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We live in an era of digital transformation where governments and brands are utilizing new technologies to communicate with customers. Consumer desire for omnichannel services that are available 24 hours a day, seven days a week has increased the need for automated or self-service operations.

Chatbots can help businesses meet these ever-evolving customer needs. However, a typical source of dissatisfaction for people who interact with bots is that they do not always understand the context of conversations. Infact, according to a report by Search Engine Journal, 43% of customers believe that chatbots need to improve their accuracy in understanding what users are asking or looking for.

This is where conversational AI can be implemented to make chatbots more sophisticated and effective at understanding and responding to human language. In this guide, we’ll walk you through what conversational AI is, how it works and how you can get started.

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What is conversational AI?

Conversational AI combines natural language processing, machine learning and other related algorithms with traditional software like chatbots and voice assistants to engage users in human-like conversations. Conversational AI agents get more efficient at spotting patterns and making recommendations over time, as you build up a larger corpus of user inputs.

Gartner predicts that by 2021, 50% of businesses would spend more, per annum, on conversational AI than on traditional mobile app development. Conversational AI is enabling businesses to deliver more personal experiences to their users by having more fluid and intelligent conversations. Artificial intelligence, with its capabilities of comprehending natural language, active learning, dialogue flow management and data mining, helps transform and automate end-to-end user journeys.

Components of conversational AI

Conversational AI combines machine learning (ML), natural language processing (NLP) and other components to understand and respond in a natural manner.

1. Machine learning

Machine Learning (ML) is a branch of artificial intelligence that consists of a collection of algorithms, features and data sets that improve over time. As the amount of data collected increases, the AI platform improves its ability to recognise patterns and make predictions.

2. Natural language processing

Natural language processing is a field of artificial intelligence wherein unstructured data is converted into a computer-readable format, which is then processed to generate a suitable response. These NLP processes interact with machine learning in a continual feedback loop, allowing AI algorithms to develop over time. NLP comprises four major steps to understand human language.

a. Input generation

Users offer input through a website or an app, and the format of the input can be either speech or text.

b. Input analysis

If the input is text-based, the conversational AI solution will discern the meaning of the input and determine its intent using natural language understanding (NLU). If the input is voice-based, it will use a combination of automatic speech recognition (ASR) and natural language understanding (NLU) to interpret the data.

c. Dialog management

Natural Language Generation (NLG), a component of NLP, formulates a response at this level.

d. Reinforcement learning

Finally, machine learning algorithms improve accuracy over time by refining responses.

3. Data mining

Another major part of artificial intelligence is data mining, which focuses on data analysis through unsupervised learning. Its characteristics are similar to those of machine learning, however, while machine learning focuses on making predictions based on current data, data mining is a technique for discovering unknown attributes.

4. Automatic speech recognition (ASR)

ASR is a component of AI used for voice-based conversations. ASR enables the bot to understand human voice inputs, filter out the background noise, use speech-to-text to deduct the query and simulate a human-like response. There are two types of ASR softwares – Directed dialogue and natural language conversations.

Directed dialogue is a simpler version of ASR that is capable of answering basic yes/no questions. Whereas, natural language conversations are more complex and improved versions of ASR that simulate actual human conversations.

How conversational AI works?

Conversational AI combines a variety of technologies to understand, react and learn from every encounter, including automatic speech recognition (ASR), natural language processing (NLP), advanced dialog management (ADM) and machine learning (ML).

1. Natural Language Processing (NLP)

NLP is responsible for correcting spelling, identifying synonyms, interpreting grammar, recognising sentiment, and breaking down a request into words and sentences that are easier to grasp for the virtual agent.

2. Intent analysis

A number of deep learning and machine learning models take over once the request has been prepared using NLP. Natural language understanding (NLU) refers to a set of techniques that allow conversational AI to determine the correct intent (or topic) of a request and extract additional information that can be used to trigger additional actions, such as context, account preferences and entity extraction.

3. Response generation

Now that the request has been fully comprehended, it’s time to respond to the user. The programme then uses dialog management to build the answer based on its comprehension of the text’s intent. Natural language generation (NLG), which is another element of NLP, orchestrates the responses and converts them into a human-understandable format.

The application then either sends the response in text or utilizes speech synthesis or text to speech (TTS) to convey it over a voice modality.

Last but not least, there’s a part that’s in charge of learning and developing the module over time. This is known as machine or reinforced learning, and it occurs when an application accepts corrections and learns from the experience in order to provide a better answer in subsequent interactions.

Why are businesses investing in Conversational AI?

Conversational AI — such as virtual assistants, chatbots, and conversational user interfaces — is a top priority investment for businesses, according to Gartner. Companies of all sizes and shapes are investing more and more resources in conversational AI. But why?

Let’s look at the top 11 benefits Conversational AI has to offer for enterprises.

  1. Boost customer engagement
  2. Enhance lead generation.
  3. Reduce customer service costs
  4. Increase first contact resolution
  5. Lower resolution times
  6. Self-serve up to 80% queries
  7. Improve agent productivity
  8. Personalize conversations at scale
  9. Add a human touch to automation
  10. Exceed customer expectations.
  11. Achieve support scalability

Conversational AI vs Chatbots

Chatbots can be powered by conversational AI to grow smarter and more powerful. However, it’s vital to note that conversational AI isn’t used by all chatbots.

Traditional chatbots are only capable of doing a limited number of functions. In most cases, this entails answering basic FAQs. Chatbots require conversational AI to improve their ability to interpret human language and deliver more personalized, two-way user interactions in order to meet the needs of modern customers.

Use cases of conversational AI across different industries

1. Conversational AI in healthcare

COVID-19 has expedited the adoption of digital healthcare solutions by healthcare systems and organizations around the world. The ‘digital front’ has become the ‘only front’ for patients to receive clinical services in many nations.

Conversational AI solutions enable healthcare providers to handle and offer correct, timely responses to a large number of service requests and customer support enquiries at the same time, making life easier for patients, doctors and other medical staff.

conversational AI for customer service is the most common health care application of AI, some others are appointment booking, data collection, payment processing, sending medical reminders, etc.

2. Conversational AI in BFSI

Conversational AI in banking and the financial services industry is increasingly gaining popularity. The global chatbot in banking, financial services and insurance (BFSI) market was valued at $586M in 2019 and it is expected to reach $7B in 2030, according to a report by NMSC.

From claim processing and credit scoring to risk management and assessment, the potential of AI in the BFSI industry is endless. AI-enabled robo-financial consultants are emerging as the top trend in the industry in 2022.

These are conversational AI bots that can analyze a wide range of structured and unstructured data gathered from social media and personal information, in real-time, to make the right investment recommendations to your clients as well as identify the sectors and companies best aligned to meet their long-term needs and financial goals.

2. Conversational AI in retail and e-commerce

Conversational ai in supply chain management is the most important use case for the retail and e-commerce sector. Since the entire supply chain is internally operated, most companies do not make the effort to streamline the outgoing communication.

Such practices leave the customers with only two options – drop a mail and wait for someone to respond or make a call and hope to go beyond the answering machine. Both these alternatives are arduous and provide a suboptimal customer experience.

Conversational AI solves this problem and gives customers easy and round-the-clock access to information. The conversational AI bot can easily pull data from different sources to give customers the information they need. Now, customers can check the status of their order and track it in real-time without switching channels.

Some other important use cases of conversational AI in the retail and e-commerce sector are personalized product recommendations as well as cross-selling and up-selling.

4. Conversational AI in automobile industry

In order to keep a competitive edge in the market, automobile manufacturers must be prepared to provide a tailored and innovative experience to their customers throughout different stages in their journey.

Use cases of conversational AI in the automotive industry are customer engagement with fast human-like interactions, guiding customers to choose the right vehicle according to their needs and budget and reducing wait-times by providing self-service options.

5. Conversational AI in real estate

The real estate sector is now embracing conversational AI to ensure that enterprises deliver a proactive service to their customers. Conversational AI in real estates helps customers get instant answers to their questions and it turns out, 78% of prospective home buyers stick with the realtor who promptly answers their inquiry.

Besides customer support, AI-powered chatbots can also be used to generate and nurture leads by engaging with prospects. Intelligent chatbots gather and keep systematic records of customer data in real-time so that sales and marketing teams can use it to gather important insights about customer behavior and trends. This will help you close deals faster and make sure each customer gets personalized support.

6. Conversational AI in airlines and travel industry

The global pandemic has highlighted the significance of technology in all sectors. Because the travel industry was the most affected, with an estimated loss of $ 2.4 trillion, it got a chance to bounce back better than ever with the help of AI-powered automation.

Conversational AI is the way of the future for highly efficient travel. It offers a cutting-edge business processing platform for tech-savvy millennials to make travel arrangements on the go. According to the study posted by Travel Daily News, 75% of customers rely heavily on chatbots to make travel arrangements and 66% of respondents claim they find travel chatbots ‘useful’ when organizing business and leisure travel bookings.

7. Conversational AI in telecommunications

Telecommunications companies are gradually expanding their service areas and user bases. While this is good news for them, it also means that they receive a large number of customer inquiries that require 24-hour assistance.

Bots powered by next-gen cognitive technologies can be used to provide the most advanced, intelligent and human-like support at scale and in varied scopes. For example, while simple bots can be scripted to answer FAQs, more advanced bots and virtual assistants can be deployed to recognise customer intentions and hold a conversation by analyzing the relationship between words.This will not only be a huge relief for support staff, but it will also increase customer satisfaction.

8. Conversational AI in SaaS

Conversational AI in SaaS represents an exciting change in how users interact with software. Users can now talk to their software instead of clicking through a series of menus or buttons to get the information they need. This can be accomplished by using voice recognition or chatbot technology.

Conversational AI can be used by SaaS providers to deliver a more personalized user experience. Understanding the user’s unique needs and requirements allows service providers to create a more customized experience. This can help to increase user engagement and encourage users to use the software more frequently.

Conversational AI can also assist providers in gathering data about their customers. This data can help providers improve their software and make it more user-friendly, thereby improving the SaaS solution’s experience.

How to get started with conversational AI?

1. Consider your company’s long-term objectives

What are your company’s goals? Could conversational AI help you achieve those objectives? Make sure you ask the right questions and ascertain your strategic objectives before getting started.

2. Identify your target audience

Employees, customers and partners are just a few of the people your company serves. Which of these groups would benefit the most from conversational AI? It’s easy to lose sight of your aim if you’re not specific.

3. Begin with the end in mind

A good way to get started is to have an end-goal in mind. It is also important to set specific KPIs to measure the effectiveness of your conversational AI tools.

4. Choose the right platform

While you can always build chatbots from scratch, you can also partner with a next-gen total experience automation platform such as Yellow.ai to create a no-code, AI and NLP-powered, multilingual chatbot in just a few clicks.

5. Start building your first bot

Once you have decided on the right platform, it’s time to build your first bot. Make sure you have a well-laid out strategy for the kind of use-cases you want for your bot. The applications of a chatbot can be customized according to the specific needs of your target audience.

How can Yellow.ai help?

With Yellow.ai no-code chatbot building platform, you can develop AI-enabled chatbots without developer dependence. The conversational AI bots that you create on our platform possess world-class capabilities.

Omnichannel customer experience
AI chatbot enables you to provide consistent support over multiple channels that seamlessly connect, allowing customers to pick up where they left off on one channel and continue the experience on another.
Proprietary NLP engine
It empowers the chatbot to truly empathize with your customers and use intent recognition to understand the user intent in every query, no matter how complicated it is.
Proactive multilingual support
Based on AI and ML technologies, the chatbot understands your customers no matter what language they speak, with or without local colloquialism – even when they misspell their words. It identifies the language and quickly adapts the conversation.
Customizable and scalable
You can customize the chatbot to meet the individual needs of your customers and deploy it to over 35+ channels with a single click.
Advanced contextual understanding
The chatbot uses a cutting edge NLP framework that enables them to extract the relevant data from conversations with users, thus making them more sophisticated and contextual for the subsequent conversations.
Live agent hand-off
The chatbot is able to transfer complex queries to a live agent in the most integrated fashion.
Customer Analytics
Chatbot collects customer data in real time which can be used to analyze buying trends and customer behavior. Companies can also use data to identify any gaps in the product or service.
Easy integrations
AI chatbot can easily be integrated to more than 100 APIs and can be seamlessly connected with internal ERP processes. Ready to create your own chatbot? Get in touch with our experts and they’ll help you get started.

Frequently Asked Questions

1. What is an example of conversational AI?

Conversational AI combines NLP and virtual assistance to offer real-time, human-like support to the users. Some examples of conversational AI are chatbots and virtual assistants like Alexa, Siri, Google Assistant, Cortana, etc. These assistants understand natural language and user-intent to offer personalized responses. Unlike traditional chatbots that are capable of answering yes/no questions, conversational assistants can resolve much more complex user queries.

2. What is the difference between Chatbot and conversational AI?

Chatbots are software applications that are used to automate user interactions over text and voice. Conversational AI, on the other hand, empowers traditional chatbots with a new level of sophistication as it enables them to understand human language and form a response using NLP and machine learning. Conversational AI assistants come with reinforcement learning capabilities that help them improve with every interaction.

3. What are different types of conversational AI?

There are primarily three types of conversational AI.

a. Chatbots

Chatbots are software programmes that use messaging to simulate a human-like conversation with the users.

b. Voice-activated bots

Voice bots are AI-powered software that allow a caller to navigate an interactive voice response (IVR) system with their voice, using natural language.

c. Interactive voice assistants

Speech assistants are devices/apps that respond to humans using voice recognition technology, natural language processing and AI.

4. Difference between conversational AI and NLP?

Conversational AI (CAI) is a cutting-edge method of providing a conversational experience using digital technologies that replicate real-life human interactions. It accomplishes this by combining rich data sets, algorithms and linguistic understanding to deliver compelling, multilingual experiences to users across numerous platforms and devices.

Natural Language Processing (NLP), on the other hand, is a crucial component of Conversational AI that helps bots in understanding natural language by breaking down sentence structures in order to provide the most appropriate response

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