PREPARED BY

SONIA MAHINDRU

ASSISTANT PROFESSOR IN COMPUTER SCIENCE

HANSRAJ MAHILA MAHA VIDYALAYA JALANDHAR

CHATBOT

CHATBOT

Chatbot can be defined as AI based computer program that simulates human conversations. They are also known as digital assistants that understand human capabilities. Bots interpret and process the user requests and give prompt relevant answers. 

Bots can through voice as well as text and can be deployed across websites, applications and messaging channels such as Facebook Messenger, Twitter or Whatsapp. 

A chatbot -- sometimes referred to as a chatterbot -- is programming that simulates the conversation or "chatter" of a human being through text or voice interactions. Chatbot virtual assistants are increasingly being used to handle simple, look-up tasks in both business-to-consumer (B2C) and business-to-business (B2B) environments. The addition of chatbot assistants not only reduces overhead costs by making better use of support staff time, it also allows companies to provide a level of customer service during hours when live agents aren't available.

How chatbots work? 

Chatbots work by analyzing and identifying the intent of the user’s request to extract relevant entities, which is the most important task of a chatbot. Once the analysis is done appropriate response is delivered to the user. The chatbots work by adopting three classification methods. 

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Pattern matching

Bots utilize pattern matches to group the text and it produces an appropriate response from the clients. Artificial Intelligence Markup Language (AIML) is a standard structured model of these patterns. A bot is able to get the right answer in the related pattern. The bots react to anything relating it to the correlate patterns. 

Natural language understanding (NLU)

NLU is the ability of the chatbot to understand a human. It is the process of converting text into structured data for a machine to understand. NLU follows three specific concepts. They are: entities, context, and expectations.

Entities – It represents an idea to your chatbot. For example, it may be a refund system in your ecommerce chatbot.

Context – When a natural language understanding algorithm identifies the request and it has no historical backdrop of conversation, it will not be able to recall the request to give the response.

Expectations –  Bot must be able to fulfill the customer expectations when they make a request or ask a query customer says sends an inquiry. 

Natural language processing (NLP)

(NLP) Natural Language Processing bots are designed to convert the text or speech inputs of the user into structured data. The data is further used to choose a relevant answer. 

Natural Language Processing (NLP) comprises of the below steps:

Tokenization – The NLP filters set of words in the form of tokens.

Sentiment Analysis – The bot interprets the user responses to align with their emotions. 

Normalization – It checks the typo errors that can alter the meaning of the user query.

Entity Recognition – The bot looks for different categories of information required.

Dependency Parsing – The chatbot searches for common phrases that what users want to convey.

Types of chatbots 

Chatbots process data to deliver quick responses to all kinds of users’ requests with pre-defined rules and AI based chatbots. There are two types of chatbots. 

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Rule based chatbots

Rule based chatbots follow the predefined paths during conversations. At each step during the conversation, the user will need to pick from explicit options that determine the next step in the conversation. 

These bots follow predetermined rules. So it becomes easy to use the bot for the simpler scenarios.

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Interactions with rule based chatbots are highly structured and are most applicable to customer support functions.

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Rule based bots are ideally suitable for answering common queries suck as an inquiry about business hours, delivery status or tracking details.

Conversational chatbots

Conversational chatbots are also referred to as virtual assistants or digital assistants. They are much more interactive and personalized than rule based chatbots. The conversational chatbots converse with the users as in a way humans converse and communicate in real-life situations. 

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Conversational communication skills of the chatbot technology empower them to deliver what customers are looking for.

Conversational bots can understand the context and intent of complex conversations and try to provide more relevant answers.

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AI bots apply predictive intelligence and sentiment analysis to understand customer emotions closely.

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Machine learning bots learn from user behavior and provide more personalized conversations.

Why chatbots are important for your business? 

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Chatbots boost operational efficiencyand bring cost savings to businesses while offering convenience and added services for customers. They allow companies to easily resolve many types of customer queries and issues while reducing the need for human interaction. 

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Reduce customer waiting time – According to Chatbot Report, 21% of consumers see chatbots as the easiest way to contact a business. Chatbots are a smarter way to ensure that customers receive the immediate response that they are looking for without making them wait in a queue. 

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24×7 availability –  68% of customers switch to a competitor if they don’t think you care about them. Bots are always available to engage customers with immediate answers to the common questions asked by them. The top potential benefit of using chatbots is 24-hour customer service.

Better customer engagement – Conversational bots can engage customers round the clock by starting proactive conservation and offering personalized recommendations that boost customer experience.

Easy scalability with bots – Bots can be easily scalable during the peak business hours or and manage ‘n’ number of customer conversations without additional customer service costs. 

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Save customer service costs –  Juniper Research estimates that chatbots will help businesses save more than $8 billion per year by 2022. Chatbots help businesses to save customer service costs of hiring more support agents that require additional costs such as salary, training and infrastructure costs.

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Reduce customer churn rate– Engaging your visitors is arguably the single most sure-fire way of reducing bounce rates and subsequently increasing conversions. With chatbots, you can boost your engagement strategy even further and actually keep visitors hooked. 

Challenges include:

Varieties in the way people type their message can make it hard to understand their intention. Chatbots must be able to deal with both long and short sentences, as well as chat bubbles with lengthy content versus multiple very short submissions.

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The different ways in which humans talk can also be difficult for a chatbot to understand. For example, the user may use slang, misspell words and short form or acronyms. Unfortunately, NLP is limited and cannot fully resolve this challenge.

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Human beings are random and user behavior is controlled by emotions and moods; users may quickly change their minds. After initially asking for a suggestion, they might switch to wanting to give a command instead. Chatbot technology must be able to adapt to and understand this element of randomness and spontaneity.

Examples:

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Hipmunk is a platform that allows people to search for travel deals and many of its users turn to it to book flights, hotels, rental cars, or packages.

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Swelly is probably the most famous Facebook Messenger chatbot. It allows users to pick and choose one of two options, and vote together with the general public.

Thank You