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What is an AI Chatbot: How it Works, Use Cases, and Industrial Application

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What is an AI Chatbot: How it Works, Use Cases, and Industrial Application

Where haven’t we come across a chatbot? 

The Siri on your phone, the bot on your favourite eCommerce app, or the one sending you updates on WhatsApp – AI chatbots are integral to our digital identities today. We converse with them daily; they know the weather outside, and the fastest route to our destinations. In fact, every 4 in 10 global internet users actually prefer to talk to a chatbot than an agent to get help for anything.

As with any evolving technology, chatbots are becoming better at serving their purpose every day.

Chatbots are working wonders in their industrial application too. But before we dive into how to, let’s get the basics out of the way.

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  1. What is an AI chatbot?
  2. How does an AI chatbot work?
  3. Who is using AI chatbots? Industries and use cases
  4. How are businesses using AI chatbots in customer service?
  5. What is the difference between conversational AI and chatbot?
  6. Which is better – Rule-based vs machine learning?
  7. Which is the best AI chatbot?
  8. Frequently asked questions

What is an AI chatbot?

In the simplest of terms, an AI chatbot is a software program built and deployed with the help of conversational AI to assist users and make bot-human interactions, natural, engaging, and free-flowing.  

For businesses, a highly trained AI chatbot is of great help in customer communication. It can effectively assess and answer customer queries while personalising customer support to deliver happy user experiences every time.

So, how does an AI chatbot work on a fine day?

AI chatbots largely work on a 4-stage principle. We will break them down below.

  1. Input generation
  2. Input analysis
  3. Output generation
  4. Reinforcement learning

1. Input generation

The AI chatbot starts working the moment a user types in input, a query, or a question that they want answers to. It can collect such textual input when it’s deployed on chat applications like WhatsApp, Instagram, and on Facebook, on mobile app, or on website.

2. Input analysis

Once the AI chatbot receives the input, the process goes into motion. Now, natural language understanding (NLU), a subset of NLP, is applied to disintegrate text into fractions to pull out the meaning of the input by detecting the intent, sentiment and tone of the natural utterance. The machine learning engine then matches this intent with the database to fetch relevant information. 

3. Output generation

Now when the AI chatbot knows what the user means, it works on presenting them with the best possible, relevant answer that matches the query. With the help of natural language generation (NLG), the chatbot produces a written response, which is then communicated to the user.

4. Reinforcement learning

Over time and after several hundreds of thousands of interactions, your conversational AI chatbot would have collected a mountain of structured data. Every chatbot interaction is either a success or failure with the user. Based on this user experience, the AI chatbot relearns and refines its responses the next time. This information helps the chatbot stock up on multiple intents and utterances to serve as the basis for its perpetual learning. It helps you train your chatbot to render better, more satisfactory user experiences.

Who is using AI chatbots?

The application of AI chatbots is extensive. From helping businesses generate leads to supporting users during rough terrain, AI chatbots are paving smoother ways for all. Let’s look at how different industries are using AI chatbots to automate their use cases.

  1. Banking and financial services
  2. Insurance
  3. eCommerce
  4. Real estate
  5. Healthcare
  6. Ed-tech

1. Banking and financial services

If there is any industry that has most benefitted from AI chatbots, it’s banking and financial services. In fact, since the pandemic hit in 2020, the number of chatbots deployed in the banking sector has tripled since then! 

Today, BFSI is using chatbots to automate banking use cases like loan applications, service reminders, payment acceptance, policy renewals, and credit card applications. Moreover, chatbots work as an intelligent and personalised advisory for users, who can also seek everyday information on their bank accounts when they need it. Read more: How Chatbots in Banking Are Improving Customer Experience

2. Insurance

Insurance applications, claims and verifications are tricky. Most of the time, insurance companies need to tread safely while working their way around insurance claims. It is often a lengthy process, and disbursals take a long time between several authorisations. Trained AI chatbots prove to be a safer and faster alternative for insurers. Users prefer it too – 7 out of 10 users in this survey want insurance companies to replace in-office claim processes with online chat or video alternatives! Read more: 10 Use Cases A Chatbot Can Solve For Your Insurance Company.

3. eCommerce

We’re sure that as a 2022 consumer, you think about buying online before stepping into a physical store – just like all of your users! Online commerce has skyrocketed in the past few years, which has led eCommerce businesses to switch to automation. AI chatbots are 24×7 available to help users with customised product suggestions, refunds, exchanges and payments. Read more: eCommerce Chatbot: 7 Ways To Boost Engagement, Sales, Customer Support

4. Real estate

An AI chatbot equips real estate buyers with any information and help they require during the purchasing process. It helps narrow down the home search based on preferences, manage legal documentation assist in property valuation and smoothen transactions – all while speaking the user’s language. Read more: How To Build Meaningful Customer Relationships With A Real Estate AI?

5. Healthcare

Patients need real-time on-demand support in times of need. Chatbots are available to help them when healthcare providers can’t be online. A trained conversational AI chatbot can help users seek relevant medical information, book appointments, get lab reports and process medical insurance. Read more: How to use a medical AI chatbot to stremaline patient care? 

6. Ed-tech

Ed-tech automation is redefining the way students today receive and consume educational content. AI chatbot automation is also helping teachers, schools and learning centres handle and manage administrative tasks with ease. It helps providers proactively communicate information to parents and students, automate registration and enrolment, and avail learning resources to students. Read more: 6 Ways Automation is Changing the Edtech Industry

Want to see how AI chatbots are transforming the industries of today? Here are 11 industries using AI automation to grow their businesses

How are businesses using AI chatbots in customer service?

While AI has utility in every department of a business, customer support seems to benefit the most. As a client-facing function that dictates how users think about a business, quick and swift chatbots can fix customer service loopholes and help deliver beautiful support experiences. Here are the top customer support use cases AI chatbots can automate:

1. Resolve common queries

Conventionally, ALL your incoming queries are handled by human agents, who need to be available at all times to tend to users. It’s a slow, broken, and cost-ineffective process. A conversational AI chatbot trained to answer FAQs can resolve over 80% of all support tickets! Impact? Users get instant answers, agents can focus on more complex queries, and your business can reduce costs. Check out: How To Use An FAQ Chatbot To Answer Your Recurring L1 Queries.

2. Help users to self-serve

HBR says that over 8 in 10 users always want to resolve a query themselves before reaching out to an agent. This statistic is industry-agnostic. So, it’s evident that users want to choose the quickest route to any solution without involving too many people. Carefully designed conversation flows in a chatbot help them navigate to answers themselves. Result: users can interact with your business at their convenience, improving their experience with the brand. 

For example, if a user wants to apply for a credit card, a banking chatbot would help them with the step-by-step process until their application is successfully submitted – all without human intervention. Read more: Customer self-service: Helping users help themselves

3. Ensure smart agent routing

Your users are always low on time. And 40% of them don’t care if a chatbot or an agent answers their question – as long as they get it. While chatbots are doing exceptionally well at resolving FAQs, more complex cases need human agents to be involved. Based on the agent’s expertise and skills, the chatbot narrows down the best-qualified person to handle a case. This way, a user is not transferred from one agent to another and it most definitely avoids the annoying situation of the user repeating their query with every transfer. Read more: Agent Skills for Smart Routing and Quick Support

4. Deliver personalised recommendations

While great at answering routine questions, an AI chatbot also works as your user’s personal advisor. Based on the user’s needs and requirements, the chatbot can suggest the most relevant product and service suggestions. For example, to suggest the best home loan plan, the chatbot would ask probing questions like the user’s budgetary limits, ideal tenure, preferred interest rates, and EMI limits to suggest a plan that falls within the constraints. With this level of personalisation, a user doesn’t feel like just a number to your brand but more of a valued customer. Improves your retention rates and customer loyalty. Read more: 11 ways to automate customer support with personalisatio

banking chatbot

5. Collect user data and generate quality leads

When it comes to user information, AI is highly precise. Conversational AI chatbots are omnichannel. This lets your users relay key information at any point in time to a chatbot deployed at their choice of channel. A robust chatbot solution can collect and store information from multiple channels to a single accessible source, like a dashboard. So, whenever an agent needs any context during future interactions, they can simply pull user data accumulated over time by the AI in a single one-point space. This helps the support team offer highly relevant and specific suggestions that convert casual visitors into quality leads.

6. Authenticate and verify user identities

While consumer data is prone to cyber-attacks, an AI chatbot solution in compliance with enterprise-grade security standards like GDPR and PII helps you mitigate security risks with ease. A chatbot can help you verify identities before performing sensitive actions. For example, if a user needs to block his debit card, the chatbot can ask varying security questions and confirm asset possession through one-time-passwords (OTPs) sent on the user’s mobile number and email ID. Conversational AI chatbots can also authenticate users with real-time facial biometrics. Check out: Conversational Chatbot Security: Threats, Measures, Best Practices   

how does verification chatbot work

7. Send reminders and notifications

Businesses lose a lot due to customers missing meetings or not showing up for consultations. Chatbots are punctual without missing a beat. You can use your trained AI chatbot to send crucial reminders to users to ensure timely action. For example, you can set up your chatbot to remind users to maintain adequate balance when the EMI collection date approaches. Other use cases that you can send reminders for – are cart drop-off, discounts, upcoming festival sales, consultations and appointments, etc. Check out: Using Outreach to Proactively Communicating with Your Users

8. Map your user behaviour 

There lies the hidden value in the vault of information fed to your chatbot over multiple conversations. This very chunk of data can reveal a lot more about your customers than you did before. It can help you identify your users’ purchasing patterns, their likes and dislikes, features and functionalities that aren’t received well, and the overall demand curve of your target audience. For example, you find out the one problem your users most struggle with, based on the most common utterances grouped on similarity. 

What is the difference between conversational AI and chatbot?

Conversational AI is a blend of several singular but co-dependent technologies that help a chatbot mimic natural, human-like conversations with users. Whereas, a chatbot is a software program that may or may not use conversational AI to interact with users.

To better understand the difference, let’s take a look at the types of chatbots. 

Chatbots are generally clubbed into 3 types:

  1. Script-based or rule-based
  2. AI-powered
  3. Hybrid
Rule-based-NLP-Hybrid-chatbots-venn

1. Script-based/rule-based chatbots

These chatbots follow a pre-defined rule, which presents the user with specific answers to questions taken into account during the training process. If the user tries to ask anything outside the set of pre-designed questions, it renders the chatbot moot. 

2. Conversational AI-powered chatbots

Conversational AI-powered chatbots give users more room for open-ended conversations. These chatbots work based on NLP and machine learning algorithms which help them to understand, assess, and respond in natural language. These chatbots work on a specific AI model and a training data set. The algorithm then embeds the logic within the dataset by analysing it, which helps it to develop and learn from it. This creates a coherent relationship between future data reasoning and consequent outputs. 

Simply put, AI chatbots are self-learning. This helps them refine their responses after each interaction based on the user experiences they deliver.

3. Hybrid chatbots

Hybrid chatbots are trained to operate within certain fixed parameters like rule-based/script-based bots, but also employ AI when needed. They are capable of pulling out the intent from natural textual input to respond with relevant answers while also helping users navigate through certain use-cases pre-defined conversational flows. As and when the need arises, these bots are also capable of routing chats to available agents.

FactorsRule-based/script-basedAIHybrid
FunctionalityTransactionalConversationalTransactional + Conversational
FlowMenu-driven, one-wayBidirectionalBidirectional
ApproachEncoded rules, if-then-else statementsNLP/NLU, ML, NLGEncoded rules, NLP/NLU, ML, NLG, smart agent routing
PurposeProcess navigationOut-of-the-box assistance in natural languageDefined use-case navigation, capable of assisting based on the type of query

Which is better – Rule-based vs machine learning?

Both of these chatbots are designed to help with specific needs. Since their purpose of existence is different, the answer to “which is better” is subjective. 

Rule-based or script-based chatbots are easy to implement. They take lesser training, are cheaper, with shorter deployment times. They work best for businesses that want to automate specific processes in their business—for example, booking a doctor’s appointment, applying for a banking service, and registering for school admissions. Rule-based chatbots work best for companies that don’t want to shell out too much capital while automating individual functions.

AI chatbots that employ machine learning, on the other hand, help businesses diversify their digital journey. They help brands establish stronger customer communication channels, simply because they are trained to speak with customers naturally. They are built with users at the forefront, to help them with solutions specific to THEIR problems. Unlike rule-based bots, an ML-driven AI chatbot almost functions as a personal concierge that works to chart out solutions that solve the problem at hand. 

The quality of AI-led interactions is high, which means you need to equally put in the investment to build, train, and maintain an AI chatbot. While building them from the ground up is an economically hefty task, many robust chatbot solutions are making it easier for businesses to use one.

Which is the best AI chatbot?

You probably have already come across chatbots that make you wonder “God, these dumb bots are never helpful!”. Well, that’s because they are simply not designed with rigour and are not ergonomic, to say the least. So, if you are planning to deploy one for your business, you want to best be assured that your customers never have to feel that way. 

I feel you, reader. Realising a sharp AI chatbot that works great To help you narrow down your research, we worked on the top factors you need to consider to find the best AI chatbot for your business.

1. It should serve all channels your customers love visiting

As a digital business, you might find your customers using Instagram, but also WhatsApp, and at other times, Facebook. The question that crops up here is if your chatbot is omnichannel. Does it offer consistently seamless support across all your digital channels of presence? An omnichannel chatbot solution will help you merge your user journeys from multiple channels to a single management source.

2. It should integrate seamlessly with all your tools

To send emails to users, you might already be using an email automation software. Or you could be using a payment gateway to collect payments from your customers. You might also be using a marketing automation tool to send text messages to users. Your chatbot should offer effortless third-party integrations with all of your such existing tools.

3. It should pick up on user intent, tone, emotion, and urgency 

While AI is not sentient, your chatbot should know to read between the lines. Most of the prevalent chatbots are sadly not trained to, which often results in broken interactions. A great chatbot can identify varying user intents and the arbitrary, human aspects of the input. 

4. It should be well-versed in solving the user pain points 

Good training is a non-negotiable to building a good AI algorithm that works for your business problems. Your chatbot must be trained thoroughly to resolve use cases pertaining to your industry. And do so autonomously – at least 80% of the time. If you are an eCommerce brand, your chatbot must not lag when users request a “refund” or a “status update on their exchange”.

While this is a rough idea of what makes an AI chatbot the best, check out our blog which lists down specific features you can’t ignore while building yours: 7 Features That Make A Good Website Chatbot Great

FAQs

1. Are AI chatbots effective?

AI chatbots are highly effective in deflecting up to 80% of all user queries. Since most of these queries are repetitive, AI chatbots can be trained to resolve most of them autonomously. They are effective in reducing the work overload on human agents, and lowering costs, while digitally transforming a business with smart automation. The effectiveness (accuracy) depends on how well the AI is trained and the ML models used. 

2. What AI techniques are used in chatbots?

AI chatbots are equipped with natural language processing (NLP), machine learning, and cognitive computing are some of the top techniques used to make an AI chatbot smart, conversational, interactive, and accurate with its responses. 

3. What are some examples of chatbots?

We have worked on a list of top chatbots that deliver exceptional user experiences, check it out here: 10 Best Chatbot Examples For Better Customer Service

3. What are some disadvantages of chatbots?

While chatbots are developed to augment digital interactions, there can be a few downsides to a poorly built chatbot. Some of them include inaccurate input analysis that leads to irrelevant outputs, the ability to respond with only limited options, poor security infrastructure, robotic speech, inability to understand human emotion and tones. However, training your chatbot well can avoid most of these issues. Read more: 5 Easy Ways to Train a Chatbot

4. Why are chatbots the future?

Almost every business has gone digital today. The shift has been largely because users want to connect conveniently, interact, buy, and seek support on digital channels. Attending users at such a scale (and doing so manually) would inadvertently dampen the speed and efficiency of end-to-end conversations. Chatbots are making this a lot simpler for brands and their users. AI chatbots are trained to talk to customers and perform specific use cases for the business around the clock in real time. They drastically reduce delays in customer communication and streamline interactions as businesses scale up. Here’s how you can future-proof your business with AI.

Conclusion

AI chatbots have truly flipped communication for companies that want to build for the future. Digital consumerism will only grow in the coming years, which means businesses need to be in tandem with their users to thrive. Manning an exclusively human team to deliver diligent, on-time, and individual attention to users is not wise gameplay. This is exactly where an AI chatbot can help your business build solid customer relationships that stem from unwavering customer support.

Verloop.io is on a mission to make this a reality for businesses worldwide! Our conversational AI serves as complete customer support operating system built for teams that believe the road to success is through happy customers. Schedule a demo with one of our conversational experts today to get started.  

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