Customer Conversation Analytics: Why Conversation Data Is Your Most Valuable CX Asset
Most organizations are sitting on the single most valuable asset they have: their customer conversations. And yet, they’re treating them like a by-product of the contact center instead of a strategic reserve. Verint’s Global VP of AI and Analytics Daniel Ziv sat down with Metrigy’s VP of Research and Principal Analyst Beth Schultz to discuss industry trends, the importance of customer conversation analytics, and why interaction data is the new “data oil.”
Key takeaways
Interaction data isn’t just a contact center asset anymore: the majority of IT and CX leaders now rank it among the most valuable data they have.
Companies that treat customer conversation data as vital to the business outperform their competitors, seeing revenue growth, cost reduction, and CSAT increase.
Generative and agentic AI now helps companies extract valuable customer insights in minutes instead of weeks, and act on them immediately.
The biggest risk isn’t using AI on customer data but not using it at all.
What is “data oil” in customer experience?
Behavioral data is everything a company knows about its customers, including every call, email, chat, survey response, as well as the agent and channel performance data that shaped the outcome. Companies aren’t short on this data, but many of them are short on refining it.
In a recent Verint webinar, we brought together Beth Schultz, VP of Research and Principal Analyst at Metrigy, and Daniel Ziv, Verint’s Global VP of AI and Analytics, to make the case that interaction data is the new “data oil”, and that the technology to finally refine it at scale has arrived.
Mining that data, just like mining oil, you have to refine it, you have to turn it into different products, you have to ship it, you have to sell it. All that’s the refinery and that’s where Verint comes in with our Open CX Platform.
This refining process is fundamentally changing how we should treat behavioral data. Speech analytics solutions have existed for decades, but they were predominantly rule-based and slow. Companies had to define what churn looked like before they could even start looking for it. By now generative and agentic AI have removed that bottleneck, allowing users to ask a direct question and get an answer, sometimes with the next steps already scripted out.
How customer conversation analytics drives revenue and cost savings
A recent Metrigy research study called AI’s Role in Customer Experience: 2026-27, drawn from 759 companies globally, clearly shows that companies in the success group stand apart from the non-success group in how they treat interaction data, and the gap just keeps widening.
The most successful companies in this study lean toward thinking about customer interaction data as being vital (…). They do so two times more than the non-success group.
Compared to the non-success group, the study’s success group spends 3.5 times more on interaction analytics, is nearly twice as likely to have a dedicated Center of Excellence, and is 1.6 times more likely to prioritize contextual AI. The latter gives them the ability to carry understanding across an entire customer interaction rather than simply answering in isolated, generic fragments.
The payoff shows up directly in the numbers:
• 19% minimum revenue growth,
• 14.7% average cost reduction,
• and a 25% average CSAT increase among the success group.
Three real-world examples of AI-powered interaction analytics ROI
Even in 2026, too many contact centers still deliver fragmented, disconnected customer journeys, which leaves customers feeling unheard and unvalued. That perception is costly: 79% of customers will switch brands after just one bad experience. And the irony is that this kind of churn is largely avoidable, and fixing it not only can increase CSAT, but also save companies millions in revenue opportunities.
Learn more about the latest customer trends from Verint’s The State of CX 2026 report
During the webinar, Daniel walked us through three live Verint deployments that show just how much is possible once companies stop sitting on their data oil and start mining it.
• A call summarization rollout using Verint Wrap Up Bot automated the after-call work that agents dislike most and saved one customer $70 million per year after scaling from a 300-agent pilot to 30,000 agents.
• Verint Coaching Bot, which helped agents respond faster and more accurately, drove $9 million in savings across a 2,000-agent deployment.
• Our GenAI-powered analytics tool, Verint Genie Bot, found $6.5 million in potential revenue for one customer in just two days (!) by surfacing upsell and cross-sell opportunities buried in existing interaction data.
And the best part is that these are not just experiments or AI pilots, but real, proven business outcomes.
Balancing AI automation and the human touch in the contact center
Nevertheless, none of these outcomes is an argument for full automation. Metrigy’s business and consumer research shows a real gap between what businesses assume and what customers actually want: 43% of businesses believe consumers prefer AI agents, but only 17% of consumers agree. (Metrigy, AI’s Role in Customer Experience: 2026-27)
Even if you’re going to be presenting an AI agent to a customer, always enable them to either immediately go to a human or escalate to one anywhere along the process.
The strategic move, according to both Beth Schultz and Daniel Ziv, is using interaction data to determine which conversations belong to AI and which need a human touch — and building the option to switch seamlessly into every experience. Letting customers know early in their journey that they can switch to a human agent, if needed, goes a long way toward earning their trust.
Following Daniel Ziv’s forecast, three years from now, companies that haven’t started mining their behavioral data may not be competitive at all. The leaders won’t be the ones who used AI to cut costs, but the ones who used it to compound what they could offer, sell, and solve. The data reserve is already on the property. The question is whether you’re going to build the refinery.
Learn more: Join our upcoming Verint master class on CX intelligence
Ready to see what insights are hiding in your own customer conversations? Learn how to turn customer conversations into impactful ROI with Verint CX Intelligence from our upcoming Verint Master Class episode: How to Turn Customer Conversations into Impactful ROI with CX Intelligence.
Register today and mark your calendar for October 14, 11 a.m. ET / 4 p.m. BST to see how leading brands are already turning conversation data into revenue opportunities, churn prevention, and measurable business outcomes, and how you can best take advantage of your interaction data.
Frequently asked questions
Customer conversation analytics is the practice of using AI to analyze the calls, chats, emails, and survey responses a contact center already collects, and turning them into insight about why customers contact you and what to do about it.
“Data oil” refers to the behavioral data companies collect through customer interactions, including calls, emails, chats, survey responses, and related agent and channel performance data. Like crude oil, this data is a valuable raw asset, but it only creates value once it’s refined through interaction analytics and AI into actionable insights.
According to Metrigy’s 2026-27 research, AI’s Role in Customer Experience: 2026-27, companies that treat interaction data as a vital business asset see measurably better outcomes than those that don’t. These companies also invest more in interaction analytics and are far more likely to prioritize contextual AI.
No. The research and real-world deployments discussed in the webinar point to a hybrid approach instead. While AI can handle summarization, coaching, and insight generation at scale, less than 20% of consumers prefer AI-only interactions, even though almost half of businesses assume they do. The recommended strategy is to use interaction data to route conversations appropriately between AI and human agents, while keeping the option to switch to a person.
In the three Verint deployments highlighted in the webinar, one company saved $70 million a year after scaling a call summarization rollout to 30,000 agents, another saved $9 million across a 2,000-agent deployment using an AI coaching tool, and a third identified $6.5 million in potential revenue in just two days using GenAI-powered analytics to surface upsell opportunities.
