashboards and reports to their specific workflows.

Best fit: Zoho Analytics is best suited for organizations seeking an accessible, conversational analytics layer on top of a modern BI platform, especially those already using Zoho’s ecosystem or looking for a cost-effective analytics solution.

4. SCIKIQ NLQ

SCIKIQ NLQ is a conversational analytics solution designed to make enterprise data accessible through natural language. The tool enables users to ask questions across governed datasets and receive accurate, context-aware insights instantly. Its emphasis on data governance, semantic understanding, and explainability makes it a strong fit for organizations that prioritize trust and compliance in analytics. 

Key strengths:

• SCIKIQ supports natural-language questions with context-driven, accurate responses based on governed data.

• Its semantic layer ensures queries map to the correct data definitions, reducing ambiguity and errors.

• The platform includes explainability features that show how answers were generated and which data sources were used.

• SCIKIQ integrates with diverse enterprise systems, providing a consistent analytics experience across teams.

• It offers out-of-the-box connectors and strong data lineage support for compliance-focused organizations.

Best fit: SCIKIQ NLQ is ideal for businesses that require governed, explainable conversational analytics and want to democratize data access without compromising accuracy or compliance.

5. Qlik Sense

Qlik Sense is a modern data analytics platform that supports conversational analytics through AI-augmented exploration. While known for its associative analytics engine, Qlik also enables users to interact with data using natural language, making it easier to uncover insights without advanced technical skills. Its combination of guided analytics, automation, and broad data integration makes it a powerful enterprise solution.

Key strengths:

• Qlik’s conversational interface allows users to ask questions in natural language and generate insights instantly.

• The associative engine uncovers hidden relationships in data that traditional query-based tools often miss.

• AI-assisted analysis provides automated insights into trends, drivers, and anomalies.

• Qlik integrates with a wide range of data sources, creating a unified analytics environment.

• Its highly customizable dashboards and visualizations support advanced analytics workflows across teams.

Best fit: Qlik Sense is best suited for enterprises that need a scalable analytics platform combining conversational querying, automated insights, and robust visualization capabilities.

6. Tredence

Tredence offers AI-driven analytics solutions that incorporate conversational interfaces and agent-like capabilities to help users interact with complex data environments more intuitively. With a focus on data democratization, automation, and enterprise-scale analytics, Tredence enables teams to run analyses through natural language and receive actionable guidance powered by intelligent AI systems.

Key strengths:

• Tredence supports conversational analytics through natural-language interfaces for exploring enterprise data.

• Its AI-driven solutions automate key analytical tasks and accelerate insight generation.

• The platform integrates seamlessly with large data ecosystems, supporting advanced use cases in operations, supply chain, sales, and more.

• Tredence emphasizes explainability and trust, ensuring analytics outputs are transparent and enterprise-ready.

• Strong consulting and implementation capabilities help organizations build custom conversational analytics experiences.

Best fit: Tredence is ideal for large enterprises seeking a blend of conversational analytics, AI-driven automation, and industry-specific analytical solutions that scale across business functions.

Key features to look for in the best conversational analytics software

When choosing conversational analytics software, it’s important to focus on the features that will provide the most value to your business.

Gartner forecasts that by 2029, agentic AI will handle about 80% of common customer‑service issues autonomously, driving up to a 30% reduction in operational costs. This is a clear indicator of the operational leverage enabled by conversation analytics and automation.

Here are the key capabilities you should look for to make sure you’re selecting the right tool.

Real‑time conversational insights & alerts

Real-time insights are crucial when you need to stay on top of customer interactions. Look for a tool that provides immediate feedback and sends alerts for high-risk conversations. This allows your team to step in and resolve potential issues before they escalate, improving the overall customer experience. With conversational analytics, you should be able to interact with live data and make decisions in real time rather than analyzing past interactions.

Sentiment, intent & theme detection

A good conversational analytics tool should help you understand the sentiment, intent, and recurring themes in your customer interactions. These insights offer a deeper understanding of customer emotions and needs, allowing your team to respond more effectively and make decisions based on real-time conversations, not just past analysis.

Agent performance scoring & coaching

To improve performance, you need to be able to score and compare both agents and bots. The best software can highlight best practices, identify common objections, and pinpoint areas for improvement. This enables managers to provide focused coaching that drives better results by leveraging real-time data, helping agents improve their performance as conversations unfold.

Omnichannel data capture & unified dashboards

Your software should capture data from all communication channels, including voice, chat, email, and social media. OvalEdge excels in this area, offering seamless integration with a wide range of platforms, allowing businesses to track insights from all customer interactions in one unified dashboard. This makes it easier to act on live data, track insights dynamically, and ensure a consistent experience across all customer touchpoints

Integration and scalability considerations

It’s crucial that your conversational analytics tool integrates seamlessly with existing systems like your CRM, chatbots, and contact center platforms. For instance, OvalEdge is designed to scale as your business grows, integrating with multiple data sources and easily accommodating new communication channels without compromising performance. This ensures that your tool evolves with your business needs, providing the flexibility to work with both structured and unstructured data in real-time.

Conclusion

If you're struggling to make sense of all the customer interactions flooding your systems, you’re not alone. Many businesses still miss the opportunity to unlock valuable insights from customer conversations, which can lead to missed growth and optimization opportunities. 

The right conversational analytics software can change that, turning everyday conversations into actionable data that drives better decisions. OvalEdge solves this problem by offering a powerful combination of data governance and conversational analytics. With features like askEdgi, it enables teams to interact with data using simple, natural language, no coding required.

OvalEdge integrates seamlessly with your existing systems, making it easy to capture, analyze, and act on real-time insights from voice, chat, and email interactions. This allows you to enhance customer service, improve agent performance, and optimize your workflows with ease.

Ready to see how OvalEdge can help you get more out of your customer conversations? Book a free demo today to transform your data into actionable insights.

FAQs

1. What is the difference between conversational analytics and traditional analytics?

Conversational analytics uses AI and NLP to analyze customer voice and text interactions in real-time, while traditional analytics typically focuses on structured data from sources like surveys, sales data, and web traffic for retrospective analysis.

2. Can conversational analytics software integrate with CRM systems?

Yes, most conversational analytics tools integrate seamlessly with CRM systems, enabling businesses to link conversation data with customer profiles, providing a unified view to enhance relationship management and delivering personalized customer service based on real-time insights.

3. How does conversational analytics improve customer service?

By analyzing customer interactions across multiple channels, conversational analytics identifies pain points, monitors agent performance, and tracks satisfaction trends. This enables businesses to optimize workflows, improve support responses, and deliver better customer experiences with actionable insights.

4. Is conversational analytics only for call centers?

No, conversational analytics can be used in various industries and departments, including sales, marketing, product development, and customer service. It analyzes interactions across chatbots, emails, social media, and live chats to improve customer engagement and drive business growth.

5. What industries benefit the most from conversational analytics?

Industries such as retail, telecommunications, banking, healthcare, and technology benefit the most from conversational analytics. These sectors rely on frequent customer interactions and feedback, which conversational analytics helps to enhance engagement, improve services, and gain deeper business insights.

6. How secure is conversational analytics software?

Most conversational analytics software adheres to strict industry security standards, including data encryption, access controls, and compliance with regulations like GDPR. This ensures the privacy and protection of sensitive customer data, particularly in high-risk sectors such as banking and healthcare.