Conversational intelligence solutions transcribe customer conversations and spotlight insights that allow businesses to improve products, services, and customer experience.
According to Opus Research, 49 percent of organizations say using a conversational intelligence solution has helped them support customer satisfaction.
Recognizing this success, more businesses are implementing such solutions and trialing many new use cases – from tracking new metrics to pinpointing customer journey pain points.
Nowadays, the possibilities are seemingly endless, and in this roundtable, four industry experts share their favorite emerging use cases for the tech. They are:
- Frank Sherlock, VP of International at CallMiner
- Dvir Hoffman, CEO of CommBox
- Tatiana Polyakova, COO of MiaRec
- Tapan Patel, Senior Director of Go-to-Market & Product Marketing at Verint
In addition, they share their favorite success stories and predictions for the future of the conversational intelligence market below.
Share Your Favorite Use Case for Conversational Intelligence In CX.
Real-Time Agent Alerts
Sherlock: Today’s leading conversation intelligence tools have deep AI and machine learning capabilities that identify customer emotions, from anger and dissatisfaction to happiness and joy, and use those indicators to drive better customer outcomes.
For example, by detecting a happy customer during an interaction, agents can be alerted in real-time to potential up-sell opportunities – driving loyalty, retention, and lifetime value. Inversely, a frustrated customer could be quickly routed to a supervisor, reducing churn.
Additionally, conversation intelligence can detect subtle cues, emotional shifts, and patterns that indicate potential concerns or desires of customers in a single interaction.
These insights can drive action, such as identifying agent behaviors to improve coaching.
Monitoring Customer Intents & Behaviors
Hoffman: Conversational AI can analyze customer intents and patterns to ensure live and virtual agents respond to customers with hyper-accurate, personalized information.
For example, a conversational intelligence solution can identify if a customer requires a specific document during an automated interaction. That information may then pass through to a bot connected to the organization’s CRM via integration, which can send the relevant document to the customer and deliver seamless service.
This process can be managed end-to-end, without involving human agents, saving time without compromising on tailored support.
Significantly, conversational intelligence can also identify patterns faster – or better than an agent could – which means they can identify and offer the customer relevant opportunities, upsells, or recommendations. That ensures the customer feels valued and no business opportunity is missed.
Contact Center Quality Assurance (QA) Automation
Polyakova: By leveraging Conversational Intelligence tools, businesses can meticulously analyze interactions between agents and customers, ensuring adherence to predefined quality standards and identifying areas for improvement promptly.
The technology enables organizations to better understand customer interactions, uncovering patterns, trends, and sentiment that may influence overall satisfaction.
Additionally, automated quality management streamlines the evaluation process, reducing manual effort and increasing efficiency.
Through the integration of conversational intelligence, businesses can also enhance agent training programs, refine reward & recognition strategies, and ultimately elevate the CX by fostering consistently high-quality interactions.
Supporting CX and Compliance In Highly Regulated Industries
Patel: I see significant potential for conversational AI in the life sciences and healthcare industries driven by enhanced quality of care and cost reductions.
Going beyond member self-service, companies can enhance patient experience with 24/7 medical information, drug interactions, health reminders, and adverse events reporting to automate and deliver better containment and conversational experiences.
With advancements in Large Language Models (LLMs) and Retrieval Augmented Generation (RAG), these use cases can understand and respond to natural language with information and knowledge that is custom or specific to an industry or domain.
The latter is key to improving a conversational AI application’s accuracy, performance, and explainability in regulated industries like life sciences and healthcare.
Share a Case Study of a Brand That Implemented a Conversational Intelligence Solution to Great Effect.
A Financial Service Company Spotlights “Save Attempts” to Improve Retention
Sherlock: To deliver data-driven insights to clients, the BPO team at NTT leverages conversational intelligence to understand its clients’ omnichannel voice of the customer (VoC).
One NTT client in the financial services industry showed robust customer retention rates. Yet, the company was experiencing unusually high cancellation rates for credit card accounts and had difficulty understanding why.
Running a conversational intelligence initiative, NTT helped the client pinpoint areas within the call flow where agents could make “save attempts”.
By embedding coaching in these “save attempts”, NTT’s client experienced an eight percent improvement in their customer retention rate that sustained for over ten months.
Altshuler Shaham Auto-Summarizes Customer Conversations
Hoffman: Leading financial service provider Altshuler Shaham struggled with time-to-resolve, impersonal responses, and low agent productivity.
Consequently, customers experienced poor support, and Altshuler Shaham lost existing and potential customers due to missed leads.
To resolve this, CommBox overhauled its customer outreach.
Indeed, Altshuler Shaham integrated CommBox AI into all chat functions, which enabled 24/7 instant support for customers.
Moreover, the company enhanced agent productivity with 100+ work hours saved monthly by AI conversation summarization and a 20 percent reduction in operational costs.
Finally, it automated – via CommBox’s AI chatbot on native platforms like WhatsApp – the process of offering detailed investment information to customers before they connect to live agents.
As a result, Altshuler Shaham recorded a 760 percent growth in new customers and a 540 percent increase in incoming leads.
Sym-tech Dealer Services Automates Contact Center QA
Polyakova: Sym-tech Dealer Services, a provider of automotive Finance and Insurance solutions, encountered challenges in evaluating agents and obtaining comprehensive visibility into their performance.
Meanwhile, manual evaluations were both time-consuming and limited in scope.
To address this, they implemented a conversation intelligence solution to automate QA and drive more efficient, detailed, data-driven analysis.
Consequently, efficiency increased, and performance evaluations became more accurate across all customer calls.
Moreover, by utilizing AI-powered automated evaluations, Sym-tech pinpointed areas for improvement, enhancing agent training programs and overall customer experience.
As Victoria Johnston, Director of CX Operations at Sym-tech Dealer Services, stated:
MiaRec Automated call quality evaluation scorecards will replace hours of manpower spent by several team leads performing these call evaluations manually. It will also provide a truer agent performance rating since all calls are rated, not only the ones that are randomly selected.
What’s Your Number One Prediction for the Future of Conversational Intelligence?
AI to Augment Contact Center Agents Thrives
Sherlock: The most effective and impactful applications of AI will be to augment and support agents to be better in their roles.
As consumers, we’ve all experienced a scenario when an AI-powered chatbot fails to meet our expectations, and we want to contact a live agent.
Rather than attempting to replace the agent’s role entirely, generative AI, automation, and – of course – conversational intelligence will most effectively supplement existing workflows. These aim to benefit the agent, customer, and broader business.
Using AI to remove mundane contact center tasks allows agents to focus on up-skilling their capabilities, empowering them to tackle increasingly complex issues and ultimately providing better customer experiences and outcomes.
Conversational Intelligence Enables a “Near Self-Sufficient Support System”
Hoffman: The future of conversational AI and intelligence lies in how it will support and be supported by generative AI to promote a near self-sufficient support system at scale.
Already, this is happening. The CommBox AI chatbot leverages conversational and generative AI to measure customer sentiment and uses this analysis to inform responses and action pathways, like generating a unique return label.
In the future, this will become supercharged as AI analyzes patterns to better predict behaviors and proactively reach out to customers – perhaps before the issue even occurs.
Also, conversational intelligence could enhance emerging technologies – like augmented reality and visual assistants – and their ability to strengthen real-time customer engagement.
Conversational Intelligence Spreads Across the Business
Polyakova: As conversational intelligence continues to automate repetitive tasks and extract actionable insights from customer interactions, the data will transcend departmental silos.
After all, with a deeper understanding of customer needs and preferences, businesses can tailor their offerings, optimize sales strategies, and cultivate lasting customer relationships.
Moreover, conversational intelligence will enable cross-departmental collaboration, ensuring that insights gleaned from customer interactions inform not only sales and marketing efforts but also product development, customer service enhancements, and strategic decision-making.
As a result, companies will be better equipped to drive revenue growth, foster customer loyalty, and maintain a competitive edge in dynamic markets.
Such a shift will mark a significant evolution in the role of contact centers, positioning them as integral drivers of organizational success rather than mere operational functions.
Generative AI (GenAI) Will Further Augment Conversational Intelligence Solutions
Patel: Large language models (LLMs) and GenAI will advance their impact on conversational intelligence to improve virtual agent performance, CX automation, and self-service experiences.
Customers will leverage GenAI to identify and generate content – e.g., responses, intents, and entities – by learning from repositories of curated, domain-specific sources without explicitly training the models – as is typically done in a conversational AI platform.
Of course, this raises concerns around bias, hallucination, and the accuracy of bot-human interactions. As such, companies will continue to invest in data and AI governance to mitigate risks.
In parallel, interest will grow in a streamlined and unified orchestration engine that coordinates across AI models, systems of record, channels, and services used in multiple virtual agents to achieve their stated goals.
Miss out on our previous CX Today roundtable? Check it out here: Contact Center Automation: Trends, Tricks, and Tools
