CallMiner Streamlines Use of Customer Interaction Data

Speech analytics systems are designed to automatically analyze and extract meaning and insights from spoken language. These systems use technologies such as natural language processing and machine learning to analyze and understand spoken language, and are becoming increasingly sophisticated and effective at handling a wide range of languages and accents. Speech analytics supports a variety of applications, including customer service, market research and language processing.

As artificial intelligence continues to improve, the range of use cases in contact centers and customer support are branching out to include agent coaching and evaluation, along with ways to save time for agents by automatically creating note summaries and reducing after-call work. By automatically analyzing customer interactions, organizations can identify common customer issues and questions, using the information to develop more effective responses and solutions. This can help reduce the time and effort required to resolve customer issues, leading to a better overall customer experience.

CallMiner provides interaction analytics and customer engagement software that uses natural language processing and machine learning techniques to extract and analyze meaning from spoken language and text sources. The main offering in this space is the Eureka platform for speech analytics. The company has focused on expanding the range of data that can be analyzed and creating an analytic toolkit specifically for tackling key contact center challenges, like avoiding customer churn or improving agents’ sales skills.

One of the main benefits of this kind of AI-based analysis is that it provides a way to use the significant data about customer interactions and feedback, which historically has been so difficult to manually gather and analyze that many organizations don’t bother (or can’t justify the resources for it). By making the collection and analysis of customer data automatic, businesses can identify trends and patterns that may not be apparent from individual interactions. We assert by 2026, more than one-half of contact centers will capture real-time transcripts of calls to help with coaching and training and provide automated agent assistance.

CallMiner’s applications are useful in multiple contexts within an organization. Marketers will find it effective to have direct access to voice of the customer data to understand how a brand is performing. In addition to using data to measure agent performance, CallMiner’s tools can provide contextual guidance in real time. The Eureka platform is not a stand-alone contact center operational tool, but connects to many common routing platforms.

CallMiner is likely to move forward by continuing to extend the use cases deeper into customer support environments. This could potentially take the form of deeper agent guidance and next best action features as well as more flexible summarization of interactions. As more organizations come to rely on analytics as the centerpiece of CX efforts, vendors like CallMiner will be considered for providing insight into interactions that can be used to correct negative concerns and maximize positive experiences.