Enhancing customer service through conversational AI

 Startek Editorial
Startek Editorial

Apr 26, 2023 | 6 min read

Conversational AI is a rapidly growing field that has revolutionized the way businesses interact with customers. From chatbots to virtual assistants, conversational AI technology allows for seamless communication between humans and machines.67% of U.S. millennial internet users would purchase a product or service from brands using a chatbot as they transform your customer’s way of purchasing. 

What is conversational AI

Conversational AI has transformed the way we engage with technology. It enables machines to understand and respond to human language and enter into meaningful conversations with people. Conversational AI can change how businesses interact with their customers, streamline communication and automate tasks, resulting in cost savings and increased efficiency, as AI can deliver a better experience for the customer and for the agent. Conversational AI, also known as chatbots or virtual assistants, is a type of artificial intelligence that enables machines to conduct human-like conversations with users. It uses natural language processing (NLP) and machine learning algorithms to understand and respond to user input in real-time.

The primary goal of conversational AI is to mimic human conversation as closely as possible so users can interact with it as they would with another human being. These systems can be used for a wide variety of purposes, from customer service to personal assistants to retail/e-commerce, travel/hospitality /cable/media/telecom

Conversational AI is an umbrella term for a variety of technologies that allow machines to simulate human-like conversations. This includes natural language processing (NLP), machine learning (ML) and deep learning (DL) algorithms, which enable machines to interpret, analyze and respond to human language. Conversational AI systems can be implemented through voice assistants, chatbots or virtual agents.

How does conversational AI work?

A conversational AI system is built on a dataset that the system learns from. This dataset includes a wide range of human language inputs, such as text messages, emails and voice recordings. This data is then used to train the system's NLP and ML models, which enable it to understand and interpret human language. Once the system is trained, it can begin to interact with users. 

When a user interacts with a conversational AI system, their input is processed by the NLP model. This model analyzes the user's input, breaking it down into individual components such as keywords and phrases. This analysis enables the system to understand the user's intent and respond accordingly. The ML model is then used to generate a response based on the user's input and the system's training data, using a set of rules that the system has learned over time, which enable it to generate contextually appropriate responses.

Conversational AI works by using natural language processing (NLP) and machine learning algorithms to understand and generate human-like responses to text or speech input.

The process typically involves three main components:

  1. Input processing: The AI receives and analyzes the user's input, whether it's text or speech. This involves breaking down the input into its component parts, such as individual words, phrases or sentences.
  2. Understanding and reasoning: The AI uses NLP algorithms to analyze the input and understand its meaning. This involves identifying the intent behind the user's input and extracting relevant information, such as names, dates or locations. Based on this understanding, the AI then reasons and decides how to respond.
  3. Output generation: Finally, the AI generates a response using natural language generation (NLG) algorithms. The response can take many forms, including text, speech or a combination of both. The AI can also use machine learning to improve its responses over time, based on feedback from users.

Conversational AI is a complex system that requires a deep understanding of NLP and ML techniques. They are used in a variety of applications, including customer service, chatbots, virtual assistants and more.

Types of conversational AI

  1. Chatbots: Chatbots are one of the most common and well-known uses of conversational AI. Chatbots are computer programs designed to simulate human conversation and provide automated responses to user input. They are often used for customer service, virtual assistants and other applications where users need assistance with tasks or information. These computer programs are designed to simulate conversation with human users through text or messaging interfaces and create the ability for brands to deliver seamless, scalable 24/7 support.
  1. Voice and mobile assistants: These are conversational AI applications that respond to voice commands and can perform a range of tasks, such as setting reminders, making phone calls and sending messages. Voice assistants are AI-powered digital assistants that use natural language processing to understand and respond to spoken commands. Examples include Amazon Alexa, Apple Siri and Google Assistant.
  1. Interactive voice assistants (IVA): These are voice-based conversational AI systems that understand and respond to natural language commands, often used in call centers and customer service environments. These are chatbots designed to assist users through messaging apps or social media platforms. They can be used for customer service, shopping assistance and other applications.
  1. Virtual assistants: These are advanced conversational AI systems that can perform a wide range of tasks, such as scheduling appointments, managing emails and making reservations. They are typically powered by machine learning and NLP technologies, accessed through a variety of interfaces including voice, messaging and chatbots. These AI-powered digital agents can engage in more complex conversations with users, using machine learning algorithms to understand the context and provide personalized responses. They are often deployed for customer service and support.

How conversational AI helps brands enhance their CX  

In today's digital world, customers expect a seamless brand experience and conversational AI offers brands significant opportunities for brands to enhance their customer experience (CX). Conversational AI allows businesses to automate customer interactions and provide quick, personalized responses to their queries, which increases customer satisfaction and brand loyalty.

Personalized conversations: By analyzing customer data and behavior, brands can provide tailored recommendations and solutions to their customers. Personalization leads to a better customer experience, driving customer loyalty and retention.

24/7 availability: Customers can get their queries resolved at any time of the day or night. This leads to improved customer satisfaction as issues are resolved quickly without waiting for customer support during business hours.


Efficient and cost-effective: Conversational AI helps businesses save time and money. By automating customer interactions, brands reduce customer support teams’ workloads, allowing them to focus on more complex queries. This creates efficiency savings.

Omnichannel support: Customers can interact with brands through multiple channels, such as social media, email or messaging apps. Conversational AI can also integrate with customer relationship management (CRM) systems, making it easier for brands to track customer interactions across all channels.

Improved customer insights: Companies can gather valuable customer insights by analyzing customer interactions. This data can be used to identify customer pain points, preferences and behavior patterns. Brands can use this information to improve products and services, leading to increased customer satisfaction and loyalty.

Conversation AI best practices

  • Understand the user: Conversational AI should be designed with the user in mind. Understand the user's needs and preferences to create a personalized experience. This includes gathering data on users’ demographics, behavior patterns and language usage.
  • Be natural and human-like: The conversation should feel natural and flow like a human-to-human conversation. Avoid using technical jargon or language that may be difficult for the user to understand.
  • Use multi-modal interactions: Incorporate multiple modes of interaction, such as voice, text and visuals, to create a more engaging and user-friendly experience.
  • Ensure context awareness: Conversational AI should be contextually aware and able to understand the user's intent even when there are colloquialisms, typos or other variations in language.
  • Provide feedback and guidance: Provide the user with clear feedback and guidance throughout the conversation, especially when the user is uncertain or confused.
  • Use machine learning and NLP: Utilize machine learning and natural language processing (NLP) techniques to improve the accuracy of the system's responses over time.
  • Continuously monitor and improve: Monitor the system's performance and user feedback to identify areas for improvement and optimize the conversation flow.
  • Respect user privacy: Ensure that user privacy is respected and their data is protected. Be transparent about the data collected and how it is used.

Conversational AI is a rapidly growing field that is transforming the way we interact with technology. By leveraging natural language processing and machine learning, conversational AI systems can understand and interpret human language and generate contextually appropriate responses. As these systems continue to learn and adapt, they have the potential to become increasingly personalized and intuitive, enabling seamless communication between humans and machines. Conversational AI is transforming the way brands interact with their customers. By providing personalized, 24/7 support, improving efficiency, reducing costs and providing valuable customer insights, conversational AI is enhancing the customer experience and driving customer loyalty. Brands that adopt conversational AI will have a competitive advantage over those that do not, as they will be able to provide a superior customer experience in a digital-first world.

Contact us today to learn how conversational AI can deliver positive outcomes for your brand.

Related posts


Self-service enhances the cable and media customer experience

Discover the latest self-service trends in cable and media and how they’re revolutionizing the cable and media customer experience

May 2023

Read more


Technology unlocks CX success in travel and hospitality

Discover the top three ways technology elevates travel and hospitality CX and help brands stay on top

May 2023

Read more


Five ways to strengthen and recession-proof your CX strategy

Learn how to maintain quality CX during a recession with limited resources, leveraging technology and a robust CX strategy

May 2023

Read more


How augmented reality enhances the retail customer experience

Read the blog to discover why augmented reality is playing an essential role in enhancing retail customer experience

May 2023

Read more


Building a multilingual CX strategy

Having a multilingual CX strategy is crucial for global success. Our blog discusses the benefits and challenges of developing one

May 2023

Read more


Transforming CX with contact center knowledge management

The article outlines the benefits of knowledge management for enhanced customer experience

May 2023

Read more
View all

Stay connected

Please enter a valid email address.
Please select the checkbox before submitting the registration form.
Thank you for sharing your details. We look forward to keeping in touch.