Customer interactions can provide a wealth of insight for companies, yet only half of the 1,104 participating companies in Metrigy’s AI for Business Success 2025-26 research had adopted interaction/conversation analytics as of earlier this year. This percent should shift into a large majority by year’s end, with an additional third of companies planning for adoption in 2025.
IT and CX decision makers tempted to delay implementation for other technology priorities should take this study datapoint into consideration: Among those companies already using interaction/conversation analytics, nearly 91% said they consider customer interaction data to be either the most important or among the most important data available to them for improving business metrics.
Examples of how applying interaction analytics to customer service and sales conversations can improve business metrics are plentiful. On the service side, for example, interaction analytics can enable companies to pinpoint the precise point at which customers get frustrated during calls and then use this information to implement targeted coaching for agents and smooth out rough spots causing disruption. Metrics to watch for improvement include agent efficiency and customer satisfaction.
On the sales side, analyzing sales interactions of the top performers could reveal which sales techniques are most effective and how to best persuade customers to make a purchase or accept an upsell or cross-sell offer. Training the entire team to take the same approaches could lead to higher conversion rates and increased revenue.
Bear in mind, too, that interaction analytics doesn’t just apply to human agent-to-customer interactions. In the AI for Business Success study, slightly more than 10% of companies using interaction analytics said they recognize the biggest value to be in analyzing AI agent interactions with customers. From this analysis, companies might discern factors such as how well the AI agent understands customer requests, whether it is speaking in a natural manner, and how often it resolves customer issues without human intervention or completes desired sales actions.
Interaction analytics also allows companies to compare the effectiveness of AI agents vs. human agents. For 46.2% of companies—the highest percent—this is the biggest value to be gained in using interaction analytics. Say a company wants to determine whether AI agents or human agents are more effective at handling customer needs independently. It could do so by analyzing first call resolution for human agents against containment rate for AI agents. Or, on the sales side, it could examine success closure rate vs. successful completion of sales flow, respectively.
Don’t keep customer interaction data internal to the contact center, either. As important as it is in driving improvements in business metrics, most companies agree that customer interaction data should be part of company performance dashboards for executives. Nearly 84% of all companies and an even higher percent of this study’s success group, at 96.7%, believe this is a good idea.
Ultimately, investing in interaction analytics isn’t just about listening to conversations. Rather, it’s about extracting actionable intelligence that drives tangible business improvements, whether manifested in higher customer satisfaction ratings, improved agent efficiency, greater operational efficiency, or increased revenue generation.
