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What is Conversational AI? Explained with Example and Use Cases

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What is Conversational AI? Explained with Example and Use Cases

Learn what is conversational AI, how it works and how your organisation can use it to provide delightful customer experiences. 

Conversations are integral to human interactions. They are natural, unique and personal to every individual. Every day, customers are giving businesses many opportunities to interact with them. And they expect the same natural, unique and personalised experiences from them as well. 

While it’s possible to some extent, this experience could not be scaled. At least not until conversational AI came into the picture. 

Conversational AI is an NLP (natural language processing) powered technology that allows businesses to duplicate this human-to-human interaction for human-to-machines conversations. 

  1. What is a conversational AI?
  2. How does conversational AI work?
  3. What is conversational AI used for (examples)?
  4. What are the top benefits of conversational AI? 
  5. What is the key differentiator of conversational AI?
  6. What are some frequently asked questions?
  7. What is the future of conversational AI?
conversational AI

What is Conversational AI?

Definition: Conversational AI is a subset of AI that uses NLP and NLU to help humans communicate with machines through natural, human-like conversations. This is done by understanding their text or speech inputs, identifying the intent behind them, and responding with relevant information. All this with contextual and personalised messaging, similar to human conversations.  

The main components of conversational AI are: 

  • Machine Learning
  • Natural Language Understanding
  • Automatic Speech Recognition 
  • Text-to-Speech and Speech-to-Text

Conversational AI uses these components to interact with users through communication mediums such as chatbots, voicebots, and virtual assistants to enhance their experience. 

In customer support, businesses use conversational AI solutions to help users find relevant information, resolve queries, complete transactions, track order status and do much more – all through natural, contextual, and personalised conversations. 

conversational support

How does Conversational AI Work?

Conversational AI provides quick and accurate responses to customer queries. While it provides instant responses, conversational AI uses a multi-step process to produce the end result. And that’s what we are going to cover in this section. 

Broadly speaking, the process is divided into four steps: 

1: Input Generation

2: Input Analysis

3: Output Generation

4: Reinforcement Learning

Along with the components we covered earlier, we will look at how conversational AI works, step by step. 

how does conversational AI work?

Step 1: Input Generation

The process begins when the user has something to ask and inputs their query. This input could be through text (such as chatbots on websites, WhatsApp, Facebook, Viber, etc.) or voice (such as voicebot and voice assistants) based medium. 

Step 2: Input Analysis

After the user inputs their question, the machine learning layer of the platform uses NLU and NLP to break down the text into smaller parts and pull meaning out of the words. 

Note: if the user is using voice AI to speak with customer support, automatic speech recognition (ASR) is first applied to the voice note to parse the sound into a language the machine can understand. Learn more about how voice AI works here

Once the machine has text, AI in the decision engine (deep learning and neural network) analyses the content to understand the intent behind the query. 

This is where conversational AI becomes the key differentiator for companies. Based on how well the AI is trained (which also depends on dataset quality), it will be able to answer queries covering multiple intents and utterances. More on this later. 

Step 3: Output Generation

Now that the AI has understood the user’s question, it will match the query with a relevant answer. With the help of natural language generation (NLG), it will respond to the user. 

Before generating the output, the AI interacts with integrated systems (the businesses’ customer databases) to go through the user’s profile and previous conversations. This helps in narrowing down the answer based on customer data and adds a layer of personalisation to the response. 

There is a good chance that the AI cannot map the intent with the database. In such cases, it hands over the conversation to an agent. 

As in the Input Generation step, voicebots have an extra step here as well. The AI’s response is converted back from text to speech. The user hears the voice response from the Voice AI, all in real-time. 

Step 4: Reinforcement Learning

This is where the self-learning part of a conversational AI chatbot comes into play. Based on how satisfied the user was with the answer, AI is trained to refine its response in the next interaction. 

With each interaction, businesses get a treasure trove of data full of variations in intent and utterances which are used to train the AI further. Over time, the user gets quicker and more accurate responses, improving the experience while interacting with the machine. 

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What is conversational AI used for (examples)?

Conversational AI is bridging the gap between users and brands by providing delightful customer experiences with every single interaction. 

What started out as a medium to simply support users through FAQ chatbots, today businesses use conversational AI to enable customers to interact with them at every touch point. from finding information, to shopping and completing transactions to re-engaging with them on a timely basis. 

By and large, the top three use cases of conversational AI (CAI) are 

  1. Lead generation – CAI automates customer data collection by engaging users in conversations. These CAI solutions are soon replacing traditional lead generation methods, such as forms, as they see a higher success rate and engagement. 
  2. Customer support – Along with intelligent automation, CAI interacts with customers at different touchpoints to answer their questions. With this use case, Conversational AI is scaling personalised customer engagement. 
  3. Re-engagement – Automated flows allow businesses to re-engage with their customers to send them reminders, updates, notifications, etc. With the help of conversational AI platforms, these messages can be personalised based on customer preferences. 

Every industry applies these use cases based on their need. We will see how different industries use CAI below. 

Conversational AI for customer service and sales

Customers want quick responses from companies 24×7. Conversational AI is enabling businesses to automate frequently asked questions and be available round the clock to support customers. With the help of chatbots and voicebots, CAI empowers customers with self-service options and/or keeps them informed proactively. 

This reduces the load on customer support agents, who can then take up complex queries and deliver delightful experiences. 

Suggested Reading: Faster, Smarter, Better — AI is Improving Customer Service

Conversational AI in retail and eCommerce

Conversational Chatbots allow e-commerce and retail companies to reach out to their customers in real-time and around the clock through two-way conversations. E-commerce companies can provide pre-and post-purchase support, enable catalogue browsing on multiple channels (in addition to the website) and share notifications on shipment, refund and return orders. With conversational AI, companies can retarget abandoned carts and increase sales. 

Suggested Reading: Conversational Commerce: A Complete Guide for Beginners

Conversational AI in real estate

Using a conversational AI platform, a real estate company can automatically generate and qualify leads round the clock. It can collect customer details such as names, email IDs, phone numbers, budget, and locality, and get answers to other qualifying questions. CAI can also hand these leads seamlessly to your agents and close more leads every day. Plus, it can reduce human involvement in scheduling visits, document sharing, EMI reminders, etc. 

Suggested Reading: AI in Real Estate: Build Meaningful Bond with Customers

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Conversational AI for banking and fintech

As consumers move away from traditional brick-and-mortar financial institutions, CAI can help these organisations provide a smooth online banking experience. Financial services can reduce cost per query drastically by using conversational AI for sharing and verifying documents, completing the KYC process online, sending alerts & notifications on bank updates, downtime, payment dues, etc., allowing customers to check bank and credit balances, locating ATMs and banks, etc. 

Suggested Reading: What is Conversational Banking: A Complete Guide

Conversational AI for insurance

Customer challenges for insurance companies include customers dropping off mid-application or renewing expiring contracts. Companies can use a conversational AI platform to tackle these hurdles. Conversational chatbot’s media-friendly interface allows customers to submit ID proof, health declaration forms, income statements, and signed proposal forms. 

Suggested Reading: Conversational AI in the Insurance Industry

Conversational AI in logistics

Low-level queries inundate logistics companies. Questions about order statuses, refund policies, cancellations, and returns clog support channels. Instead of having service reps manning phones and email all the time, companies can move to a conversational AI platform and see drastic benefits in customer and employee experience. 

Suggested Reading: How To Use Conversational AI in Logistics Management

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Conversational AI in edtech

Conversational AI for education can solve many support-related issues and make the student, parent and teacher/admin experience better. Potential use cases of AI in education include automating the application process, registering and enrolling students in programs and clubs, sharing timetables and class/club/school updates, sharing learning materials and updating the community on exam schedules. 

Suggested Reading: WhatsApp Chatbot for Edtech: A Complete Guide

Conversational AI in telecom

AI in telecom can help companies reduce call volumes. It automates FAQs and streamlines processes to respond to customers quickly and decreases the load on agents. With instant messaging and voice solutions, CAI encourages self-service to resolve queries, find relevant information and book meetings with technicians. 

Conversational AI for healthcare

Medical chatbots are revolutionising the healthcare industry. A report suggests that the healthcare chatbots market will be worth $703.2 million by 2025.  Clearly, medical chatbots are serving this industry well. What can it do? Find healthcare services near customers, schedule doctor’s appointments to avoid long queues, raise awareness about healthcare issues and reduce misinformation, share medication and routine check-up reminders with patients, simplify the billing and registration process at hospitals and clinics, collect feedback and ask survey questions in an easy to answer format and much more. 

Suggested Reading: Happy Health Is Just A Text Away on WhatsApp

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Benefits of using Conversational AI

Conversational AI has many benefits — both for customers and companies. It enables brands to have more meaningful one-on-one conversations with their customers, leading to more insights into customers and hence more sales. 

Even though different industries use it for different purposes, the major benefits are the same across all. We can broadly categorise them under benefits for customers and benefits for companies. Let’s look at them in detail below. 

Conversational AI benefits for customers

benefits of using conversational AI for customers

1. Quick, accurate response

One of the biggest benefits of using conversational AI is the quick and accurate responses users get. As soon as customers input their queries, they get a response from the chatbot or voicebot. A well-trained AI replies with accurate information, allowing the customer to resolve their questions with self-service. 

2. Personalised interactions

Customers get personalised responses while interacting with conversational AI. By integrating with CRMs, it creates a customer profile with all the relevant information on the customer. This is then used to personalise interactions and add context to the conversation. This increases the customer satisfaction score. 

3. Reduce wait/hold time

Customers are most frustrated when they are kept on hold by the call centres. Conversational AI reduces the hold and waits time when a customer starts a conversation. It replies instantly and reduces the average resolution time. And if the conversation is handed over to an agent, the CAI instantly connects to an online agent in the right department. 

4. Convenience 

Conversational AI takes customer preferences into account while interacting with them. Customers can interact with brands on their preferred channels such as websites, mobile apps, Facebook, Instagram, WhatsApp, Viber, etc., converse in a language they are familiar with through multilingual bots and resolve queries round the clock. 

5. Contactless customer experience

With the onset of the 2020 pandemic, customers do not want to step out of their homes and interact with humans in person. Conversational AI enables them to resolve their queries and complete tasks from the comfort of their homes. Be it finding information on a product/service, shopping, seeking support, or sharing documents for KYC, they can do this without compromising on personalisation. 

Conversational AI benefits for companies

benefits of using conversational AI for companies

1. Scale

A growing business or an enterprise company sees thousands of queries every day. This can increase the burden on agents who then cannot respond to customers on a timely basis. Conversational AI can help these companies scale their support function by responding to all customers and resolving up to 80% of queries. It also helps a company reach a wider audience by being available 24×7 and on multiple channels. 

2. Upsell opportunities

AI takes into account customer preferences. Based on their behaviour it can offer the best upsell at the right time. It can also reduce cart abandonment by answering customer queries instantly and encouraging them to complete their purchases. It also ensures a smooth form-filling process which in turn makes it easier for the sales team to act on the leads faster. 

3. Reduced costs

Customer support division can be expensive, particularly if you respond to customer queries 24×7 and in multiple languages. Conversational AI can help companies save on operational costs by automating repetitive and mundane tasks that don’t require human involvement. With CAI, companies do not have to add extra agents to handle scale, it reduces human errors and is available 24×7 at no extra cost. 

4. Customer insights

A good CAI platform captures customer details and uses them to get insights into customer behaviour. With this data, businesses can understand their customers better and take relevant actions to improve the customer experience. This in turn leads to happier customers which leads to return customers and increased loyalty and sales. 

5. Agent efficiency

Conversational AI not only reduces the load of repetitive tasks on agents but also helps them become more efficient and productive. It provides them with tools to respond to customers quickly and personalise each interaction. This lets them handle more queries in less time. Agents can then take up challenging work that increases a company’s revenue. 

Suggested Reading: 5 Ways Conversational AI Makes Your Agent’s Life Easier

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What is the key differentiator of conversational AI?

The key differentiator of conversational AI is the NLU and NLP model you use and how well the AI is trained to understand the intent and utterances for different use cases. 

To become “conversational”, a platform needs to be trained on huge AI datasets which have a variety of intents and utterances. To add to this, the platform should be compatible with other tools and tech stacks for smooth integrations and sharing of data. And when it comes to customer data, it should be able to secure the data and prevent threats. 

challenges in implementation of conversational AI

A good conversational AI platform overcomes many challenges to become the key differentiator in customer experience. 

1. Training data  

It’s not easy for companies to build a conversational AI platform in-house if they do not have enough data to cover variations of different use cases. Once a business gets data, it would need a dedicated team of Data Scientists to work on building the ML frameworks, train the AI and then retrain it regularly.

2. Conversations in the user’s language 

Conversational AI platforms are usually trained in the English language but only 20% of the world population speaks it. Training the AI in multiple languages is not common. Many companies converse in multiple languages, but they work as rule-based chatbots because their AI is not trained in those languages. 

3. Data security 

User data security and privacy are a big concern when implementing conversational AI platforms. The conversational AI platform should comply with the region’s data regulation guidelines and be secure enough to overcome any attacks from hackers. 

Suggested Reading: Conversational AI Chatbot Security – What You Need To Know

4. Ease of use

A conversational AI platform should be designed such that it’s easy to use by the agents. If the user experience is not good, the agents will not use the platform. This includes creating conversational flows, responding to end-users, analysing data, changing settings, etc. 

5. Self-learning capabilities

The AI should be able to learn from the conversations it has with users. If it doesn’t have the reinforcement learning capabilities, it becomes obsolete in a few years. Then, the companies will not see a return on investment after it is implemented. 

6. Costs

When implementing conversational AI for the first time, businesses find the costs expensive. However, in the long run, it shows a quick ROI. The challenge lies in trusting the AI and making the first investment. If the AI is not good, companies will not benefit from it. 

7. Discovery and adoption

Soon after implementation, businesses using CAI suffer from a lack of customers using chatbots to interact with them. Companies need to put in some effort to inform their users about the different channels of communication now available to them and the benefits they can see from them. 

8. Scale

Like any other technology, the conversational AI platform should be able to handle multiple conversations simultaneously. Enterprises see 500K plus chats on average. The AI architecture should be strong to handle the traffic load it sees on the chatbot with crashing or delay in response. 

9. Integrations  

A conversational AI platform can personalise customer conversations if it integrates with other tools and the tech stack of a company. During the implementation stage, this becomes one of the biggest challenges – the platform is not compatible with other software. Integrations are important for seamless syncing and personalising the customer experience. 

10. Connecting to agents

Customers want human interactions. And when it comes to complex queries, the conversational AI platform needs to hand over the chat to a human agent. While implementing the platform, adding agents/departments to the platform and ensuring the handover is smooth and to the right person can be a challenge for some. 

However, once you overcome these challenges, there are many benefits to gain from this technology. 

Suggested Reading: 12 Things to Consider Before Selecting a Conversational AI

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What is the future of Conversational AI?

The conversational AI market is growing at a rapid pace. The global market size of conversational AI in 2021 was USD 6.8 billion and is expected to grow to USD 18.4 billion by 2026.  

This growth is in part due to the digitisation of customer interactions, innovation in technology and the changing customer demands. 

More and more companies are adopting AI-powered customer service solutions to meet customer needs and reduce operational costs. Of these AI-powered solutions, chatbots and intelligent virtual assistants top the list and their adoption is expected to double in the next 2-5 years. 

Some future predictions for conversational AI are:

  • Gartner predicts that by 2022, 70% of white-collar workers will interact with conversational platforms on a daily basis. 
  • By 2030, chatbots and conversational agents will raise and resolve a billion service tickets. This chat-first strategy will increase self-service and deliver fast ROI according to Gartner
  • IDC predicts that AI will define 50% of customer interactions. 
  • By 2025 nearly 95% of customer interaction (both online and telephone) will be taken over by AI according to a conversational AI report. 
  • Companies that use AI to automate their customer engagement will see a 25% increase in their operational efficiency. 

These numbers clearly show an accelerated adoption of the technology. 

Suggested Reading: 100 Best Chatbot Statistics for 2021


Frequently asked questions

1. What is the difference between conversational AI vs chatbot? 

Chatbots can be of two types: rule-based chatbots and NLP-powered chatbots. Rule-based chatbots are simple chatbots that follow a flow chart system to respond to customer queries. It’s not smart to answer any deviation from the set rules. On the other hand, a conversational AI chatbot is an NLP-powered chatbot that understands customers’ intent and sentiment and responds accordingly.

Read more about the difference between chatbot vs conversational AI here

2. What is the difference between Conversational AI vs NLP

Conversational AI uses multiple technologies to converse with customers in natural, human-like language. One of the technologies it uses is NLP. Natural language processing (NLP) is an AI technology that breaks down human language such that the machine can understand and take the next steps. 

3. What is the difference between Conversational AI vs NLU

Similar to NLP, NLU is a subset technology of AI that is used to understand text/voice inputs

4. What is conversational AI voice?

Conversational AI voice, or voice AI, is a solution that uses voice commands to receive and interpret directives. With this technology, devices can interact and respond to human questions in natural language. 

Know more here: Voice AI: What is it and How Does it Work?

See conversational AI in action

The future of conversational is bright. More people are ready to use a conversational AI solution and hence more companies are adopting it to interact with their customers. 

According to a Statista report, 

  • 78% of service organisations use CAI for simple self-service tasks. 
  • 77% of companies leverage conversational chatbots to assess the type and difficulty of a question and accordingly hand it over to an agent. 
  • 70% of companies use a conversational solution to assist agents in retrieving information, canned responses etc to resolve queries faster. 

Despite these numbers, implementing a CAI solution can be tricky and time-consuming. At Verloop.io we have helped businesses like Nykaa, ADIB, AbhiBus, Kanmo Group, BLF Group, TravelStart, GlobeMed, and Watania get started with their conversational AI journey and delight their customers with seamless support experiences. 

If you’re curious if conversational AI is right for you and what use cases you can use in your business, sign up here for a demo. We’ll take you through the product, and different use cases customised for your business and answer any questions you may have.

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