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.
