What is Conversational AI? Explore its Benefits, Uses & Implementation for Customer Service
Conversational AI refers to artificial intelligence technologies that can understand user queries and respond in a way that simulates natural conversations. Conversational AI often utilizes underlying technologies like natural language processing (NLP), machine learning, and speech recognition to comprehend and generate human-like responses.
The advent of Generative AI technologies is furthering Conversational AI by having the ability to generate tailored and contextual responses to user queries.
Conversational AI, when used for customer service, can be found in chatbots, virtual assistants, agent assistance aids, messaging platforms, and other applications designed to engage in conversation with users. This article explains conversational AI concepts and emerging usage in customer service. It also discusses how organizations can start using Conversational AI for their business and some of the best practices to maximize user benefits.
Contents
What is Conversational AI
Key differentiators of Conversational AI
Examples of Conversational AI in Customer Service
Benefits of Conversational AI in Customer Service
Getting Started with Conversational AI for Customer Service
Best practices of conversational AI for Customer Service
Conclusions
FAQs
What is Conversational AI
Conversational AI refers to artificial intelligence (AI) that focuses on technology and systems capable of engaging in natural and human-like conversations with users. It leverages natural language processing (NLP), machine learning, and other AI techniques to understand and generate human language, enabling machines to interact with users through text or speech in a conversational manner. Key components and technologies that make up conversational AI include:
Natural Language Processing (NLP): NLP is the technology that enables machines to understand and interpret human language. It involves tasks like text parsing, sentiment analysis, and language understanding.
Speech Recognition: In applications that involve spoken language, speech recognition technology is used to convert spoken words into text, allowing the system to process and respond to voice commands.
Natural Language Generation (NLG): NLG is the process of generating human-like text or speech in response to user input. It's crucial for providing coherent and contextually relevant answers. The advent of LLMs and generative AI has driven significant advancements in this area.
Machine Learning: Machine learning algorithms are often employed to train conversational AI models using large datasets of human-generated conversations. These models learn to understand and generate human language.
Dialog Management: Dialog management systems help maintain a coherent and context-aware conversation. They decide what the system should say or do next based on the current conversation context.
Knowledge Base: Many conversational AI systems are integrated with a knowledge base, which contains information and data that the AI can reference to answer questions accurately.
Key differentiators of Conversational AI
Several salient capabilities differentiate Conversational AI, noteworthy ones include,
Natural Language Understanding (NLU): Conversational AI is designed to comprehend and interpret human language. It can understand context, idiomatic expressions, and varied communication methods, making interactions more human-like.
Contextual Response with Intent Understanding: It can infer intent and maintain context throughout a conversation, allowing for more coherent interactions. Conversational AI can remember previous interactions, keeping track of the information provided, which helps provide more accurate and relevant responses.
Adaptability and Learning: Conversational AI systems often employ machine learning algorithms that learn over time. They can adapt and improve over time based on the data they receive and their interactions, enhancing their ability to respond more accurately and effectively.
Personalization: Conversational AI systems can personalize interactions. They can remember user preferences, past interactions, and behaviors to tailor responses and recommendations.
Task Automation: Conversational AI can perform tasks on behalf of the user. This could range from setting reminders, making reservations or handling customer service inquiries, thereby reducing the need for human intervention.
Continuous Availability: Chatbots and virtual assistants built on conversational AI are available 24/7, providing immediate responses and assistance to users without time constraints.
Scalability: These systems can handle a high volume of conversations simultaneously, making them suitable for scenarios where there's a need to interact with many users.
These differentiators collectively make conversational AI a powerful tool for businesses and customers, allowing for more natural, efficient, and effective human-computer interactions.
Examples of Conversational AI in Customer Service
Customer service and support functions across industries have greatly benefited from the evolution of Conversational AI technologies. The advent of Large Language Models that help with accurate and fast generative AI has furthered the efficacy of Conversational AI for customer service and support. Conversational AI manifests itself in the following popular ways in customer support and service,
Chatbots: Many companies utilize chatbots on their websites and mobile applications to immediately assist customers. For example, a bank might employ a chatbot to handle basic inquiries about account balances, transaction history, etc.
Intelligent Voice Agents: Voice assistants like Amazon's Alexa or Google Assistant are being integrated into customer service workflows. Some companies specialize in building Intelligent Voice Agents for customer service as a specialty - IrisAgent, Omilia, Nuance, Avaamo, Yellow.ai, and Kore.ai are a few that have gained traction.
Agent Assistance: Call center and customer service agents can access relevant content and responses to customer queries using Conversational AI capabilities.
Post-Purchase Support: Conversational AI is employed to assist customers post-purchase. For example, it might help with order tracking return processes or provide additional information about the purchased product or service.
AI-Powered Knowledge Bases: Some customer service platforms use AI to develop and maintain extensive knowledge bases. These AI systems can retrieve relevant information to support customer queries, providing quick and accurate answers to various questions.
Reservation and Booking Systems: In the hospitality and travel industries, conversational AI manages bookings and reservations. These AI systems can assist customers in booking flights, hotels, or rental cars, providing information on availability pricing, and assisting in the reservation process.
Feedback and Surveys: Conversational AI systems are employed to gather feedback and conduct surveys. These systems conversationally engage with customers to collect opinions, reviews, and insights about their experience, which can then be analyzed for improving services.
These applications of Conversational AI in customer service aim to streamline interactions, provide quicker responses, improve overall customer experience, and reduce the workload on human agents for routine or repetitive tasks, allowing them to focus on more complex or critical customer issues.
Benefits of Conversational AI for Customer Service
Conversational AI in customer service offers a wide array of benefits, transforming the way businesses interact with their customers. Key advantages include:
Self-Service: AI-driven systems empower customers to solve their issues independently by providing self-service options, with no need to wait on a call to speak to an agent.
24/7 Availability: Conversational AI enables round-the-clock customer service, addressing queries and providing assistance anytime, even outside regular business hours. This ensures customers can get support whenever they need it.
Improved Response Time: AI-powered systems can provide instant responses to customer queries, reducing waiting times and enhancing the overall speed of service. This swift response contributes to better customer satisfaction.
Personalization: Conversational AI systems can be designed to provide personalized interactions, using customer data and preferences to tailor responses and recommendations. This personal touch enhances the customer experience.
Cost-Efficiency: By automating routine tasks and inquiries, businesses can reduce the workload on human agents. This can lead to cost savings by optimizing human resources and allowing them to focus on more complex or high-value tasks.
Scalability and Accuracy: Conversational AI can handle a high volume of customer inquiries simultaneously without compromising the quality of service. It ensures consistent and standardized responses across all interactions.
Adaptability and Learning: These systems continuously learn and adapt based on customer interactions, allowing them to improve their performance over time. As they gather more data and feedback, they become more effective in addressing customer needs.
Data Collection and Analysis: Conversational AI can collect valuable data from customer interactions, enabling businesses to gather insights and analyze trends. This data can be used to understand customer needs, preferences, and behavior, which is crucial for refining products and services.
Implementing conversational AI in customer service can significantly improve customer satisfaction, streamline operations, and increase loyalty and positive brand perception.
Getting Started with Conversational AI for Customer Service
Most organizations have some level of automation for support. Traditionally, it is an IVR, knowledge base, or self-service/FAQ section. To get started on Conversational AI, we recommend the following approach to build upon what may already be in place,
Define business goals and key use cases: Determine key business metrics, such as improvements in CSAT, call deflection rates, and first call resolution(FCR) rates. Start by identifying specific areas where Conversational AI can make a significant impact on these metrics.
Understand Your Audience: Understand your customer needs, preferences, and pain points. This insight will guide the design and functionality of your Conversational AI solution.
Design Conversation Flows: Design conversation flows by mapping out possible dialogues and user interactions. Prioritize user experience and design conversational flows that are intuitive and user-friendly.
Choose the Right Platform or Tools - Map the use cases to the capabilities that various platforms mentioned in the section above offer.
Compliance and Security: Pay attention to data privacy, security, and regulatory compliance to maintain trust and integrity with users' data.
Measure KPIs and improve: Test for accuracy, usability, and natural language understanding. Incorporate feedback loops to continuously improve the AI's performance based on user interactions and feedback.
Best practices of conversational AI
For customer service best practices using Conversational AI using LLMs, refer to the IrisAgent blog here - How Customer Service Automation Works: Benefits, Practices & Processes (irisagent.com)
Conclusions
Conversational AI has gained significant traction in revolutionizing customer service. With Conversational AI, organizations can streamline interactions, provide quicker responses, improve overall customer experience, and reduce the workload on human agents for routine or repetitive tasks, allowing them to focus on more complex or critical customer issues. Conversational AI, when done right, can significantly improve CSAT, increase call deflection rates, improve agent performance, and increase First Call Resolution (FCR) rates.
Frequently Asked Questions
What is Conversational AI?
Conversational AI refers to artificial intelligence technologies that can understand user queries and respond in a way that simulates natural conversation.
Why should organizations care?
Conversational AI, when done right, can significantly improve CSAT, increase call deflection rates, improve agent performance, and increase first-call resolution (FCR) rates. With these improvements, businesses can drive better financial performance - saving operational costs while improving customer retention and the top line.
What platforms are available for Conversational AI?
Alexa from Amazon, Siri by Apple, Google Assist, and IBM Watson are popular platforms. In addition, there are platforms tailor-made for customer service and support. IrisAgent belongs to this category and we encourage you to sign up and set up a 30-minute discussion to get you started on your Conversational AI journey.
