Customer interaction analytics spurs better business results
Collect, dissect and act on customer interaction data. Integrate it with different company departments, especially the C-suite. And watch out for privacy issues.
Customer interactions are brimming with valuable intelligence. Savvy customer experience leaders and other business executives use this data to drive CX improvements in operational efficiency, sales effectiveness and other initiatives. Such potential makes conversation and interaction analytics one of the most compelling technologies for the contact center today.
All customer interactions are applicable, whether they occur between human agents and customers or AI agents, bots and customers.
First, organizations should gather raw and unstructured customer interaction data, such as text and audio. This includes transcripts of customer service calls, text messages from chatbot interactions, email, social media direct messages, video and screen-sharing sessions and call recordings. Next, make that data available for analysis, with the goal of understanding what was said, how it was said and why it was said.
This is where conversation analytics, the use of software and machine learning, including natural language processing, comes in. Applying conversation analytics enables companies to derive business value from their conversation data; they analyze it to extract meaning, insights and quantitative metrics.
Gather, analyze and act on customer data
In a CX context, conversation analytics identifies sentiment, intent, behavior patterns, agent performance and other crucial factors in near real time. Conversation analytics, in other words, is the process of turning raw customer conversation data into meaningful, structured and actionable knowledge.
Conversation analytics is the process of turning raw customer conversation data into meaningful, structured and actionable knowledge.
Of course, just gathering and analyzing the data isn't enough. Acting on it is imperative.
Historically, however, Metrigy's research has shown significant drop-offs from the percentage of companies gathering customer data to those analyzing it and, especially, those acting on it. Fortunately, the gap is starting to shrink. In a 2024 CX study, 50% of companies that gathered data also acted on it. In 2025, that percentage grew to 59.1%.
In part, the change is fueled by the rise of conversation and interaction analytics, which has shifted from a contact center innovation to a strategic asset used across departments such as customer service, marketing and sales.
Nearly 50% of companies already use interaction analytics, and another 29.9% plan to implement the technology this year, according to Metrigy's "AI for Business Success 2025-26" global study of 1,104 companies. Based on these results, it's clear to see that customer interaction analytics is quickly becoming an imperative for business success.
Customer interaction data becomes vital
The rise of generative AI large language models (LLMs) has propelled customer interaction analytics into the realm of a corporate-wide strategic asset.
LLMs analyze large volumes of unstructured data, including call transcripts, recordings, videos and screen sharing at a rapid clip to produce meaningful insights. LLMs also uplevel deliverables with interactivity, meaning business leaders can access static intelligence and query those insights in real time. CX leaders and other company executives can dive into the data, ask questions to refine findings or drill down into the root cause of a trend, ultimately resulting in actionable advice.
No surprise, then, that Metrigy research participants agree that customer interaction data is a critical source of business intelligence for their organizations. About 90% of companies using conversation analytics consider customer interaction data the most important, or among the most important, data available to them for improving business metrics. Specifically, 39.4% rate it as "vital," and 51.2% rate it as "important."
Conversational data transcends contact center
Contact center supervisors are the chief beneficiaries of the insights from conversation data. With the data, contact center managers learn how agents are performing, identify skills gaps and uncover next-best actions, such as assigning hands-on training or AI coaching.
But the interest in and reliance on conversational insights extends beyond the traditional contact center. Sales, product management, technology, security and compliance are among the other roles that can tap into the intelligence from conversational data.
Here are examples of how these roles can use the data:
Sales leaders. Evaluate metrics like word choice, time spent listening versus talking and customer sentiment for greater sales pipeline accuracy, improved close rates and more targeted coaching, especially for effective conversion and upsell techniques.
Product managers. Determine product updates and revisions that meet customer demand and have the biggest effect on revenue and customer satisfaction (CSAT).
Technology analysts. Examine channel usage, application efficiency and service interactions to correlate patterns with CSAT, providing insights to optimize digital experiences and inform IT strategy.
Security and compliance leaders. Identify potential data leakage, inappropriate communications and capture personally identifiable information.
C-suite wants access to customer interaction data
Interest in customer interaction data is extending all the way to the top of the corporate ladder, so to speak. In Metrigy's AI study, more than 80% of companies said that analyzing customer interaction data should be a standard component of C-suite decision-makers' performance dashboards. For the highest-performing companies, based on measured business improvements, this conviction is even higher at 96.7%.
Interest in customer interaction data is extending all the way to the top of the corporate ladder.
With access to a conversation analytics-powered dashboard, executives can proactively address emerging issues. For example, if analysis reveals a spike in complaints about a product defect, a dashboard alert could inform customer service, communications and marketing executives in real time so they can swiftly coordinate a fix before viral social media posts cause financial and reputational problems.
Today, companies are primarily applying interaction analytics to customer service and sales conversations. For some, this includes using analytics to compare human agent and AI agent performance. For 46.2% of companies, this benchmarking of effectiveness, quality and outcomes is the single most valuable use of the technology.
Customer analytics poses cost and security caveats
As interest in conversational analytics extends to different roles, the types of conversations analyzed expand beyond customer interactions. For example, one emerging and valuable use case is analyzing internal employee conversations to uncover workflow bottlenecks and improve collaboration. Conversational analytics could expand to IT, HR and frontline employees to uncover operational inefficiencies, identify high and low performers and assess campaign outcomes.
However, as with all things AI -- whether companies are implementing conversation analytics for customer service solely or more broadly -- there are caveats. For example, successfully using conversation analytics requires access to customer interaction data in a secure way that doesn't compromise privacy.
Additionally, companies must scrutinize the cost of implementing conversation analytics and managing related expenses as use expands. As conversation analytics increasingly drives strategic business decisions, they must ensure that organizational processes are in place to guide proper use.
When implemented with proper due diligence, customer interaction analytics can be a game-changer, no doubt. It's clear that investing in conversation analytics is no longer merely about listening to calls; it's about extracting actionable intelligence that drives tangible business improvements. By using the power of generative AI and making conversation data visible to the C-suite, organizations are equipped to make faster, data-based decisions, leading to higher CSAT ratings, improved agent efficiency and increased revenue generation.
Beth Schultz is vice president of research and principal analyst at Metrigy. She focuses her research on unified communications, collaboration and digital customer experience.
