Contact Center Pipeline August 2025 | Page 11

RESEARCH

Many contact centers that use AI-enabled analytics are doing so to improve first-contact resolution, with 42 % stating that improving FCR and having fewer repeat calls is their primary KPI focus for analytics: this is wise, as FCR is a strong driver of customer experience.

AI analytics is increasingly used for the auto-categorization of calls, which is very useful as a starting point to investigate issues, processes or products which are driving repeat calls, as well as other negative outcomes such as complaints or customer churn.

Having automatic categorization take place at the end of the call means that agents not only spend less time on postcall work, but that any call disposition codes are more likely to be consistent and accurate: there are many large contact centers which offer more than 50 disposition codes to agents, few of whom will have the time or inclination to select the one most appropriate, always assuming in any case that every agent would judge the same call in the same way.

The following chart shows that insight from analytics is most widely used within the contact center itself, and is seen to generally be a useful way to improve KPIs and indeed CX.

It is interesting to note that this insight is used much less outside the contact center, with 25 % of organizations using analytics simply not sharing this information with other business departments. Even in cases where insight is provided outside the contact center, as many respondents said that it was ineffective as said it was very effective. This lack of cross-functional engagement represents a real missed opportunity to transform the contact center from a cost center to a strategic hub of customer intelligence.

This may be due to a number of reasons: interdepartmental communication and politics may play a part, as well as the non-contact center functions getting less involved in the initial process of deciding what to look for. This is currently a wasted opportunity for many organizations, and ways of gathering and sharing this insight across departments should be considered by senior leaders.

WHERE IS ANALYTICS INSIGHT USED, AND HOW EFFECTIVE IS IT?

Analytics can also help organizations to identify which key performance indicators( KPIs) are actually most important to their business by correlating various performance and operational benchmarks against required business outcomes, such as understanding which operational KPIs and / or agent behaviors are linked with high levels of contract renewals or NPS scores.

KEY FINDINGS FROM THE REPORT

• Understanding Contact Reasons Many contacts are avoidable. AI-enabled analytics reveals the causes of repeat calls and failed self-service, enabling upstream fixes and cost savings. Only 42 % of U. S. contact centers report having a detailed understanding of failure demand, which is a significant blind spot.

• Sentiment Analysis Goes Deeper AI can now detect not just keywords, but also tone, volume and pace to assess emotional intensity. This allows for deeper understanding of what triggers customer frustration or delight, insights that can reshape product / service design and process improvements.

• Competitive and Product Intelligence Speech and text analytics help identify when competitors are mentioned or when products underperform. Businesses can act faster to tweak pricing strategies or fix friction points in the user journey, using real customer feedback.

• Real-Time Anomaly Detection Beyond dashboards, AI systems can alert teams in real time to emerging issues( e. g., website errors or billing glitches), helping prevent crises before they escalate.

FIGURE 1

• The Rise of Discovery and Root Cause Analytics Rather than relying on human hypothesis, discovery tools automatically surface patterns in data, pinpointing causes of dissatisfaction and service breakdowns without predefined search terms.

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