Conversation Analytics: A Necessity for AI-Enabled Customer Service
While conversation analytics (CA) is one of the earliest AI-enabled contact center products, the application is currently experiencing a renaissance driven by generative and agentic artificial intelligence. Companies find CA to be helpful on its own in capturing and identifying customer insights, but with AI technology advancements, these solutions should be able to more rapidly provide insights and deliver business outcomes that generate measurable benefits for customers, agents, and the bottom line.
CA Is Mission-Critical for Contact Centers
Organizations expect CA solutions to deliver insights into why customers reach out to them for assistance. A solution’s ability to surface valuable intel is one of the major differentiating factors in this highly competitive sector. Basic CA solutions, which identify the key words and phrases, display these findings in dashboards; more advanced solutions use this data to enable agents, supervisors, and analysts to take actions and kick off workflows to automatically improve their operations.
Conversation analytics technology is a foundational component for many AI-enabled applications that have been introduced into contact centers or enhanced in recent years, including automated quality management (AQM), real-time guidance (RTG)/next best action (NBA), and automated post-interaction summarization. However, the combination of genAI and large language models (LLMs) have improved the accuracy of CA, making its findings and insights more practical and useful, although guardrails are necessary to reduce the risk of hallucinations and bias. The recent addition of agentic AI to CA, which adds the ability to autonomously make decisions and recommendations and take actions based on these insights, signals the future direction for these solutions.
All verticals will benefit from and ultimately adopt these solutions. Healthcare, which was somewhat slow in applying CA, has become one of the more progressive use cases, though strict guardrails are necessary for this privacy- and accuracy-sensitive industry. Collections operations is another industry realizing significant benefits from CA capabilities and related products.
Trends Driving the Growth of the CA Market
Companies seek technologies that reduce their reliance on live agents and other employees, while enabling them to deliver a more personalized and outstanding customer experience (CX). This is not just a preference but a business necessity, as the demands on CX organizations to scale continue to increase, as does the cost of human agents. AI is the answer, and CA is one of the foundational technologies that will play a pivotal and influential role in the future of contact center technology and applications.
Conversation analytics is a required technology for modern contact centers and any organization that needs visibility into its CX and customer journey. Its use cases are broad and varied; CA can make contributions within contact centers, as well as to many other enterprise functions and departments. This has become a fast-moving and innovative sector, with the ability to reinvent itself in the next three to five years as vendors incorporate emerging AI capabilities, such as agentic AI, to deliver autonomous intelligence and automation into operating environments.
The contact center market is in transition, and CA-enabled applications will be instrumental in its evolution. GenAI-powered CA is significantly improving traditional CA solutions and related applications. AQM provides statistically valid insights into department performance by automating the identification of agents, procedures, and systems in need of coaching or improvement. Even better, when agentic intelligence is added, AQM solutions can automate the delivery of targeted and personalized training to agents and measure its effectiveness. And in the future, AI is likely to make the system changes to fix broken systems and procedures, with humans in the loop. GenAI-enhanced CA provides the intelligence to fuel RTG and NBA applications. While it’s still the early days of rolling out these applications to contact center agents and back-office employees, adoption of guidance tools is expected to greatly improve response accuracy and speed. The union of CA with genAI and customized LLMs vastly improves transcription and speech-to-text capabilities, which are enablers of automated post-interaction summarization. Vendors are now releasing enhanced applications that identify follow-up activities to kick off and automate with agentic AI.
These applications are already in the market, and dozens of vendors provide some or all of these capabilities. But this is just the beginning of the innovation that AI-enabled CA is projected to bring to the market in the next three to five years. CA will be a core technology component of many future AI-enabled capabilities. As CA is a fundamental building block for contact centers and is necessary to gain insights required to improve the CX and customer journey, it’s a great way to get started with AI.
CA Headwinds and Challenges
Conversation analytics solutions, particularly those enhanced with genAI, are rampant in the contact center market, but having a solution and using it properly are two different things. Enterprises are under enormous pressure to use AI to improve productivity, reduce staff, cut operating expenses, and improve the customer experience, but achieving these goals has proven more challenging than most executives expected.
When it comes to CA, its primary use continues to be providing insights into why customers reach out to an enterprise’s contact center or customer service department. While this information is useful, it’s rarely actionable by itself and has not generated the results that managers hoped it would. It’s time for CA solutions to become more outcome- and action-oriented so they deliver benefits, not just findings in attractive charts. For this to happen, CA automation platforms must incorporate genAI and agentic AI. The vendors also need to introduce key performance indicators that measure and quantify the solution’s realized benefits.
Final Thoughts
GenAI-enabled CA solutions are mission-critical for enterprises for their ability to accurately identify insights into why their customers interact with them and to share this information on a timely basis, increasingly in real time. GenAI-enriched CA technology has quickly become a foundational AI component for many of the newer applications entering contact centers, including RTG/NBA, automated post-interaction summarization and the ability to identify the follow-ups, and more. During the past two years, dozens of vendors from many IT segments, including AI providers, hyper-scalers, contact center-as-a-service (CCaaS) providers, workforce engagement management (WEM) providers, system integrators, consulting firms, and more have come to market with these capabilities. While it’s great for enterprises to have a large variety of product options, the solutions and their results are highly varied.
CA solutions need to be an integral and interoperable component of an enterprise’s CX, AI, and automation platforms to deliver actionable findings and outcomes that enrich how enterprises treat their customers and employees. To that end, companies must define their CX and AI strategies and identify the applications they need to achieve their short- and long-term objectives.
Donna Fluss, founder and president of DMG Consulting, provides a unique and unparalleled understanding of the people, processes, and technology that drive the strategic direction of the dynamic and rapidly transforming contact center and back-office markets. As the foremost analyst and visionary dedicated to the contact center and back-office markets, Fluss has provided expert guidance for more than 30 years to technology leaders as well as disruptive newcomers, investors, and enterprises that want to build next-generation AI-enabled contact centers. She can be reached at Donna.Fluss@dmgconsult.com.